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AI-900 Microsoft Azure AI Fundamentals

EXAM NUMBER : AI-900 EXAM NAME : Microsoft Azure AI Fundamentals Prove that you can describe the following: AI workloads and considerations; fundamental principles of machine learning on Azure; features of computer vision workloads on Azure; features of Natural Language Processing (NLP) workloads on Azure; and features of conversational AI workloads on Azure.

Candidates for the Azure AI Fundamentals certification should have foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services.

This certification is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure.
This certification is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are not required; however, some general programming knowledge or experience would be beneficial.

Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it’s not a prerequisite for any of them.

Describe AI workloads and considerations
Describe fundamental principles of machine learning on Azure
Describe features of computer vision workloads on Azure
Describe features of Natural Language Processing (NLP) workloads on Azure
Describe features of conversational AI workloads on Azure

Module 1: Introduction to AI
In this module, you'll learn about common uses of artificial intelligence (AI), and the different types of workload associated with AI. You'll then explore considerations and principles for responsible AI development.

Artificial Intelligence in Azure
Responsible AI
After completing this module you will be able to:

Describe Artificial Intelligence workloads and considerations
Module 2: Machine Learning
Machine learning is the foundation for modern AI solutions. In this module, you'll learn about some fundamental machine learning concepts, and how to use the Azure Machine Learning service to create and publish machine learning models.

Introduction to Machine Learning
Azure Machine Learning
After completing this module you will be able to:

Describe fundamental principles of machine learning on Azure
Module 3: Computer Vision
Computer vision is a the area of AI that deals with understanding the world visually, through images, video files, and cameras. In this module you'll explore multiple computer vision techniques and services.

Computer Vision Concepts
Computer Vision in Azure
After completing this module you will be able to:

Describe features of computer vision workloads on Azure
Module 4: Natural Language Processing
This module describes scenarios for AI solutions that can process written and spoken language. You'll learn about Azure services that can be used to build solutions that analyze text, recognize and synthesize speech, translate between languages, and interpret commands.

After completing this module you will be able to:

Describe features of Natural Language Processing (NLP) workloads on Azure
Module 5: Conversational AI
Conversational AI enables users to engage in a dialog with an AI agent, or bot, through communication channels such as email, webchat interfaces, social media, and others. This module describes some basic principles for working with bots and gives you an opportunity to create a bot that can respond intelligently to user questions.

Conversational AI Concepts
Conversational AI in Azure
After completing this module you will be able to:

Describe features of conversational AI workloads on Azure

Microsoft Azure AI Fundamentals
Microsoft Fundamentals test Questions
Killexams : Microsoft Fundamentals test Questions - BingNews Search results Killexams : Microsoft Fundamentals test Questions - BingNews Killexams : What is Fundamental Analysis?

Analysis helps you decide whether a stock is a good investment or something you should avoid. You can use 2 types of analysis known as technical analysis and fundamental analysis when reviewing a stock. 

This article will provide you expert guidance on how to conduct your own fundamental analysis. It will also provide an analysis of a major tech company as a concrete example.

Main Takeaways: Using Fundamental Analysis

  • Technical analysis and fundamental analysis are the 2 main types of analysis used by stock traders. Fundamental analysis evaluates security to create a forecast for its future price.
  • Fundamental analysis uses specific statistics for stocks. These include earnings per share (EPS), the price-to-earnings (P/E) ratio, beta and more. We explore what each of these is and the role they play below.
  • Amazon is a great company to use fundamental analysis for. We provide an example below for this tech company. 

What is Fundamental Analysis?

Fundamental analysis is the process of evaluating a security to make forecasts about its future price. For a stock, fundamental analysis typically includes reviewing many elements related to stock prices, including:

  • Performance of the overall industry the company participates in
  • Domestic political conditions
  • Relevant trade agreements and external politics
  • The company’s financial statements
  • The company’s press releases
  • News releases related to the company and its business
  • Competitor analysis

If some fundamental indicators of a company imply a negative impact, this is likely to eventually be reflected in its share price. On the other hand, if positive data is released, like a favorable earnings report, this can boost the stock price of the respective company.

Here are some examples of key company performance statistics that are commonly used to perform fundamental analysis on stocks:

  • Earnings per share (EPS)
  • Price-to-earnings (P/E) ratio
  • Price-to-book (P/B) ratio
  • Return on equity (ROE)
  • Beta

Each of these key performance statistics gives information that is helpful to conduct a fundamental stock price analysis. You can then buy the stock on the assumption that the price will increase if your analysis suggests the price of the stock should rise from its present level. 

Example of Fundamental Analysis

Although there’s no standard way to do fundamental analysis, since stock trading is not as accurate as a math problem, you can follow certain guidelines. Also, the same information in one industry and stock may not mean the exact thing in another. A few of the most important statistics used in fundamental stock analysis are described in greater detail below.

EPS and Diluted EPS

Earnings per share relate to the portion of a company’s profit allocated to each of the company’s shares. The EPS is an indication of the firm’s profitability. The higher the earnings per share, the healthier the company, so the better the stock should perform for an investor. 

At the same time, if a stock’s earnings per share are unusually high for its company’s industry, this could mean one or more of the following things:

  • Earnings are expected to decrease back to more normal levels.
  • The price of the stock could increase to normalize the stock’s EPS.
  • An adverse condition exists for the company that has depressed its share price. 

While EPS just takes into account the number of common shares issued by a company, many fundamental analysts prefer to look at diluted EPS that also includes convertible securities.

Price-to-Earnings Ratio

The price-to-earnings (P/E) ratio is used to evaluate companies and determine if they are under or overvalued. The ratio is computed by taking the share price of the company and dividing that by its earnings per share. The P/E ratio shows whether a share of stock pays well compared to its price. 

For example, imagine that the price per share is $30 and the stock pays $2 earnings per share. The P/E ratio of the stock is computed as follows:

30 / 2 = 15

The lower the P/E ratio, the higher the earnings compared to the stock price, and the more attractive the stock. Furthermore, an unusually low P/E ratio could show extra potential for a future price rise. 

If the P/E ratio is too low, below 10 for example, this means that the price per share seems low compared to the company’s earnings especially if competing firms typically have higher P/E ratios. This might mean that the stock could be undervalued, so its price can increase significantly. The opposite holds true for high P/E ratios.

Price-to-Book Ratio

The price-to-book (P/B) ratio is a financial ratio that shows how much the stock is worth compared to the book value of the company. It is computed by taking the price per share and dividing that by the book value per share. 

For example, if a company worth $10 million has 500,000 shares outstanding, it will have a book value per share of:

$10,000,000 / 500,000 shares = $20 book value per share

If its stock trades at $80 per share, then the P/B ratio is:

$80 / $20 = 4 P/B ratio

If the P/B ratio is more than 1, this means the market’s consensus is that the stock will grow at a faster pace than its book value suggests, which is the reason why its price is higher than its book value. In some cases, you can see very high P/B ratios of 100 or more. High P/B ratios are often seen in high growth stocks.

Return on Equity (ROE)

The return on equity is a measurement that determines how efficient a company is when using its shareholders’ equity. You calculate the ROE by dividing the shareholders’ equity by the company’s net income. If a company has generated $5 million this year and its shareholder’s equity is $50 million, this means the ROE is:

$5,000,000 / $50,000,000 = 0.1 or 10%

Note that analysts typically display the ROE result as a percentage. The higher the ROE, the more efficient the company is. If a company generates a less than $5 million income this year (say $2 million) with the same shareholders’ equity, this means it is less efficient:

$2,000,000 / $50,000,000 = 0.04 or 4%

Here, the company has a lower ROE given the same shareholder’s equity, so it is less efficient in using its shareholders’ equity to generate income.

Beta (β)

The beta coefficient or market beta provides information about how the stock’s price correlates on average to the entire market. This can be computed by comparing the stock to a benchmark index like the S&P 500 or the NASDAQ indices. The beta usually varies between -1 and 1. Sometimes values can go much lower than -1 or much higher than 1.

Values above 0 mean that the stock correlates positively compared to the benchmark index. The higher the beta, the higher the correlation. A higher beta also usually means that the volatility of that stock is higher as well, so the risk of holding it can be greater.

Values below 0 mean that the stock is inversely correlated to the benchmark index. The lower the beta goes below 0, the higher the inverse correlation between the stock and the market index.

Fundamental Analysis of Amazon Inc. (NASDAQ: AMZN) is a well-known stock within the tech sector. The company has shown considerable revenue growth since 2004 as the chart below shows.

Amazon’s net income or earnings only began to rise significantly since 2015, however, as the chart below illustrates. 

Another important fundamental factor is that Amazon has beaten the consensus EPS estimates for 2 of the past 4 quarters.

Such earnings data shows that Amazon is healthy and aims for growth that beats expectations every year. The best news is that they are succeeding, for now at least.

Diluted earnings per share: $2.07

Note that Amazon’s diluted EPS seems low compared to its current stock price of $120.73. This results in an extremely high P/E ratio for Amazon of:

P/E ratio: 59.05

This tells us that only a very small part of Amazon’s earnings actually goes to its shareholders. This is totally normal for an IT company of Amazon’s rank. They constantly expand and reinvest funds in research and development. 

Some of the fields Amazon is currently working actively in include robotization and artificial intelligence. Since the company spends a lot on R&D there, there’s not much left for the shareholders.

This offers up further information, however. Since Amazon invests a lot and aims to expand more, we can expect the company to grow even further. This strategy will eventually push its stock price up. After all, during the past 4 years, Amazon’s stock price has increased by more than 40% on average.

P/B ratio: 9.30

That number is the ratio of Amazon’s stock price of $120.09 divided by its book value per share of $13.18 for the quarter ending 2022. A P/B ratio of nearly 10 is very high. This means investors are currently paying nearly 10 times more for an Amazon share than the book value of the company.

ROE: 28.8% as of December 2021’s net Quarterly income in Q1 of 2022 attributable to common stockholders was $14.3 billion. The company’s average total stockholders equity (TSE) in that same quarter was $138,245 B. The ratio yields’s annualized ROE percentage for the September quarter shown above.

Amazon’s ROE % remains high for its sector, although Apple Inc.’s is higher still. Here’s a general comparison with other top tech companies from the S&P 500:

  • Apple’s (NASDAQ: AAPL) ROE: 48.7%
  • Facebook’s (NASDAQ: FB) ROE: 25%
  • Microsoft’s (NASDAQ: MSFT) ROE: 23.1%
  • Alphabet’s (NASDAQ: GOOG) ROE: 11.6%
  • PayPal’s (NASDAQ: PYPL) ROE: 13.8%

Amazon’s high ROE % suggests the firm is more efficient when it comes to generating returns on the investment it obtained from shareholders than 4 of the 5 companies listed above. Note that this doesn’t necessarily mean that Facebook, Microsoft, Alphabet (Google’s owner) and PayPal are not efficient in that regard. It just means they are less efficient than Amazon and Apple. 

Beta: 1.29

Beta shows the sensitivity of a stock’s expected excess asset returns to expected excess market returns, and Amazon has a relatively high beta. This means its stock is well-correlated to the S&P 500 benchmark index, and it is also one of the top 10 stocks of the S&P 500 in terms of its market capitalization.

Furthermore, a beta of 1.29 means that Amazon’s stock price is currently more volatile than the overall market, which isn’t unusual for a huge multinational corporation that dynamically reinvests its funds in research and development.

Fundamental Analysis vs. Technical Analysis: What’s the Difference?

Fundamental analysis for stocks relies largely on computing and reviewing a company’s various financial parameters as you saw in our analysis above. Technical analysis, on the other hand, only takes into consideration past price action and other market observables like volume and open interest to forecast future price behavior.

Investors typically perform technical analysis on a chart. The goal of their analysis is to make forecasts based on past stock price performance. Pure technical analysts don’t rely on company data and fundamentals. They instead compute technical indicators and analyze charts for patterns they recognize as having a strong potential to predict future price behavior.

At first take, the idea that technical analysis might be able to predict future price behavior could seem surprising. However, there’s a strong relationship between price action and the psychology of market participants. 

Furthermore, since the price of a stock is based on supply (selling interest) and demand (buying interest), important psychological levels in the price of a stock could have an impact on the attitude of the market participants when those levels are attained. 

An example of this is a round number level on a chart like $100 that is likely to have a psychological impact on market participants. If a stock approaches from below $100 per share, this $100 psychological level could attract selling interest since many investors might think that the company has no capacity to expand above $100 per share. They therefore decide to sell their assets at or below that level, thereby creating supply and providing resistance to the price of the stock trading above $100.

Technical analysts use various tools to analyze the price action of stocks. Some of these are especially useful chart types like candlestick and point and figure charts. Others are patterns like triangles, trend lines and channels, while computed indicators might include historical volatility and momentum oscillators. Volume numbers are also often used by technical analysts to confirm chart pattern breakouts. 

Each of these tools helps a technical analyst predict market behavior in different ways. When the signals obtained from them align with each other, a technical analyst can use them to trade with greater certainty. 

In most cases, your broker will supply you with most of the important data you’ll need to conduct a fundamental analysis of a stock. Some other excellent sources for information and research tools for fundamental analysis include the following. 


Finviz is a very adequate solution you can use to screen fundamental stock data, and it even has a helpful free version.

This is what you will see when you search for a stock. The data includes the most important fundamental parameters of a stock you can use in your analysis.

Best For

All Trading Levels

Benzinga Pro

Benzinga Pro gives you everything you need for market news and research in an easy-to-use platform. Powered by the Benzinga Pro Newsfeed, you’ll get lightning-fast market news and data to inform your trading decisions. Benzinga Pro offers more:

  • Audio alerts to key headlines and breaking news with Audio Squawk.
  • The Benzinga Pro team monitors the news you need to know from 6 a.m. to 6 p.m. EST.
  • Use Movers and Screener to scan for stocks tailored to your trading strategy.
  • Access to Signals notifies you about events such as block trades, price spikes, opening gaps and more.
  • Chat community lets you ask questions, get trading ideas and more with traders of all experience levels. 

Get access to all of this and more when you start a free 14-day trial of Benzinga Pro.

Stock Rover

get started securely through Stock Rover’s website
Best For

Professional Investors


1 Minute Review

Stock Rover is a comprehensive stock analysis and screening tool that gives investors access to high-quality research tools, educational content, expert analysis and more. Stock Rover goes well beyond standard analysis tools, offering account holders 1 of the most comprehensive sets of screening criteria and research qualifications that we’ve seen. For example, users can filter investment opportunities using over 650 metrics with Premium Plus access.

Stock Rover offers 4 different plan tiers, which can become confusing for new investors who aren’t exactly sure which tools they’ll use. However, free accounts offer a wide range of functionality that gives users a better feel for Stock Rover’s setup, along with a free trial of Premium Plus functionality. Getting started with a Stock Rover account is also exceptionally easy — we were able to open our free account in under 60 seconds.  

Best For
  • Professional investors who need high-quality analysis and research tools
  • Visual learners who benefit from seeing data laid out using charts and graphs
  • Anyone searching for the widest array of screening metrics
  • Incredibly comprehensive range of investment analysis tools
  • Over 650 screening criteria included on Premium Plus plans
  • Simple account opening and brokerage account linking
  • Free accounts available
  • Wide variety of plan options that can be more confusing for new investors

Stock Rover is a comprehensive stock analysis and screening tool that gives investors access to high-quality research tools, educational content, expert analysis and more.

It goes well beyond standard analysis tools, offering account holders 1 of the most comprehensive sets of screening criteria and research qualifications that we’ve seen. For example, you can filter investment opportunities using over 650 metrics with Premium Plus access.

Stock Rover offers 4 different plan tiers, and free accounts can provide you a better feel for Stock Rover’s setup. You can also sign up for a free trial of its Premium Plus.

Getting started with a Stock Rover account is also exceptionally easy — we were able to open our free account in under 60 seconds.  

Get Familiar with Fundamental Analysis

Many people consider fundamental analysis an essential part of stock trading and investing. You should become familiar with the basic fundamental indicators if you want to start investing in stocks so you can build a better picture of the financial condition of a company and determine if it’s financially efficient, sustainable and profitable. 

Although fundamental stock analysis is important, it often makes sense to pair it with technical analysis. Keep in mind that important psychological levels on the chart might be a turning point and watch for classic chart patterns. Try to find situations where the technicals agree with the fundamentals for better opportunities. If you manage to master both of these techniques, your analysis can reach a new level of market forecasting accuracy.

Looking to Excellerate your stock trading strategy? Check out our guide on how to create an investment strategy, or the best online stock brokers for beginners if you’re just starting to build your portfolio.

Frequently Asked Questions


What is the benefit of using fundamental analysis?


What is the benefit of using fundamental analysis?


Luke Jacobi



Fundamental analysis looks at the company’s financials and industry to determine if it is a good long-term investment.




What factors influence fundamental analysis?


What factors influence fundamental analysis?


Luke Jacobi



Factors that influence fundamental analysis include the economy, industry, management and the firm’s financial condition.



Related content: TrendSpider Review

Mon, 25 Jul 2022 12:00:00 -0500 en-US text/html
Killexams : Fundamentals of Creating Online Documents

The ability to write and present online material is not just an asset in the current job market—it's a requirement. This course will introduce you to the principles and processes of designing effective online documentation.

We'll begin with an overview of online documentation today, and explore how online documentation is changing the role of the technical communicator.

We will also examine how users interact with online documents and what techniques you can incorporate into your documentation design to facilitate these interactions. You will get hands-on practice with several current industry tools to create some basic online documents.

Wed, 16 Mar 2022 13:32:00 -0500 en text/html
Killexams : Why Amit Sharma created DoWhy

“Data tells stories. My research aims to tell the causal story,” proclaims Amit Sharma, a researcher at Microsoft and the developer of software library DoWhy (2018). This year, this library was in the news when Microsoft moved DoWhy to an independent open source governance model in a new PyWhy GitHub organisation.

Analytics India Magazine caught up with Sharma for a quick chat about PyWhy, Causal Inference, and more.


AIM: Let’s begin by getting a peek into your early years and professional journey. 

Amit Sharma: I did my graduation in engineering from IIT Kharagpur. While studying there, I got a chance to intern at industry and university labs that exposed me to the process of research. I was acquainted with a few PhD students and liked how they were all trying to solve tough problems with many unknowns. And the best part was they were being paid to study and research – the idea fascinated me. So, I applied for PhD programs and got through the Computer Science department at Cornell University. 

My advisor Dan Cosley was in the Information Science department. Hence, I had the advantage of taking courses and interacting with students from both departments. While Computer Science focuses on the design of technology systems, Information Science studies how these systems interact with society. This dual experience changed my outlook. Initially, I wanted to build systems that would help people, but I gradually learnt that it is equally important to reflect on whether I really know what will help people and what are ways to confirm my hypothesis. In other words, the ability to ask the right question is often as important as coming up with the best answer to a question.

I am now working as a principal researcher at Microsoft Research in India. I try to merge the ways of thinking in what I do – causal inference and technology for mental health.

AIM: To whom (or what) do you credit your interest in Causal Inference?

Amit Sharma: My first encounter with Causal Inference was during an internship at LinkedIn, where I was working to Excellerate LinkedIn’s recommendation algorithm. I was struck by my team’s dependence on conducting a randomised A/B experiment to test a new algorithm, even though there were many established accuracy metrics that could be computed from log data. That felt wasteful, so I went up to my manager and asked, “why not evaluate algorithms offline using the log data? That will be so much faster”. He said, “We’ve tried that before. You’ll be lucky if offline evaluation provides the right direction of estimates, let alone an accurate answer.” I was intrigued. Something felt wrong here, but I didn’t know how to express it and moved on.

A few years later, during an internship at Microsoft Research, my collaborators helped me find the answer: Causal Inference. Turns out that causality has been an important course in

statistics, economics, and the biomedical sciences, but it found very little attention in Computer Science at the time. And it could perfectly explain the A/B test riddle. The issue was that we wanted to find the causal effect of any new recommendation algorithm, but the offline accuracy metrics were not correctly set up to measure that (and as I learnt later, it is tough to set them up to measure the causal effect). But the really interesting part was that it was not just A/B testing versus offline evaluation or about finding the impact of a recommender system, the principles of causal inference apply to all decision problems from healthcare to economics. I started studying more about causality, especially from Judea Pearl’s writings and got hooked.

AIM: You built the DoWhy library. Tell us about the whole process of development.

Amit Sharma: Well, even though I was excited about the topic, it was very difficult to learn about Causal Inference. Back in 2015, the best resources were statistics textbooks or the Causality book from Judea Pearl. None were accessible to me. I spent a good part of 2015 trying to understand what these books were saying. 

At the outset, it looked like there was no agreement on the best way to do a causal analysis. But the more I read and worked with data, I realised that all causal analysis problems boiled down to four steps. They were the same steps repeated in each project, but these details were often skipped when presenting the analysis. The accepted best practices for formulating and validating assumptions that we’d hear from experts were not written down anywhere. So, my collaborator Emre Kiciman and I thought: Why not create a library for Causal Inference that enabled these best practices for everyone? 

The four steps are: model the world knowledge, identify whether a causal quantity is estimable given the knowledge, estimate the quantity if so, and finally refute or validate the obtained estimate. These four steps form the core API verbs of the DoWhy library we built. Before DoWhy, most software for causality focused only on the estimation step. But the other steps are equally important. As we designed the DoWhy library, we wanted to convey that Causal Inference is not like predictive machine learning, where you start with data. Here, you need to start with assumptions that you are willing to take on the data-generating process. If you make the wrong assumptions, no amount of data modelling will save you. Therefore, DoWhy’s focus is on setting up and validating assumptions of a causal analysis.

AIM: How do you see DoWhy evolve? 

Amit Sharma: The response has been heartening. In a short time, DoWhy has been installed over 1.3 million times. It is being used as a teaching tool in universities, in research papers, and in answering various business questions in the industry. Going forward, we will continue to add better ways to validate the Causal Analysis. DoWhy is also moving towards other causal tasks: in addition to effect estimation, we are extending it to attribution, prediction and counterfactual estimation using the same four-step API.

We’ve just recently moved DoWhy to an independent Github organisation, py-why, so DoWhy is now an open-source, community-led project. If you are interested, feel free to join the community on Discord. We welcome your contributions! 

AIM: Your work revolves around using modern algorithms as interventions. Please elaborate.

Amit Sharma: Around the time that I started working on causality in online systems, there was a growing concern about algorithmic decision-making systems in critical domains such as finance, education and governance. I realised that we are quickly moving away from a world where technology helped people do a task to where systems are making important decisions that can affect people’s lives. This is a paradigm shift. Consider an algorithm used by a bank to decide on loan applications or a governmental algorithm to distribute aid. Such algorithms are not just passive prediction algorithms, they are coming in and making decisions with real consequences for the people involved. And their effects can be massive.

So, two questions come to mind: 

1) How do we measure the impact of these systems? 

2) How can we design such systems for better impact? 

These questions are not too different from the evaluation of medical treatment or economic policy (that’s why the term algorithmic interventions) where causal inference has historically been applied. How do we do the same in computing systems?

AIM: Right, so what are you working on now?

Amit Sharma: I am working on developing better ways to validate causal models from data. That remains an open question, so advances here can accelerate the progress in building causal models, like what cross-validation did for machine learning. The other direction that I’m interested in is how causality can help machine learning systems become more robust and trustworthy. I am developing techniques that use interventional input examples to interpret the patterns learnt by a predictive ML model (see the DiCE project), evaluate its fairness with respect to people’s expectations and Excellerate the ML model. I am also fortunate to be associated with the Center for Societal Impact through Cloud and AI (SCAI) at Microsoft Research, where I’m working to see how such models can be responsibly deployed in sensitive contexts. I’m also working with clinical psychologists from NIMHANS on a mental health app, MindNotes, that aims to reduce stigma around mental health and encourage more people to seek help.

AIM: What resources would you suggest to those interested in knowing more about Causal Inference?

Amit Sharma: Compared to predictive machine learning, causal inference requires a different thought process and can have a steep learning curve. So I would like to suggest a few learning resources:

1) It’s best to start with the Book of Why to understand the basic concepts.

2) If you like to go deeper into the context of computing systems, you can check out the draft book, Causal Reasoning: Fundamentals and machine learning applications, that I’m co-writing. The first chapter describes how causality, out-of-distribution predictive generalisation, and reinforcement learning are all connected.

3) If you prefer videos, you may check out this webinar on causal machine learning that includes a sample analysis with the DoWhy library.

Wed, 27 Jul 2022 21:04:00 -0500 en-US text/html
Killexams : Forex Analysis

What are the types of Forex Analysis?

There are innumerable ways to analyze the Forex market, but its goal is the same: trying to predict where the price is headed next. The most popular types of forex analysis are:

  • Technical analysis
  • Fundamental analysis
  • Sentiment analysis

What indicator is best for Forex?

There are several indicators key to trade the forex market, and all of them are a great tool for the trader to forecast where the price can go next.

There are indicators of sentiment, trend, volume, etc. Their value will depend on the strategy the trader is trying to follow and its risk management rules.

How do you analyze trends in Forex?

Detecting directional movements is critical. At the end, an FX trader needs to determine and anticipate such a move, or trend, to make profits.

There are multiple tools that can be used to achieve the goal, such as trend lines, supports, resistances, technical indicators, and even pure observation of the price behaviour on the forex chart.

It’s important to take into account that fundamental analysis is as important as the technical one. Using both the trader could better forecast the trend to maximize the benefits and limit the losses trading Forex.

How do I create a Forex Strategy?

The best strategy for one person could be the worst one for others. There are several questions that need to be answered ahead of defining it. How many hours can I dedicate to FX? What is my risk tolerance? These among the most relevant questions.

A forex strategy is a combination of tools that should result in a positive balance in a certain period of time. During the creation of a strategy, flexibility and imagination are very important values. Once the strategy is created, discipline is one of the greatest virtues.

How can beginners start trading forex?

Every learning process is evolutionary and requires the right steps to be followed in order to obtain knowledge and develop skills.

For new traders, it is advisable to work with simple but very powerful tools, such as trend lines, support and resistance or Japanese candlesticks.

It is advisable to start working on simple patterns, such as triangular figures, rectangles or reversal patterns. These types of layouts are easier to manage and the novice trader will learn to manage positions without large ranges that increase risks.

Educate yourself. Knowing the tools, and learning to use it is critical. As in any other professional career, learning before practicing is the key to success. Recognizing strengths and weaknesses is also a critical part of this process.

Thu, 28 Jul 2022 22:54:00 -0500 en text/html
Killexams : The Uber whistleblower: I’m exposing a system that sold people a lie

Mark MacGann, a career lobbyist who led Uber’s efforts to win over governments across Europe, the Middle East and Africa, has come forward to identify himself as the source who leaked more than 124,000 company files to the Guardian.

MacGann decided to speak out, he says, because he believes Uber knowingly flouted laws in dozens of countries and misled people about the benefits to drivers of the company’s gig-economy model.

The 52-year-old acknowledges he was part of Uber’s top team at the time – and is not without blame for the conduct he describes. In an exclusive interview with the Guardian, he said he was partly motivated by remorse.

“I am partly responsible,” he said. “I was the one talking to governments, I was the one pushing this with the media, I was the one telling people that they should change the rules because drivers were going to benefit and people were going to get so much economic opportunity.


What are the Uber files?


The Uber files is a global investigation based on a trove of 124,000 documents that were leaked to the Guardian by Mark MacGann, Uber's former chief lobbyist in Europe, the Middle East and Africa. The data consist of emails, iMessages and WhatsApp exchanges between the Silicon Valley giant's most senior executives, as well as memos, presentations, notebooks, briefing papers and invoices.

The leaked records cover 40 countries and span 2013 to 2017, the period in which Uber was aggressively expanding across the world. They reveal how the company broke the law, duped police and regulators, exploited violence against drivers and secretly lobbied governments across the world.

To facilitate a global investigation in the public interest, the Guardian shared the data with 180 journalists in 29 countries via the International Consortium of Investigative Journalists (ICIJ). The investigation was managed and led by the Guardian with the ICIJ.

In a statement, Uber said: "We have not and will not make excuses for past behaviour that is clearly not in line with our present values. Instead, we ask the public to judge us by what we’ve done over the last five years and what we will do in the years to come."

Thank you for your feedback.

“When that turned out not to be the case – we had actually sold people a lie – how can you have a clear conscience if you don’t stand up and own your contribution to how people are being treated today?”

The senior role MacGann held at Uber between 2014 and 2016 put him at the heart of decisions taken at the highest levels of the company during the period in which it was forcing its way into markets in violation of taxi-licensing laws. He oversaw Uber’s attempts to persuade governments to change taxi regulations and create a more favourable business environment in more than 40 countries.

He said the ease with which Uber penetrated the highest echelons of power in countries such as the UK, France and Russia was “intoxicating” but also “deeply unfair” and “anti-democratic”.

Uber files whistleblower comes forward: 'We sold people a lie' – video

In his wide-ranging interview, MacGann detailed the personal journey that led him to leak the data years after leaving Uber.

“I regret being part of a group of people which massaged the facts to earn the trust of drivers, of consumers and of political elites,” he said. “I should have shown more common sense and pushed harder to stop the craziness. It is my duty to [now] speak up and help governments and parliamentarians right some fundamental wrongs. Morally, I had no choice in the matter.”

The Guardian led a global investigation into the leaked Uber files, sharing the data with media organisations around the world via the International Consortium of Investigative Journalists (ICIJ).

After MacGann identified himself as the whistleblower, Uber said: “We understand that Mark has personal regrets about his years of steadfast loyalty to our previous leadership, but he is in no position to speak credibly about Uber today.”

Responding to the wider investigation, Uber acknowledged past failings but insisted the company had transformed since 2017 under the leadership of its new chief executive, Dara Khosrowshahi. “We have not and will not make excuses for past behaviour that is clearly not in line with our present values,” a spokesperson said.

The Uber files consists of confidential company data that MacGann had access to at Uber. It includes company presentations, briefing notes, security reports and tens of thousands of emails and WhatsApp, iMessage and chat exchanges between the company’s most senior staff at the time.

They include Travis Kalanick, Uber’s combative co-founder and then chief executive, David Plouffe, a former Barack Obama campaign aide who became a senior vice-president at Uber, and Rachel Whetstone, a British PR executive who has also held senior roles at Google, Facebook and now Netflix.

When MacGann departed Uber in 2016, Whetstone described him as “a wonderful leader”. Plouffe called him a “talented public policy professional” and “terrific advocate for Uber”.

The one-time cheerleader-in-chief for Uber in Europe, MacGann now looks set to become one of its sharpest critics.

His profile as a senior executive and political insider make him an unusual whistleblower. So, too, does the fact he actively participated in some of the wrongdoing he is seeking to expose – and the fact it took him more than five years after leaving the company to speak out.

The process through which he came to re-evaluate what he witnessed at Uber was a gradual one, he says. “When I decided I had an obligation to speak up, I then went about finding the most effective, impactful way in which to do that. Doing what I am doing isn’t easy, and I hesitated. That said, there’s no statute of limitations on doing the right thing.”

MacGann is understood to have recently reached an out-of-court settlement with Uber after a legal dispute relating to his remuneration. He said he was prohibited from discussing his legal dispute but acknowledged he had had personal grievances with the company, which he alleges undervalued his role as an interlocutor with government and failed in its duty of care to him.

MacGann (right) and Travis Kalanick

He accuses Uber under Kalanick’s leadership of adopting a confrontational strategy with opponents in taxi industries, that left him personally exposed. As a public face of Uber in Europe, MacGann bore the brunt of what became a fierce backlash against the company in countries including France, Belgium, Italy and Spain.

Amid threats to his life, he was given bodyguard protection. His experience of working at Uber, he says, took a mental toll and contributed to a subsequent diagnosis of post-traumatic stress disorder (PTSD).

Brazenly breaking the law

A Brussels insider, MacGann was an obvious pick to lead Uber’s government relations in the Europe, Middle East and Africa (EMEA) region in 2014. Born in Ireland, he speaks several languages and possessed an impressive contacts book built up over two decades in lobbying and public affairs.

MacGann had worked at established public policy firms such as Weber Shandwick and Brunswick, and had run DigitalEurope, a trade association that advocated for companies such as Apple, Microsoft and Sony. His most latest job had been as senior vice-president at the New York Stock Exchange on a salary of $750,000 a year.

MacGann took a significant salary cut to work at Uber for €160,000. But like all senior executives joining the company back then, the financial reward was in the promise of stock options that could be worth millions if Uber realised its global ambitions.

Uber and its investors were eyeing vast returns if the tech company succeeded in its mission to deregulate markets, monopolise cities, transform transit systems and one day even replace drivers with autonomous vehicles. The plan, MacGann acknowledges, required Uber to flout the law in cities in which regulated taxi markets required hard-to-get licences to drive a cab.

“The company approach in these places was essentially to break the law, show how amazing Uber’s service was, and then change the law. My job was to go above the heads of city officials, build relations with the top level of government, and negotiate. It was also to deal with the fallout.”

MacGann started work for Uber around the summer of 2014, when he worked on contract for a European lobbying consultancy that Uber had hired to oversee government relations outside the US. In October 2014, Uber brought him in-house and put him in charge of public policy for the EMEA region.

On his first day on staff, MacGann was in an Uber from London City airport when he got his first taste of the startup’s laissez-faire approach to privacy. After emailing a senior executive to tell them he was in traffic, MacGann received the reply: “I’m watching you on Heaven – already saw the ETA!”

“Heaven”, otherwise known as “God View”, was the codeword Uber employees used at the time for a tool that allowed staff to surreptitiously use the app’s backend technology to surveil the real-time movements of any user in the world.

“It felt like children playing around with powerful surveillance technology,” said MacGann. “Even back then it was dawning on me this was a rogue company.”

In its statement, Uber said tools such as God View, which it stopped using in 2017, “should never have been used”. A spokesperson for Kalanick said it would be false to suggest he ever “directed illegal or improper conduct”.

Travis Kalanick in 2018. Photograph: Elijah Nouvelage/Getty Images

The Uber files contain some instances in which MacGann pushes back at the company’s operations and decisions. But, for the most part, the documents show him expressing little dissent over the company’s hardball tactics, and on some occasions he appears directly involved in wrongdoing.

He describes himself as having been “drunk on the Kool-Aid” at Uber, a company he alleges did not encourage dissent or criticism. But he does not dispute he was at the heart of many of the controversies that have been revealed by his data leak.

“I believed in the dream we were pushing, and I overdosed on the enthusiasm,” he said. “I was working 20 hours a day, seven days a week, constantly on planes, in meetings, on video conference calls. I didn’t stop to take a step back.”

His whirlwind stint at the company involved meetings with prime ministers, presidents, transport and economy ministers, EU commissioners, mayors and city regulators.

MacGann said most senior politicians were instinctively supportive of Uber, viewing the tech company as offering an innovative new platform that could allow for flexible working and help reboot economies after the financial crisis.

Striking French taxi drivers try to march down a major bypass during a day of protests in January 2016. Photograph: Olivier Coret/Rex/Shutterstock

However, it was a more mixed story in France, where Uber’s unlicensed service prompted taxi driver riots and divided the cabinet of the then president, François Hollande.

On one side was Bernard Cazeneuve, the minister of the interior, who according to MacGann once summoned him to his office and threatened him with jail, saying: “I will hold you personally and criminally responsible if you do not shut it down by the end of the week.”

On the opposing side of the debate was Emmanuel Macron, the pro-tech, pro-business economy minister who, the leak reveals, became something of a secret weapon for Uber.

The data includes text message exchanges between MacGann and Macron, who was working behind the scenes to assist the US tech company. In one exchange, MacGann asks for Macron’s help in the midst of a raid on the company’s offices. In another he complains about an apparent ban on its services in Marseille.

Macron told MacGann he would “personally” look into the matter. “At this point, let’s stay calm,” the minister said.

MacGann with Emmanuel Macron

MacGann recalls Macron as being “the only person who gave us the time of day … So he was a massive breath of fresh air.”

Macron did not respond to detailed questions about his relationship with Uber. A spokesperson said his ministerial duties at the time “naturally led him to meet and interact with many companies” engaged in the service sector.

After leaving Uber, MacGann maintained relations with Macron and helped raise funds for his La République En Marche party in 2016. He says his political support for the French president was a personal decision and had “absolutely nothing to do with Uber”. They continued to exchange text messages with one another up to as recently as April this year.

MacGann (centre) with Peter Mandelson (left) and the former UK prime minister Tony Blair

‘Speed dating for elites’

The French president is not the only political figure who knows MacGann. He is on first-name terms with two former EU commissioners, Neelie Kroes and Peter Mandelson. After leaving Uber, MacGann maintained a business relationship Lord Mandelson, a former Labour cabinet minister.

MacGann is also a familiar face among VIPs who attend the World Economic Forum in Davos, which he describes as “speed dating for elites”. He recalls persuading an initially reluctant Kalanick to attend the gathering in the Swiss Alps in 2016.

“For a lobbyist, Davos is a wonderful competitive advantage that only money can buy,” he said. “Politicians don’t have a retinue of advisers and civil servants hanging around taking notes.”

Mark MacGann at the World Economic Forum with Kristalina Georgieva, the former EU commissioner who is now managing director of the IMF

Uber’s executives met with the Israeli prime minister, Benjamin Netanyahu, the Irish taoiseach, Enda Kenny, and the UK chancellor, George Osborne. Securing those meetings, MacGann said, was “a piece of cake”. “Uber was considered hot property.” So much so that when Kalanick met Joe Biden at the Swiss resort it was at the US vice-president’s request.

The Uber files reveal that Kalanick fumed when he was kept waiting by Biden, texting other Uber executives: “I’ve had my people let him know that every minute late he is, is one less minute he will have with me.”

However, it was another Kalanick text in the leak – in which the former CEO appears to advocate sending Uber drivers to a protest in France, despite the risk of violence – that has sparked headlines across the world.

Warned by MacGann and Whetstone that encouraging Uber drivers to protest amid violent taxi strikes in Paris risked putting them at risk, Kalanick replied: “I think it’s worth it. Violence guarantee[s] success.”

MacGann called Kalanick’s instruction to stage an act of civil disobedience with French Uber drivers, despite the risks, as a “dangerous” and “selfish” tactic. “He was not the guy on the street who was being threatened, who was being attacked, who was being beaten up.”

Kalanick’s spokesperson said he “never suggested that Uber should take advantage of violence at the expense of driver safety” and any suggestion he was involved in such activity would be completely false. Uber acknowledged past mistakes, but said no one at the company, including Kalanick, wanted violence against Uber drivers.

MacGann insists that Uber drivers were seen by some at the company as pawns who could be used to put pressure on governments. “And if that meant Uber drivers going on strike, Uber drivers doing a demo in the streets, Uber drivers blocking Barcelona, blocking Berlin, blocking Paris, then that was the way to go,” he said. “In a sense, it was considered beneficial to weaponise Uber drivers in this way.”

The files show MacGann’s fingerprints on this strategy, too. In one email, he praised staffers in Amsterdam who leaked stories to the press about attacks on drivers to “keep the violence narrative” and pressure the Dutch government.

Looking back, MacGann said: “I am disgusted and ashamed that I was a party to the trivialisation of such violence.”

Taxi drivers with wanted posters on the sides of their cars in Brussels in September 2015 during a protest against Uber. Photograph: Dursun Aydemir/Anadolu Agency/Getty Images

A parting of ways

One of the worst flashpoints in Europe was at Brussels Midi train station, where Uber drivers lingered to pick up passengers who would otherwise be queueing at a regulated taxi rank. MacGann was first recognised there on 27 April 2015.

“Got spotted by a bunch of taxi drivers at the train station arriving from London,” he emailed a colleague that day. “Seven of them followed me as I went to get my Uber, hurling insults and spitting … One of them ran after me for a while, intending to hurt my driver.”

The colleague replied: “Thank God you made it … This weekend Uber driver and taxi driver got into a fistfight. Getting intense in Brussels.”

The threats intensified over subsequent weeks. Emails show alarm at the company after a taxi driver trailed MacGann’s limousine to his apartment in Brussels and posted his home address on a “stop Uber” Facebook group in Belgium. Taxi drivers snapped surveillance-style photos of MacGann outside a hotel with friends and uploaded them to the internet.

Surveillance-style photos uploaded to the internet of MacGann outside a hotel in Brussels

In August that year, a security report commissioned by Uber mentioned rumours that MacGann and another Uber executive were going to be “taken off the streets by a core group of taxi drivers”.

Uber gave MacGann a personal team of bodyguards. An email states that between September and November 2015, the security team spent 619 hours shepherding him in Belgium alone, while Uber also beefed up security for foreign trips.

During a protest in Brussels, about 100 taxi drivers gathered outside MacGann’s office in the city and blocked the road. An Uber security report described how an initially relaxed atmosphere became “more grim”. Fireworks were let off and riot police charged protesters.

A wanted poster depicting Mark MacGann and Uber executives

Taxi drivers at the protest attached “wanted” posters on the sides of their cars. They displayed photos of MacGann and two other Uber executives. The caption read: “International criminals.”

In October 2015, MacGann emailed a colleague: “I have had bodyguards full-time now for five months and it is becoming very stressful.” A week later, he told Plouffe and Whetstone of his intention to resign. He officially departed four months later, on 12 February 2016.

It seemed an amicable split. Publicly, he expressed no regrets and used his Facebook page to lavish praise on Kalanick.

“Toughest boss I ever had and I’m a stronger leader for it,” he said, adding there was “no thing” he would change about his time at Uber. “Forget the hyperbole in the media; forget the intrigue; think about how pushing a button and getting a ride makes your life better.”

In his departure email to colleagues, MacGann described himself as “a strong believer in Uber’s mission”.

Uber publicly commended MacGann’s work and asked him to stay on as a consultant.

He was given a new job title – senior board adviser – and retained his Uber-provided emails, laptops and phones.

That role ended in August 2016, after which MacGann took on a new job at a telecoms company and started his own business venture. It was a full year after leaving Uber that, MacGann says, he experienced his most “terrifying” ordeal as a perceived representative of the cab-hailing firm.

‘MacGann, we will get you’

The incident outside Brussels Midi station was recorded in a police report, Uber emails and media reports. It took place between 11.45am and 12.15pm on 19 September 2017, shortly after MacGann arrived at the station.

As he walked towards his waiting Uber, taxi drivers approached him and ordered him not to get into the car. One grabbed him by the arms to stop him from putting his bags in. Concerned for his safety, MacGann asked the Uber driver to lock the doors when he was in the car.

Image posted on social media of car following MacGann

Several more taxi drivers joined the fray, surrounding the car. MacGann called the police. A security report commissioned by Uber questioned whether the taxi drivers had recognised him. But he recalls the drivers yelling: “MacGann, we will get you, we know where you live.”

He recalls them thumping on the windows and rocking the car from side to side. Three taxi drivers were taken to the police station, but no further action was taken.

MacGann said he was left fearing for his life – and that of his Uber driver, who “was shaking and in tears, scared for his life”. “These taxi drivers had his licence number, they could come after him again. It just seemed to me that Uber viewed this guy as expendable supply – not an employee with rights.”

Shortly afterwards, MacGann received an anonymous threat on Twitter: “One day police won’t be there and you’ll be alone. And we will see if money will help you.”

MacGann held his former employer responsible. “I felt that Uber had caused this, by its ‘success at all costs approach’ that encouraged confrontations between Uber and taxi drivers … I started to feel it was indicative of Uber’s wider relationship with drivers, putting them in harm’s way for their own financial interests.”

By mid-2018, MacGann said, the death of a close friend contributed to a deterioration in his mental health. A medical report from March 2019 said a subsequent diagnosis of PTSD was “evidently linked and impacted by the professional stress he had to endure” during his time at Uber.

MacGann said that months of treatment and therapy between 2018 and 2019 – and an enforced period of personal reflection – led him to reassess his time at Uber. “I’d stepped off the corporate hamster wheel for the first time in decades. I emerged with a new sense of clarity about everything at Uber.”

Get in Touch embed

No longer living the fast-paced life of a corporate executive, MacGann had time to listen more carefully to the stories of Uber drivers who were ferrying him around. He credits those conversations with changing his understanding of what the company used to call “driver economics”.

In its statement, Uber’s spokesperson said “driver earnings globally are at or near all-time highs today” and that Uber’s interests were “aligned with drivers, ensuring they have a positive experience earning on the platform”. If drivers were dissatisfied with its platform, she added, “they can and do choose to earn somewhere else”.

In the statement released after MacGann identified himself as the whistleblower, Uber said his litigation against the company was “an attempt, among other things, to get paid a bonus he claimed to be owed for his work at Uber. That lawsuit recently ended with him being paid €550,000. It is noteworthy that Mark felt compelled to ‘blow the whistle’ only after his cheque cleared.”

MacGann first contacted the Guardian five months before his legal dispute with Uber was settled and placed no restriction on when journalists could use the leaked data. He disputes Uber’s claim that he has been paid €550,000, and said he was still awaiting his full payout from the settlement. His lawyer said: “While Uber has paid most of the settlement amount, a sizeable portion remains outstanding while issues relating to tax are resolved.”

A phone displaying the Uber app in front of a taxi rank at Waterloo station in London. Photograph: Chris J Ratcliffe/Getty Images

Sharing secrets

In February 2020, MacGann, increasingly angered by what he viewed as the mistreatment of drivers, tried to take action. Uber was appealing against a decision by Transport for London (TfL) to refuse the company a licence in the capital, on the grounds it failed to meet the “fit and proper” test.

Emailing the mayor’s office, MacGann explained he was a former Uber executive with information to share in a “private and non-sensationalist manner, given my intimate knowledge of the company”. MacGann said he felt “frustrated” when his attempt to formally raise concerns about Uber did not receive a reply.

In February 2021, MacGann went a step further. After studying about a French lawyer who was bringing a class action lawsuit against Uber on behalf of drivers, MacGann got in touch and offered to provide information to help their case. The lawyer visited him at his home and MacGann allowed him to take photographs of a small sample of Uber documents he had stored on his old computer.

His relationship with the French lawyer turned out to be short-lived. But the dam had been broken. MacGann realised quite how many of Uber’s secrets he was sitting on.

In January 2022, Uber’s former top lobbyist travelled to Geneva and met with reporters from the Guardian.

He opened two suitcases and pulled out laptops, hard drives, iPhones and bundles of paper. He warned it would take a few days, at best, to explain everything he knew. “I’ve seen some really shady shit, to use one of the Silicon Valley expressions.”

Mon, 11 Jul 2022 02:55:00 -0500 en text/html
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Teradata Corporation (NYSE:TDC) Q2 2022 Earnings Conference Call August 4, 2022 5:00 PM ET

Company Participants

Christopher Lee - Senior VP and Head of IR and Corporate Development

Steve McMillan - President, CEO and Director

Claire Bramley - CFO and Principal Accounting Officer

Conference Call Participants

Chad Bennett - Craig-Hallum

Tyler Radke - Citi

Wamsi Mohan - Bank of America

Anushtha Mittal - RBC Capital Markets

Derrick Wood - Cowen


Good afternoon. My name is Lisa, and I will be your conference operator today. At this time, I would like to welcome everyone to the Teradata Second Quarter 2022 Earnings Call. All lines have been placed on mute to prevent any background noise. After the speakers, remarks there will be a question and answer session. [Operator Instructions]

I would now like to hand the conference over to your host today, Christopher Lee, Senior Vice President of Investor Relations and Corporate Development. You may begin your conference.

Christopher Lee

Good afternoon, and welcome to Teradata's 2022 second quarter earnings call. Steve McMillan, Teradata's President and Chief Executive Officer, will lead our call today followed by Claire Bramley, Teradata's Chief Financial Officer, who will discuss our financial results and our outlook.

Our discussion today includes forecasts and other information that are considered forward-looking statements. While these statements reflect our current outlook, they are subject to a number of risks and uncertainties that could cause actual results to differ materially. These risk factors are described in today's earnings release and in our SEC filings, including our most latest Form 10-K and in the Form 10-Q for the quarter ended June 30, 2022. The that is expected to be filed with the SEC within the next few days. These forward-looking statements are made as of today, and we undertake no duty or obligation to update our forward-looking statements. On today's call, we will be discussing certain non-GAAP financial measures, which exclude such items as stock-based compensation expense and other special items described in our earnings release. We will also discuss other non-GAAP items, such as free cash flow and constant currency revenue comparisons. Unless stated otherwise, all numbers and results discussed on today's call are on a non-GAAP basis. A reconciliation of non-GAAP to GAAP measures is included in our earnings release, which is accessible on the Investor Relations page of our website. At Investor A replay of this conference call will be available later today on our website.

And now I will turn the call over to Steve.

Steve McMillan

Thank you, Chris, and good afternoon everyone. Thank you for joining us today. Teradata’s momentum continued during the second quarter of 2022. Our results demonstrate our business resilience during volatile economic trends and these results are driven by our solid business model, critically important what we do for customers and our cloud momentum. Our strategy is right, and our business fundamentals are solid.

In the second quarter, we delivered public cloud ARR of $234 million, growing 75% year-over-year in constant currency. Our cloud ARR [indiscernible] growth accelerated as more customers connected to Teradata in cloud both year-over-year and sequentially, and they committed more substantially as well. Recurring revenue is now 80% of our total revenue, which not only demonstrates the mission-critical value we provide to our enterprise customer base, but also underpins the healthy generation of durable free cash flows. We also generated more than $100 million of free cash flow in the quarter, and we exceeded the high end of our quarterly outlook for non-GAAP earnings per share. On both metrics, we have passed 60% of our annual outlook.

Our ongoing execution reflects our clear focus on our strategy as a profitable multi-cloud data and analytics platform leader and gives us conviction that we are reaffirming our annual outlook, including an expanded outlook range for total ARR. Claire will share more in her remarks. We are intent on keeping our momentum doing what we said we would do, even in the current challenging macro environment. Our strategic customer focus is in leading global enterprises diversified across many industries our customers are strong, stable businesses that require the best data and analytics to succeed. And when they place their trust in Teradata, they make multiyear commitments. As a result, our business model is resilient with predictable revenue. We all know that every day, the amount of data grows and right along with it grows the need to capitalize on that data extract the greatest value from it and make the best business decisions faster.

Our platform is designed to deliver a unique advantage that enables cost-effective growth. With Teradata companies can access and use the data without duplication or data movement. We take the clearly an analytics engine to the data. There is no need to copy and move it to get value. As a result, the Teradata platform provides efficiencies over others, competitors costs increased disproportionately higher and faster than the growth in analytics, use it don't move it as a differentiator for us and a real benefit for our customers. Using data where it resides allows businesses to create advanced analytics with the entire universe of available data and not waste money moving and duplicating it.

West Vantage the foundation for game-changing analytics is already in place. It is a simple and very efficient effort to add data elements to bring better insights and greater value. Just one example, as a company is doing a forecast model for inventory needs during the holidays, and might need a subset of weather data to build a more accurate models and make more informed decisions. With competitors' platforms, the company would have to copy all of that data and move it into their system. Doing the series forecasting would require additional data movement, which always leaves more time and more cost to the customer. But with Teradata, all of that can be done without moving the data a much smarter and more efficient solution that only we deliver and one that drives more consumption for Teradata.

Particularly now, companies need Enphase provided by the powerful data and analytics Teradata provides. Studies are pointing out the C-suite leaders and notably CIOs are planning for technology-enabled growth and efficiency during unpredictable payments of challenge and uncertainty. Companies need data at enterprise scale to review, rethink and rapidly adjust to changing market conditions. We have seen these secular drivers play out during the uncertainties of the pandemic and the need for data and analytics and is likely to remain a C-suite imperative. High priority areas are directly in our sweet spot, including cloud, data and analytics.

Each quarter, we provide updates on our technology, demonstrating our continued growth as a cloud leader I'm very excited about our announcement that it's just around the corner as we take the next base step with our cloud data and analytics platform. Later this month, we will make our most significant cloud capabilities in entry yet, bringing our industry-leading enterprise data and analytics platform, best-in-class workload management and patented analytics capabilities into the next generation for the companies can scale smarter, innovate faster and grow stronger in the cloud.

Organizations support their largest and most complex workloads with outstanding cost efficiencies and enterprise price performance with Teradata today. and we are taking our capabilities even further. They will be unable to accelerate their highest value opportunities, unlock data through our flexible and scalable platform and activate their analytics through more data-driven decision making. Ultimately, to solve mission clinical challenges and generate returns. We will share more at the time of our launch, but rest assured, we'll do it better than anyone else.

Following our announcement, we will be taking our message on the road, coming to all of our regions around the globe with our event series entitled possible. Our marketing team is accelerating to drive greater awareness of Teradata's differentiated capabilities. Throughout this global series, we will showcase proven approaches to create value from data and analytics and accelerate results even in dynamic business environments, exactly what our customers need now. To help customers address their analytics needs, we are ensuring they know of our capabilities. We have driven ongoing expansion and our pipeline of opportunities in the cloud through the first half of 2022.

We have actively worked on expanding the pipeline with a number of drivers, including greater awareness of the differentiated capabilities of our platform and brand as the connected multi-cloud data platform for enterprise analytics, greater experience in selling cloud first and in selling with partners. Our results are clearly showing the strength of our efforts to grow through migrations to the cloud by continuing to expand hybrid environments as customers maintain their on-prem environment while adding new incremental workloads in the cloud with Teradata and winning new logos, both in the cloud and on-prem. We expect customer successes to continue as we look ahead to the second half of the year. Let's walk through a few examples.

A Fortune-500 insurance company is migrating its entire Vantage on-prem environment, development, production and disaster recovery systems to Vantage on AWS. The customer chose Teradata because of the ease of migration we offer, our proven scalability to manage increasing analytical workloads and our road map that aligns perfectly with their long-term strategy for data and analytics. PetroRio, the largest independent oil and gas company in Brazil has chosen Vantage on AWS as an enterprise data and analytics platform. As a new Teradata customer, PetroRio is relying on Teradeda to build a logical and flexible environment it can leverage for business analytics.

One of the largest boutique health care labs in the U.S. has chosen Vantage as the foundation for its data and analytics strategy. This new Teradata customer is integrating desperate data from several legacy databases and homegrown systems into a hybrid advantage environment. Workloads will primarily be deployed on-prem to start then ultimately migrate to Vantage on AWS. Best customer will leverage Vantage to Excellerate its personalized services and develop unique therapies to treat cancer. American Airlines, one of our long-standing customers migrated to the cloud with Vantage and Microsoft.

Vantage on Azure provides the flexibility, elasticity and industry-leading price performance this airline Titan needs for its business-critical operations. I encourge you to watch the videos on our website that outlines the successful migration and the results achieved from data and analytics running on Vantage. I am very proud of how our team performed and drove wins in the quarter. We did not see anything unexpected as we executed our strategy in a competitive market and challenging macro environment. There were no surprises or deal delays in the quarter. I am really enthusiastic about the strength of our pipeline as we enter the second half of the year. I mentioned wins and pipeline growth with partners. As a data platform leader, we are steadily enabling partners to extend our reach and drive adoption and consumption of Teradata. We have recently made some great partnership announcements that address important and emerging needs.

We recently announced the integration of our Vantage platform with Amazon stage major. With the enterprise scale advantage, we empower even the largest organizations to execute complex analytics on massive data sets while using their favorite data science tools and languages like SageMaker, West Vantage and the machine learning capabilities of SageMaker, AI and ML projects are able to move and to wide-scale production in weeks instead of months. So that our joint customers can rapidly accelerate their AI, ML projects and get to value faster.

Also just announced was a new collaboration between GE Digital Microsoft and Teradata. We are working together to support the imperative of addressing the devastating effects of climate change. Together, we will develop an offering designed to help provide aircraft operators castle and reduce carbon emissions and Teradata Vantage will be the underlying data and analytics platform to support this environmental sustainability initiatives. Operating sustainably is a core element of ESG and ESG as a core element of all aspects of our business. Our annual ESG report has just been released, and that outlines the depth of our increased attention and focus as well as the actions we have taken and the progress we have made as a responsible corporate sale, I invite you to read through it on our website. Another significant element of ESG is governance, and we've just added new strength to our Board of Directors. I'm incredibly pleased that Tod Matalan joined our Board in the second quarter, and I'm excited that Teradata will be able to benefit from his deep financial experience and proven track record and successful transformation to the cloud. Teradata continues to be recognized as an ESG leader and has been named in the 50-50 women on Boards Gender Diversity Directory. Diversity, equity and inclusion are cornerstones of our company, and I am proud that we are walking the walk.

As I turn the call over to Claire I remain very confident in our future. We are growing in cloud. We have powerful technology that keeps on getting better. All this increases our total addressable market, a market that is large and organically growing from the never-ending need for data and analytics. I'm enthusiastic about accelerating in the second half of the year driving profitability, generating free cash flows and returning shareholder value.

Now I'll turn the call to Claire.

Claire Bramley

Thank you, Steve, and good afternoon, everyone. In the second quarter, we reported $25 million of sequential cloud ARR growth or $30 million in constant currency. We also delivered earnings per share, $0.03 above the high end of the previously provided range and generated another quarter of very healthy free cash flow. This quarter was in line and consistent with the forecast that underpins our fiscal 2022 outlook. Our forecast is driven by our cloud first profitable growth strategy and by our solid financial fundamentals. Despite the current challenging economic environment and persistent currency headwinds, we are doing what we said we would do, and we remain on track to achieve our fiscal 2022 outlook.

Let's get into the quarterly results, starting with ARR. Total ARR decreased by approximately 3% year-over-year as reported and grew 1% year-over-year in constant currency. On a year-over-year basis, there was approximately 4% of negative impact in the reported growth rates associated with exiting Russia as shared with you last quarter. On a sequential basis, total ARR declined by approximately $37 million, with approximately $32 million related to currency headwinds.

Cloud ARR grew 68% year-over-year as reported and 75% year-over-year in constant currency. Cloud ARR grew in all three geographic regions, both year-over-year and sequentially, continuing the momentum we have seen throughout the year. Cloud ARR growth in the second quarter was driven primarily by migrations, including a number of 7-figure deals. This migration activity came from a healthy number of existing on-prem customers new to the cloud with Teradata. Total new logos for the quarter were in the low double digits. This amount grew sequentially and year-over-year, better than historical seasonality. Total new logos included both on-prem and sale customers, representing the financial services, government and transportation industries to name a few. This new logo momentum is a proof point of executing our strategy.

Regarding cloud expansions, our net expansion rate was approximately 120%. This is below our planned rate, but we are still on track to deliver our outlook of approximately 80% growth year-over-year in cloud ARR as reported and in constant currency. In the quarter, we had more customers expanding versus the same period last year. These enterprises are starting their expansion on the Vantage cloud platform with smaller test and development workloads with the potential for even greater growth ahead. In addition, we see two other trends that are not in the net expansion rate calculation that point to future growth. First, customers are expanding more than one for one at the point of migration that we originally modeled. Second, we are seeing new incremental cloud workloads from existing on-premise customers who are expanding their hybrid environment. These trends, along with our seasonally stronger second half and excellent cloud pipeline provide us confidence in our ability to achieve this year's cloud ARR growth target.

With regards to non-cloud components of total ARR, subscription ARR decreased 3% year-over-year as reported and maintenance and software upgrade rights ARR decreased 24% year-over-year as reported. Both were driven primarily by customers shifting their Teradata spend to term or cloud subscriptions and were also impacted by currency headwinds and the ceasing of Russia operations, which was in last year's numbers.

Moving to revenue. Total revenue was $430 million, a 12% decrease year-over-year as reported and an 8% decrease in constant currency. Recurring revenue was $345 million, an 8% decrease year-over-year as reported and a 5% decrease in constant currency. As a percentage of total revenue, recurring revenue was 80% in the second quarter, a new high for Teradata. There was a negative year-over-year impact in the reported growth rate of approximately 15% of total revenue and approximately 14% for recurring revenue. Both were affected by the ceasing of operations in Russia the impact of upfront recurring revenue and currency headwinds.

We have provided a slide in this quarter's earnings presentation that shows the impact by quarter to ARR and revenue. It also clearly demonstrates the underlying business is growing as expected. To add additional color on this quarter's recurring revenue, there was approximately $12 million of recurring revenue that was remained from our results due to exiting Russia. There was also less year-over-year benefit from upfront recurring revenue engagement. The impact of a negative $6 million is a small net positive amount we had expected in the quarter. For reference, this compares to a positive $22 million impact in the second quarter of 2021.

Looking ahead, we see a change in the quarterly shape and net upfront recurring revenue in the second half of 2022. We continue to expect a net negative amount in the third quarter, but now anticipate a net positive amount in the fourth quarter. As a result, we now anticipate a net $0.20 benefit to earnings per share related to upfront recurring revenue, similar to last year. Regarding perpetual and consulting revenue, we continue to execute against our strategy, moving to a higher-margin subscription revenue model and collaborating more with partners that drive higher adoption and greater consumption of Teradata.

Moving to profitability. Teradata's second quarter gross margin rate was 61.2% and gross margin dollars were $263 million. The year-over-year decline in gross margin dollars is primarily due to negative currency impacts, ceasing our business operations in Russia, and the negative impact from upfront revenue abatement in the quarter. Cloud gross margin dollars are improving as we continue to scale our cloud revenue. Operating profit margin was 12.8% in the quarter and the year-over-year decline in operating profit margin was primarily attributable to lower revenue. We continue to invest in cloud, go-to-market and R&D to drive greater adoption and consumption of our platform while also continuing solid cost discipline. Second quarter earnings per diluted share of $0.33 exceeded the high end of the previously provided outlook range of $0.30 even after accounting for a $0.02 benefit from the more favorable tax rate.

Turning to free cash flow and capital allocation. Free cash flow generated in the quarter was $102 million, driven primarily by efficient cash conversion sustaining our positive operational trends of cash collections over the last 5 quarters. In the second quarter, we repurchased approximately 2.1 million shares or $67 million in total. Of this dollar amount, $50 million related to the completion of the ASR transaction we entered into in February and $17 million related to incremental shares repurchase during the quarter as we believe our shares are undervalued. We will continue to be opportunistic during the second half of the year. For the first half of 2022, we have returned 126% of our year-to-date free cash flow to shareholders. As a reminder from our Investor Day, we committed to an annual 50% return target. We remain committed to capital allocation that drives shareholder returns. That includes share repurchases but also continued investment in the company that supports our strategy, the cloud acceleration and profitable growth.

On our capital structure, we upsized and extended the maturities of our debt facilities this past June, securing better pricing and increased flexibility for the company. In conjunction, we also entered into new interest rate and cross-currency swaps to reduce interest expense and minimize risk.

With regards to 2022 outlook, I would like to provide some context on the third quarter and the rest of the year. We continue to have confidence that cloud ARR dollar growth will accelerate sequentially throughout the year. We know that our fourth quarter is seasonally our highest quarter from a sales perspective, and we expect a similar pattern from total ARR and cloud ARR growth in 2022. The strength of our weighted pipeline continues to improve. As Steve mentioned, customers continue to commit more substantially to Teradata in the cloud. We see that in our increasing win rates.

Despite the current economic environment, we still have strong conviction in our cloud ARR. We are building in conservatism and widening the outlet range for total ARR to account for on-prem deal timing given the macroeconomic environment. We have reviewed our 2022 financial forecast against the end of July currency exchange rates. Despite the continued strengthening of the U.S. dollar since April 2022, we are pleased to reaffirm our outlook for all other elements in constant currency and on a reported basis. though we could be towards the lower end of our ranges if currency headwinds continue to worsen. We will continue to focus on the fundamentals that drive healthy profitability and durable free cash flow generation.

With that, we reaffirm our 2022 financial outlook. This includes approximately 80% growth year-over-year in cloud ARR as reported and in constant currency. Free cash flow of approximately $400 million and non-GAAP earnings per diluted share to be in the range of $1.55 to $1.65. For total ARR, we are widening the outlook range to decline in the low single-digit to mid-single-digit percentage range year-over-year as reported and grow in the low single digit to decline in the low single-digit percentage range year-over-year in constant currency. Our complete 2022 outlook can be found in our second quarter earnings press release and presentation.

For the third quarter of 2022, we anticipate non-GAAP earnings per diluted share to be in the range of $0.27 to $0.31. We project the non-GAAP tax rate to be approximately 21% in the third quarter and approximately 25% for the full year. We also forecast the weighted average diluted shares outstanding to be approximately 106 million shares in the third quarter and approximately 107 million shares for the full year.

Thank you very much for your time today. Let's please open the call for questions.

Question-and-Answer Session


[Operator Instructions] Your first question comes from the line of Chad Bennett with Craig-Hallum.

Chad Bennett

So just on the cloud -- public cloud ARR in the quarter. Last quarter, we really, despite the FX impacts on the rest of the business, revenues and ARR, we didn't get a call out for FX impact on public cloud ARR. What kind of change this quarter? And I guess I was under the impression that the majority of our public cloud business was being priced in U.S. dollars?

Claire Bramley

Yes, this is Claire. Thanks for your question. So yes, we did see an impact, as you say, between the 68% year-over-year and 75% year-over-year between reported and constant currency. You may have noticed we did keep our full year guide of approximately 80% in reported and constant currency. So it's just a mix that we're seeing in the current quarter for Q2 that has a bigger impact on the currency impact. But for the full year, the mix is unchanged and in line with what we laid out a few previously.

Chad Bennett

So do we expect a headwind in the second half of the year from FX to public cloud? I know you reiterated reported in constant currency, but is the delta we've seen in the quarter something we should expect?

Claire Bramley

No, I think as you move towards, there may be a small impact in Q3, but as you look to how we're going to finish that in Q4 that mix is more weighted to your in contracts in U.S. dollars. So the second half will not see a significant headwind with regards to currency.

Chad Bennett

And maybe one quick follow-up for me, if I could. Just on net expansion, Claire or Steve, for that matter, just the 120 plus, and I understand kind of the kind of development and test use cases initially and kind of the language around that. But just -- it's a trailing 12-month metric. So how do we think about from a cohort standpoint? And I know we don't have years and years and years of cohorts necessarily, but maybe 24 months back or 12-month cohort in kind of aging those cohorts and how they've kind of how expansion has played out, if you can provide any detail there.

Steve McMillan

Yes, Chad, I'll start off. Thanks very much for the question. It's really interesting when we look at the cohorts. I think what's actually playing out with our migration of our customer base to the cloud as the sales team are executing exactly on strategy and are incented to maximize their cloud ARR and that start of the migration. So we are seeing incremental cloud ARR compared to on-prem ARR at that first point of migration. And so they're actually capturing more expansion on that first point of migration, and for those cohorts, it's then impacting the subsequent net expansion rate. But overall, it actually means that we are still very solid in terms of the models that we have to achieve our cloud ARR, both and for the guidance for the fiscal year, and also for the goals that we've set out for 2025. So simply said, we're seeing more expansion on migration, and that is subsequently reducing the expansion through the life of that cohort, we expect to see that continue to Excellerate as we move through time.


Your next question comes from the line of Tyler Radke with Citi.

Tyler Radke

Claire, could you just help us understand some of the assumptions that are going into the wider range on total ARR. I guess first on the macro side, are you embedding any more conservatism in terms of close rate, deal timing? And then secondly, just because you are seeing more cloud expansions, and it does feel like you feel pretty good about the cloud ARR number. I guess if you see more cloud conversions, does that help or hurt total ARR just in terms of the economics between on-prem and cloud? Just help us understand those moving pieces as we think about the wider range.

Claire Bramley

Yes, thanks for your two question. So first of all, with regard to the extended range on total ARR. Absolutely, we are adding in some conservatism there as we look towards the end of the year. That expansion is really to take into account potential on-prem deal timing impact that we may see in Q4 given the current macro volatility and also knowing that Q4 you see really our highest quarter of total ARR growth.

With regard to your second question in the terms of it actually is good economics for us. What we see is as they are converting more of the point of migration. We may then see a little bit less expansion, but the expansion over time is very strong and continues to be -- continue to be strong. So that's what gives us that confidence, not just in the full year 2022 guide of approximately 80%. But we are, thanks to what we're doing today, we're on track also for the long-term estimate of approximately $1 billion in 2025. So very happy with where we are today based on what we see and also the coverage and the wins, the customer wins that we're seeing today and the benefit that we will continue to see in the second half of this year and as we get to over $1 billion to 2025.

Tyler Radke

And Claire, just on free cash flow. So obviously, maintaining the guide for the full year despite some currency headwinds as is impressive. It does look like if we look at the second half implied free cash flow, the seasonality or mix of free cash flow is a bit higher than we saw last year. Maybe just help us understand kind of the levers at your disposal how you're able to offset those currency headwinds and why seasonality might be a little bit different than last year?

Claire Bramley

Yes, absolutely. So as you said, very happy with the guide of approximately $400 million of free cash flow. And the fact, actually, that in the current macroeconomic environment, we are maintaining this quarter our full year guide on EPS compared to our guide last quarter. So very happy with that. With regards to the cash flow generation, our biggest opportunity is in working capital. We've seen some great trends in our cash collections, for example, and a generation of working capital. And we've done that again in Q2, as you've seen with $100 million of free cash flow generation. So we're confident that we can keep that healthy balance sheet and strong working capital and cash conversion cycle as we continue through the year.


Your next question comes from the line of Wamsi Mohan with Bank of America.

Wamsi Mohan

If we look at your total ARR at constant currency, your initial outlook at the beginning of the year was high single-digit growth. And today, at the midpoint, it's flat. And I know 4 points of that is Russia, which you couldn't have anticipated. But given your more mission-critical enterprise workloads, it would seem that you should be relatively more resilient to a macro downturn.

And Steve, you noted some of that resiliency in your prepared comments, you also noted us a strong pipeline. So can you just help square the change in your constant currency total ARR from sort of the beginning of the year through now -- through that lens, if possible? And I have a follow up.

Steve McMillan

So the -- as you said, the biggest and lags or total ARR are clearly Russia and currency, yes. What we see in the business, and you had it right on the head, our workloads that we run inside our customers are absolutely mission-critical. Unlike many of our competitors, we don't run discretionary workloads around marketing campaigns or sales campaigns. We are helping these organizations, operate, close their books, run their supply chains, run the critical operations of the business. And that means that our ARR is very sticky and solid in our customer base. Our pipeline -- our cloud pipeline for the second half of the year continues to strengthen in both quantity and quality as we progress those opportunities. So I guess, the biggest delta is from a Russia and FX perspective.

Wamsi Mohan

And Claire, your gross margins on recurring revenues were at the lowest level in a while. And can you just talk about the moving pieces here? How much of that is relative to change an upfront payments? How much of that is maybe FX? And on upfront side, I think you called out sort of a delta versus expectations in the second quarter and now maybe more upfront in the fourth quarter. Can you talk about what is the underlying cause that's actually driving that change?

Claire Bramley

Absolutely. So yes, to your point with regards to recurring revenue margin, two factors that we see both quarter-over-quarter and year-over-year. So first of all, we have a full quarter in recurring revenue of the impact of Russia. So an impact there. We also obviously see the currency impact and the upfront impact. And just in case people haven't seen it on Page 8, of our earnings presentation. We have actually laid out the impact across ARR, total revenue and recurring revenue by quarter of those three headwinds that we're seeing. So upfront revenue, FX in Russia. And as I believe everyone will remember from last earnings, Russia was an accretive business for us. So we did have a drop to the bottom line out of FX and the upfront revenue year-over-year decline that we're seeing in Q2. So just as a reminder, I said in my prepared remarks, we saw a negative impact of $6 million in the current quarter compared to a positive of 22.

To your point, Wamsi, on timing, so just as a reminder, that is purely driven by the kind of renewal and expansion of our on-premise customers. It has always been difficult to predict, which is why we tend to provide high-level estimates. So there was a slight difference with what we were expecting in Q2, as you said, slight negative versus a small net positive. And it's just the deal timing. And that's the same as well as we look out to H2.


Your next question comes from the line of Erik Woodring with Morgan Stanley.

Unidentified Analyst

This is Sabrina on for Erik Woodring. I guess our first is you beat 2Q EPS out your outlook by the midpoint, but kept the full year EPS guide unchanged, which implies the second half is coming down slightly. Can you talk about why that is, given your revenue outlook hasn't changed? And then I have a follow-up.

Claire Bramley

Yes, absolutely. So actually, the $0.02 of the $0.03 above the top end of the range was actually from the favorable taxes. So that's not a knock-on benefit. Our overall tax rate for the year is unchanged. It's just a seasonality of our tax rate. Sabrina. And so actually -- and also with the additional currency headwinds, we have seen a slight -- if we look forward to the second half of the year, we have seen a slight deterioration in the currency rate by approximately 25 basis points. So we're absorbing that into our full year EPS as well. So they are the kind of big drivers from an EPS standpoint. I missed your follow-up question, sorry.

Unidentified Analyst

Yes. And just a follow-up is, can you talk about how your customer conversations have evolved since the end of the quarter? Have there been any changes by geography, deal size or the pace of customer decision-making given the macro environment?

Steve McMillan

Sabrina, no -- this is Steve. Thanks for the question. We're not seeing deltas from that perspective. Again, we're very solid in our existing customer base. We understand what those customers are doing. We've got long-term relationships with our existing customer organizations. We haven't seen any demand signal weakness for our offers or for what the customers are buying either in this past quarter or for the rest of the year. And as I mentioned earlier, we're actually seeing our cloud pipeline continue to increase. So that gives us some good demand signal from a cloud perspective. That point that we're mission critical and customer base means that commented ARR and those multiyear agreements that they've said what is gets us some stability around our business model and financial results.


Your next question comes from the line of Matt Hedberg with RBC Capital Markets.

Anushtha Mittal

This is Anushtha from Hedberg. Maybe to start with, could you talk about how you're thinking about the pace of hiring in the remainder of 2022, given the inflationary environment? And what will be the key areas of development.

Steve McMillan

Thank you for the question. Yes, we we're going to continue hiring critical talent, especially cloud talent even in the past quarter or so, we've recruited a new head of our EMEA organization. We are looking very prudently across all of our investments. Our focus is to make sure that we maintain profitable growth. We will continue to invest in the business in key areas where we think that growth will be driven from, especially from a cloud perspective, cloud skills capabilities and continuing to advance our strategy. we haven't seen any challenges from a recruitment perspective. I think that one of the real strengths of Teradata is actually the culture of the company. People love coming to work for Teradata. They can bring their genuine authentic cells. And I think that culture and the environment that we create creates a really positive place for people who want to stay with us, they want to demonstrate great results, but also enables us to attract in the rate kind of talent. But again, from an investment perspective, we are going to be prudent on our investments to ensure that we have that profitable growth.

Anushtha Mittal

And then in the current environment, as enterprises increasingly prioritize ROI investments, could you talk about the durability of Teradata's platform in a recessionary scenario? And in addition to the functionality, how does -- does better price performance than competitors serve as a benefit in landing new logos?

Steve McMillan

Yes. Absolutely. Price performance and then price performance is a key way one that we win new logos, it also increases the stickiness of our platform. The workloads that we run for our customers are absolutely mission critical. So the durability of the platform is incredibly strong. I actually look at it from an AI and ML and analytics perspective. Our customers tend to only use about 20% of the capabilities of the platform. So we see that as a key opportunity for us and for our customers to utilize those capabilities, perhaps reduce spend on other platforms that they have inside their technology stack and use Teradata capabilities that they're already paying for and they are already in the Teradata platform. that as well gives us opportunity and also enables us to, again, be very stable in customer environment.


Your next question comes from the line of Raimo Lenschow with Barclays.

Unidentified Analyst

This is Sheldon on for Raimo. We are certainly hearing different perspectives on the challenging macro environment by region. And in the quarter, it looks like the Americas segment for revenue declined 8% in constant currency versus a strong double-digit growth last quarter. This is a little surprising given the insulation from Russia and FX and the mission-critical nature. Was that a surprise to you? And is there any moving parts there to consider? Would it be the less upfront recognition in the quarter?

Claire Bramley

Yes. So absolutely, towards the end, you hit them on the head. As you saw, we're actually growing in constant currency Americas for the first half, but specifically in Q2, as you said, we are declining and the big impact there in constant currency is from the upfront revenue recognition. So it's a seasonality impact between Q2 of this year versus Q2 of last year. But for overall for the half, we are seeing constant currency growth in the Americas.

Unidentified Analyst

And then a quick follow-up, if I may. How has the reception been from customers on the blended pricing with both the capacity-based piece and the consumption element, and how did that consumption element specifically perform versus your expectations in the quarter?

Steve McMillan

Yes. So we are seeing some of our customers take advantage of that blended pricing model, but by far and away, the bulk of our revenue is actually in fixed capacity agreements, which gives us that, again, financial surety around our business model and results for the year. We are starting to see our customers utilize those consumption agreements for first in the workload. We think as well as a great way to when new logos. So that customers can start small with us and then grow. But even in those agreements, what we see is customers really want to get the best of a blended model because they can contract with is at a reduced rate for fixed capacity. And then just first for what you need, that gives us a real insight into the financials of our cloud business as we move forward. So it's an attractive model that our customers are taking advantage of. But by far and away, it's a small part of our existing recurring revenue streams.


Your next question comes from the line of Derrick Wood with Cowen.

Derrick Wood

I jumped on late. But from what I've heard so far, it sounds like you're not seeing a material impact from the macro. You remain confident in the demand and pipeline around cloud, but perhaps there was some delay in on-prem renewals, and I wanted to touch on that if I look at the on-prem ARR, I come up with about 6% decline in constant currency, which was down versus a 3% decline last quarter, or maybe flat in Q4. Was that an area that was a little weaker? And with respect to your guidance for total ARR, are you expecting that to make that up in terms of stronger renewal of that business in the second half? Or do you assume perhaps that some of those renewals slip?

Claire Bramley

Yes, let me just start on the impacts that we're seeing on total ARR. So you may have missed it that we have added a new disclosure on Page 8 of our earnings presentation, which breaks down the impacts that we're seeing across total ARR revenue total revenue and recurring revenue. And what you'll see there is that across currency and Russia year-over-year, we're actually seeing 8% impact year-over-year. So a combination of currency and Russia being a significant headwind. And actually, that increases to, if you include upfront revenue and the timing and seasonality we were talking about, that's impact from total revenue and 14% impact on recurring revenue year-over-year. So once you look at that and have a look at that additional disclosure, hopefully, that clarifies that actually the underlying business is growing as expected.

And I'll just pass to Steve to talk about the macro.

Steve McMillan

Yes. From a macro perspective, Derrick, as you know, we are rock-solid in our customers, executing mesh-critical workloads that gives us a lot of stability of the revenue platform. And in addition to that, our business model being now 80% recurring revenue gets great revenue stability as we move through the rest of the year. The commitment -- the multiyear commitments around fixed capacity that we have for our customers also gets his confidence in our second half financial performance. So from a macro perspective, clearly, there are impacts, but we have a very, very strong financial model.

Derrick Wood

And then back on the question, clarity around free cash flow. I mean you guys have done a great job in light of the FX headwinds, Russia and really kept that the health of the free cash flow this year. Are there -- I know you don't guide beyond this, but are there -- were there onetime impacts this year that you were able to pull levers on that could be tough to be repeatable going into next year? Or is that just like cash conversion focus, something that can be durable?

Claire Bramley

So I believe the cash conversion focus is durable, Derrick, absolutely within great, consistent performance. So absolutely, cash conversion cycle is durable. We did have a tax refund benefit, which I talked about in Q1, which is a benefit in the quarter, but I do have absolutely confidence in our long-term generation of durable free cash flows. And as we laid out at Investor Day last year, we still have good line of sight of approximately $550 million by 2025.


There are no further questions at this time. I will now turn the call back over to Steve McMillan for his final remarks.

Steve McMillan

Thank you, Lisa. As we sign off, I've got great confidence in our future. Our strategy in the company is absolutely right. We are growing in the cloud, and we're driving a strong pipeline for the second half of the year to accelerate that momentum. Our Vantage data and analytics platform remains mission-critical for enterprises all over the world, and I'm incredibly excited about our upcoming announcement that takes our industry-leading capabilities into the next-generation so that companies can scale smarter, innovate faster and grow stronger in the cloud. We remain really focused on delivering shareholder value. Thanks for joining us today.


This concludes today's conference call. You may now disconnect.

Thu, 04 Aug 2022 14:42:00 -0500 en text/html
Killexams : Data Science—MS

The Michigan Tech Advantage

The Michigan Tech Data Science MS provides a broad-based education in data mining, predictive analytics, cloud computing, data-science fundamentals, communication, and business acumen. You'll gain a competitive edge through domain-specific specialization in disciplines of science and engineering, and you'll have the freedom to explore and develop your own interests in one or more domains. 

Navjot Kaur

Program Prerequisites

Entry into the Data Science MS program assumes basic knowledge in statistical and mathematical techniques, computer programming, information systems and databases, and communications, obtained through a degree in business, math, computing, science, or an engineering discipline.

Past Coursework Requirements

Each year we evaluate and adjust our course lists, the coursework requirements for prior years are linked below.

Current Coursework Requirements

Our Master of Science in Data Science is a terminal degree designed to prepare students for careers in industry and government.

MS, Data Science: Coursework Option

This option requires a minimum of 30 credits be earned through coursework. A limited number of research credits may be used with the approval of the advisor, department, and Graduate School. See degree requirements for more information.

A graduate program may require an oral or written examination before conferring the degree and may require more than the minimum credits listed here:

Distribution of Coursework Credit
Distribution Credits
5000-6000 series (minimum) 18 Credits
3000-4000 (maximum) 12 Credits

Students in the Data Science program take courses from four categories: Core Courses, Elective Courses, Foundational Courses, and Domain Specific/Elective courses.

Core Courses—12 credits

Foundational Courses—Maximum of 6 credits

A maximum of six credit hours of foundational skills courses at the 3000–4000 level may be applied to the Master of Science in Data Science. These courses will build skills necessary for successful completion of the MS in Data Science. Some students will not need to take these foundational courses and will instead use the domain electives to reach the credit requirements of this program.

Electives—Minimum of 6 credits

Two courses must be taken from the list of approved elective courses:

Domain Specific Courses—Maximum of 12 Credits

To complete the Master of Science in Data Science, students must earn the remaining of the required 30 credits through completion of approved domain-specific Data Science courses. Students may choose domain-specific courses from one or more domains. Each student will consult with her/his advisor in order to determine the appropriate mix of elective courses and domain-specific courses, given the student’s background, interests, and career aspirations.

Biomedical Engineering


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Business and Economics


Cognitive and Learning Sciences


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Computer Sciences

Electrical and Computer Engineering

Forest Resources and Environmental Science

Geological and Mining Engineering and Sciences


Mechanical Engineering-Engineering Mechanics


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Social Sciences


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Applied Computing



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Sample Schedules

"The best parts of Computing[MTU] are the quality of the coursework and the helpful nature of the  professors."

Navjot Kaur, Data Science Student

Laura Brown

Thu, 12 May 2022 01:56:00 -0500 en text/html
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