Python online courses are educational programs that teach users about Python, a high-level programming language.
Python is not too difficult to learn and is generally used to develop websites and software, among other things.
Alphabet Inc. is the holding company that owns Google, along with a portfolio of other companies and assets. Among these many entities – including Calico, Sidewalk Labs, Chronicle, Dandelion, DeepMind, Google Fiber, Waymo and numerous others – Google is certainly first and foremost. By itself, even Google is no simple beast, though. It acts as the umbrella company for all of Alphabet’s business with an internet focus or connection, including the Android mobile OS, YouTube and Google Search, among many other elements.
Given Google’s enormous market recognition and mindshare, it may come as something of a surprise to learn that it is not the market leader in cloud services and delivery. In fact, Google didn’t make Forbes’ 2017 list of The Top 5 Cloud Computing Vendors. That said, the Google Cloud Platform (GCP) is a member of the top five such platforms, along with Microsoft Azure and Amazon Web Services, which routinely swap between first and second place. Oracle and IBM also place in the top five as well, often ahead of the Google Cloud Platform, depending on the metrics used to rank them.
Given all this, Google has powerful incentives to create and get behind a potent and well-regarded certification program for the Google Cloud Platform. Its efforts over the past two or three years are starting to pay some dividends, as an upcoming chart of job board search results will illustrate. But first, let’s take a look at the Google Cloud Platform certification portfolio as it currently stands.
The Google certification program has experienced significant growth since our last update. At our last update, Google offered three certifications, one at the associate level and two at the professional level. Today, Google offers one associate-level credential, five professional level certifications, plus a certification aimed at G Suite productivity and collaboration tools. Current certifications include:
To earn a Google certification, candidates must pass a single exam. All exams are reasonably priced with professional-level exams costing $200, $125 for associate-level exams, and $75 for the G Suite exam.
Associate- and professional- exams must be taken at a Kryterion testing center. At present, the G Suite test is remote. While there are no mandatory prerequisites for any certification, training is highly recommended, and Google maintains links to various training resources on the respective test web page.
Google is also affiliated with Coursera, and candidates will find formal training available through Coursera as well. At least six months of experience working with Google Cloud Platform is recommended for associate-level credentials, and a minimum of three years of industry experience for professional-level certifications with at least one year in designing and managing GCP solutions.
The Associate Cloud Engineer (ACE) deploys applications, monitors operations and manages enterprise solutions. He or she can use Google Cloud Console and the command line to complete common platform-based tasks. An ACE also maintains one or more deployed solutions that use either Google- or self-managed services in the Google Cloud environment.
To qualify candidates, the ACE exams seek to assess these specific abilities regarding Google Cloud solutions:
Google recommends two training courses: Google Cloud Platform Fundamentals: Core Infrastructure and Architecting with Google Cloud Platform: Infrastructure and are available in ILT and online formats. Both courses are also offered in affiliation with Coursera. Qwiklabs also offers Google Platform Essentials labs and a Cloud Architect Quest to support hands-on learning and experience.
It’s absolutely correct to treat the ACE as the entry-level credential for the Google Cloud Platform. It’s most likely to appeal to early-stage or mid-career IT professionals interested in cloud computing, who work with (or want a job with an organization that uses) the Google Cloud Platform. The ACE represents a great way for such people to learn and acquire the skills and knowledge needed to set up, deploy and manage a runtime environment that incorporates the Google Cloud Platform.
The Professional Cloud Architect (PCA) enables organizations to make effective and efficient use of Google Cloud technologies. PCAs must develop a thorough understanding of cloud architecture in general, and the Google Cloud Platform in particular. Those who hold this credential can design, develop and manage dynamic Google Cloud Platform solutions to meet business objectives that are robust, secure, scalable and highly available.
To qualify for the PCA, the exams seek to assess these specific abilities regarding Google Cloud Platform solutions:
A slate of related curriculum elements for the PCA is available online through Coursera, or in the classroom, as candidates’ needs and budgets may dictate. The same labs and quests offered for the ACE also apply to the PCA as well.
The PCA represents a more senior credential that’s most likely to appeal to mid- to late-career professionals interested in filling a cloud architect role in an organization of some size. Thus, the ACE makes a pretty good precursor to the PDE (even though it’s not formally required as a pre-requisite).
The Professional Data Engineer (PDE) focuses more on analyzing and using data stored in the Google Cloud Platform, rather than in designing, deploying or maintaining such environments as with the ACE and the PCA. As such, a PDE supports and facilitates data-driven decision-making based on collecting, transforming and visualizing data. Such professionals design, build, maintain and troubleshoot data processing systems. The PDE curriculum and test puts particular emphasis on ensuring that such data processing systems are secure, reliable and fault-tolerant, as well as scalable, accurate, and efficient.
To qualify for the PDE, the exams seek to assess these specific abilities regarding Google Cloud Platform solutions:
A different slate of courses is offered for the PDE, covered on the Data and Machine Learning page at Google Training. Candidates may choose among courses for three tracks for this credential: a data analyst track, a data engineering track and a data scientist track. In addition to a data engineering quest for hands-on PDE training, Google also offers an advanced, four-week machine learning advanced solutions lab at the main Google campus in Mountain View, California. A set of five practice exams may be purchased from Udemy.com for $24.99.
IT professionals interested in big data, data analysis, and/or machine learning are most likely to be attracted to the PDE. It’s a great credential for those with strong data interests and proclivities anywhere in their IT careers, though a strong background and interest in mathematics and data modeling/analysis is strongly recommended.
The Professional Cloud Developer (PCD) is ideal for candidates who use Google services, tools and recommended practices to design, build, test, and deploy highly available and scalable applications. Candidates should possess the skills necessary to successfully integrate GCP services and conduct application performance monitoring. While not covered on the exam, candidates need to be able to successfully use Stackdriver to debug, trace code, and produce metrics. Proficiency in at least one general programming language is also recommended.
The test is focused on validating a candidate’s ability and skill to use GCP services and practices in five key areas:
On the certification web page, candidates will find links to an test outline and trial case studies to help prepare for the exam. Recommended training includes the Google Cloud Platform Fundamentals: Core Infrastructure course and the Developing Applications with Google’s Cloud Platform. Quests on application development for Java or Python and core technologies, such as Stackdriver, Google Cloud Solutions: Scaling Your Infrastructure, and Kubernetes solutions, are also recommended.
A Google Professional Cloud Network Engineer (CNE) manages and implements network architectures using GCP. In addition to GCP, successful candidates should be skilled in working with technologies such as hybrid connectivity, network architecture security, VPCs, network services, and the GCP Console command line interface.
The test is comprehensive and covers related topics:
Recommended training includes the Core Infrastructure course and Networking in Google Cloud Platform. If you’re looking for hands-on practice, Qwiklabs offers labs for networking in the Google cloud and network performance and optimization.
Another newcomer to the Google certification portfolio is the Professional Cloud Security Engineer (CSE). An expert-level credential, CSEs are well-versed in industry security requirements, regulations, best practices, and security-related courses and technologies, such as identity and access management, data protection using GCP, configuring security at the network level, analyzing logs, managing incidents, and recommending organization-wide security policies. CSEs also possess the skills necessary to design, implement and manage secure infrastructures on GCP.
The test validates a candidate’s ability to:
As with other certifications, Google provides a free test outline and overviews plus in-depth discussions. In addition to the Core Infrastructure course, Google recommends taking the Security in Google Cloud Platform training and the Security and Identity Fundamentals Qwiklabs.
The G Suite cert aims at end users of Google’s productivity suite. As such, it’s likely to have only limited appeal for IT professionals, most of whom learn a productivity suite (MS Office, most typically) before they graduate from high school. The test targets a candidate’s ability to communicate, work with, and manage tasks using the G Suite productivity and collaboration tools, including Drive (cloud-based storage), Gmail (cloud-based email and messaging), Hangouts Meet (online meetings), Docs (cloud-based document creation and editing), Sheets (cloud-based spreadsheets), Forms, and Slides (cloud-based presentation software).
The certification web page contains links to a number of training options including Qwiklabs, self-paced G Suite lessons, applied digital skills, and the G Suite Learning Center.
For those who work around or with the Google Cloud Platform, the current certifications seem like a very safe bet for career and personal development. Given high demand, relatively low cost and a single test for these certifications, the risk-reward ratio looks quite favorable. Be sure to check them out, if you work (or would like to work) in an organization that uses this cloud platform.
Managing contracts is an overlooked form of management. Managers interact frequently with employees, and some of those discussions and situations naturally relate to compensation. Some of these conversations will deal with contract management. Other times, businesses need to manage contract agreements with other businesses. It’s not talked about much, but contract management is an important business topic. If you’re unsure of how the contract management process works, it’s important to understand the basics.
Contract management is the process of managing contract creation, execution, and analysis to maximize operational and financial performance at an organization, all while reducing financial risk. Organizations encounter an ever-increasing amount of pressure to reduce costs and Boost company performance. Contract management proves to be a very time-consuming element of business, which facilitates the need for an effective and automated contract management system.
When two companies wish to do business with each other, a contract specifies the activities entered into by both organizations and the terms through which they will each fulfill their parts of the agreement. Contracts affect business profitability in a very large way due to the emphasis on revenue and expenses.
When a contract is phrased poorly, one organization might lose countless thousands of dollars over a simple technicality they lacked the resources to identify. Effective contract management can ultimately create a powerful business relationship and pave the road to greater profitability over the long term, but only when managed correctly. It’s a good idea to include a legal department or a lawyer in contract management discussions. The precise wording of contracts is crucial to contract management.
Contract management also applies to managing different contracts with freelancers or employees. These occasionally require management and alterations that help both parties.
Generally, contract management involves a few key stages. There’s the early stages or pre-award phase. This is all the work that takes place prior to a contract being given to someone, whether it be a business or an employee. The middle stage is when the process is awarded. This includes all the paperwork to make the agreement final. Third, there’s the post-award stage. This is where a lot of contract management and maintenance comes in.
Those three basic stages are a simple way of looking at contract management in three phases, but the process is more complicated than that and can be viewed in more stages depending on how detailed a view you’re taking. We’ll discuss a deeper view of the process later.
It isn’t enough that an organization has professionals in place to handle contract management. Employees must be augmented with the presence of processes and software companions to satisfy increasing compliance and analytical needs. When a contract management strategy is successfully implemented, organizations can expect to see:
The foundation for contract management relies on the implementation of successful post-award and upstream activities. During the pre-award stage, employees should focus on the reason for establishing the contract and if the supplier can fulfill the terms of the agreement.
Additional consideration is needed to understand how the contract will work once awarded. Avoiding unwanted surprises requires careful research and clarity of purpose in the real contract.
Contract management requires a level of flexibility for both parties involved and a willingness to adapt contract terms to reflect any changing circumstances. Problems are inevitable, which means organizations must be prepared for the unexpected and be able to adjust contract terms when needed. [Related Story: Loan Contract Terms to Look For]
While there are many components of contract management, we can summarize the process by breaking it into five clear stages: creation, collaboration, signing, tracking and renewal.
We can further identify individual steps within the stages. In all, we can break the process down into nine steps, each of which contributes to one of the five overarching stages. This makes it easier to manage the end-of-quarter crunch that tends to happen when it’s time for a new round of contracts. Here are the steps of each stage:
1. Initial requests. The contract management process begins by identifying contracts and pertinent documents to support the contract’s purpose.
2. Authoring contracts. Writing a contract by hand is a time-consuming activity, but through the use of automated contract management systems, the process can become quite streamlined.
3. Negotiating the contract. After drafting the contract, employees should be able to compare versions of the contract and note any discrepancies to reduce negotiation time.
4. Approving the contract. Getting management approval is the step where most bottlenecks occur. Users can preemptively combat this by creating tailored approval workflows, including parallel and serial approvals to keep decisions moving at a rapid pace.
5. Execution of the contract. Executing the contract allows users to control and shorten the signature process through the use of electronic signature and fax support.
6. Obligation management. This requires a great deal of project management to ensure deliverables are being met by key stakeholders and the value of the contract isn’t deteriorating throughout its early phases of growth.
7. Revisions and amendments. Gathering all documents pertinent to the contract’s initial drafting is a difficult task. When overlooked items are found, systems must be in place to amend the original contract.
8. Auditing and reporting. Contract management does not mean drafting a contract and then pushing it into the filing cabinet without another thought. Contract audits are important in determining both organizations’ compliance with the terms of the agreement and any possible problems that might arise.
9. Renewing. Manual contract management methods can often result in missed renewal opportunities and lost business revenue. Automating the process allows an organization to identify renewal opportunities and create new contracts.
Much of contract management comes down to handling these nine steps. Contract lifecycle management is critical. As different contract types go through their various stages, contract managers need to monitor any potential changes or breaches of contract. If an employee or business is unhappy with their contract, it might be worth making alterations to the contract. It’s important to follow contractual obligations while also making sure both sides of the contract are happy.
There are many times during the contract management process when lifecycle management becomes important. Vendor performance and risk management are important considerations during the management of contracts. For example, if a vendor fails to meet their contractual obligations, you may need to rework the contract or enforce some disciplinary measure.
While the tradition is to manage contracts manually through folder and file cabinet storage, the practice is riddled with inefficiencies that can only detract from an organization’s overall efficiency.
Contract management software is an electronic approach to solving these problems. Contract management software suites can organize all contract paperwork. The software can put signing and renewing on an electronic calendar that is easy to manage, and it can help you track and allocate resources related to the contract management process.
Integration with an automated contract management service can free up countless man-hours and automate countless processes associated with managing a contract, thus creating more value for a company.
“Contract management software stores key information about contracts relating to providers, commercial leases and licensing agreements,” said Robert Powell, CEO and founder of the Rob Powell Biz Blog. “The overall purpose of contract management software is to streamline administrative tasks by creating a centralized and uniform record for each contract’s processes.”
Using contract management software can make it easier to monitor complex contracts without relying solely on paperwork.
“The most important aspect of contract management software is that it allows employees in multiple locations to access contracts in one place,” Powell said.
This software will primarily see use in departments that directly deal with creating, tracking and signing contracts. This is often offloaded to the HR department, which manages the vein of employment accounting. The software can also involve managers who need to complete vital processes. Since it can integrate with calendars and communication software, HR can use the heavy-lifting components of the software suite, while the rest helps to loop in managers and personnel who are needed for specific aspects of signing or negotiation.
Not all universities offer a degree in contract management, but some schools do. Getting that education is one option, but there are other business degrees that position you for success in the industry. From there, you want to add contract management experience in some form.
“With a bachelor’s degree and a few years of experience in the field, you can apply and test for certification through the NCMA (National Contract Management Association),” said Jared Weitz, CEO and founder of United Capital Source. “Along with education and credentials, a contract manager needs to have solid communication and writing skills and a keen eye for organization and deadline management.”
A law degree can also be beneficial to this career path. Legal knowledge is critical in managing contracts. Contract management and negotiation both rely on legal knowledge and expertise.
If you want to become a full-time contract manager, it’s a good idea to connect with other contract managers to learn how they entered their current role. There’s no one set way to become a contract manager, but business experience is important when becoming a contract manager.
Understandable, the salary for a contract manager varies by qualifications and location. According to PayScale, the average annual salary for a contract manager is $80,151. The site lists Northrop Grumman Corporation, Accenture, and the Raytheon Company as a few of the most lucrative companies for contract managers.
Contract managers can also work their way up to a senior contract manager, contract director or a contract administrator. Contract analyst is another common career path within the contract management field.
Contract managers help manage the legal and financial aspects of contracts with businesses or employees. For companies that make frequent contractual agreements, hiring a contract manager can be a good idea.
Ryan Goodrich contributed to the writing and reporting in this article. Source interviews were conducted for a previous version of this article.
A blue chip is a nationally recognized, well-established, and financially sound company. Blue chips generally sell high-quality, widely accepted products and services. Blue-chip companies are known to weather downturns and operate profitably in the face of adverse economic conditions, which helps to contribute to their long record of stable and reliable growth.
The term "blue chip" was first used to describe high-priced stocks in 1923 when Oliver Gingold, an employee at Dow Jones, observed certain stocks trading at $200 or more per share. Poker players bet in blue, white, and red chips with the blue chips having more value than both red and white chips. Today, blue-chip stocks don’t necessarily refer to stocks with a high price tag, but more accurately to stocks of high-quality companies that have withstood the test of time.
A blue-chip stock is generally a component of the most reputable market indexes or averages, such as the Dow Jones Industrial Average, the Standard & Poor's (S&P) 500 and the Nasdaq-100 in the United States, the TSX-60 in Canada, or the FTSE Index in the United Kingdom. How big a company needs to be to qualify for blue-chip status is open to debate. A generally accepted benchmark is a market capitalization of $5 billion, although market or sector leaders can be companies of all sizes.
A blue-chip company is a multinational firm that has been in operation for a number of years. Think companies like Coca-Cola, Disney, PepsiCo, Walmart, General Electric, IBM, and McDonald’s, which are dominant leaders in their respective industries. Blue-chip companies have built a reputable brand over the years and the fact that they have survived multiple downturns in the economy makes them stable companies to have in a portfolio.
The name "blue chip" came about from the game of poker in which the blue chips have the highest value.
Many Conservative investors with a low risk profile or nearing retirement may usually go for blue-chip stocks. These stocks are great for capital preservation and their consistent dividend payments not only provide income, but also protect the portfolio against inflation. In his book The Intelligent Investor, Benjamin Graham points out that conservative investors should look for companies that have consistently paid dividends for 20 years or more.
The Dividend Aristocrat List published by Standard and Poor’s comprises of large-cap blue-chip companies from the S&P 500 that have increased dividends every year for the last 25 years.
Blue-chip stocks are seen as less volatile investments than owning shares in companies without blue-chip status because blue chips have an institutional status in the economy. The stocks are highly liquid since they are frequently traded in the market by individual and institutional investors alike. Therefore, investors who need cash on a whim can confidently create a sell order for their stock knowing that there will always be a buyer on the other end of the transaction.
Blue-chip companies are also characterized as having little to no debt, large market capitalization, stable debt-to-equity ratio, and high return on equity (ROE) and return on assets (ROA). The solid balance sheet fundamentals coupled with high liquidity have earned all blue-chip stocks the investment-grade bond ratings. While dividend payments are not absolutely necessary for a stock to be considered a blue chip, most blue chips have long records of paying stable or rising dividends.
An investor can track the performance of blue-chip stocks through a blue-chip index, which can also be used as an indicator of industry or economy performance. Most publicly traded blue-chip stocks are included in the Dow Jones Industrial Average (DJIA), one of the most popular blue-chip indices. Although changes made to the DJIA index are rare, an investor tracking blue chips should always monitor the DJIA to stay up to date with any changes made.
While a blue-chip company may have survived several challenges and market cycles, leading to it being perceived as a safe investment, this may not always be the case. The bankruptcies of General Motors and Lehman Brothers, as well as a number of leading European banks during the global recession of 2008, is proof that even the best companies may struggle during periods of extreme stress.
While blue-chip stocks are appropriate for use as core holdings within a larger portfolio, they generally shouldn't be the entire portfolio. A diversified portfolio usually contains some allocation to bonds and cash. Within a portfolio's allocation to stocks, an investor should consider owning mid-caps and small-caps as well.
Younger investors can generally tolerate the risk that comes from having a greater percentage of their portfolios in stocks, including blue chips, while older investors may choose to focus more on capital preservation through larger investments in bonds and cash.
While iOS users are typically the most engaged mobile shoppers, building a better Android shopping application will be crucial for retailers going forward as the number of users on the platform continues to explode.
Retailers can take a cue from eBay, whose app engages consumers more on Android than on iOS, and consider each device’s menu and navigation setup to create a better experience for Android users. Retailers need to spend time learning about typical Android users to cater the shopping experience to the different devices and audience.
“The factors that make a good Android app versus iOS are very similar,” said Sheryl Kingstone, Toronto-based research director at Yankee Group. “The fundamentals of user experience, providing value like cross-channel experience, all of that are the same whether you’re on Android or iOS.
“What’s going to change is the native experience, that you’re not just creating an iOS app and transferring that over,” she said. “You really want to make sure that you’re building an experience for the native OS, that you’re learning from your iOS app and transferring some of those skills but recreating it for Android.
Android is a bit trickier to develop for than iOS because it is fragmented with devices from many different manufacturers.
A latest report from Yankee Group and Mobidia took a look at shopping apps and how they measure up for today’s consumers. The report, “Does your mobile shopping app stack up?,” found that a number of shopping apps displayed a gap in terms of engagement on iOS versus Android.
The report looked at an app’s weekly user activity and monthly user activity to assign a percentage based on the frequency with which consumers return to the app.
Most companies on average had a 3 percent difference for app usage between iOS and Android, with iOS coming out on top. A few apps showed more significant differences.
Victoria’s Secret saw 76 percent for iOS usage and 41 percent for Android, and H&M saw 75 percent for iOS and 46 percent for Android. Both retailers also demonstrated poor performance in number of application sessions.
EBay was the only app to significantly engage consumers more on Android than on iOS with 63 percent on Android and 55 percent on iOS.
According to Ms. Kingstone, eBay’s Android app does a good job of taking into account the device’s menu and navigation setup, creating a better experience for Android users.
In the upcoming year, retailers should expect there to be a bigger focus on improving Android apps, and they should step up their game on the platform.
Facebook is leading the way with its latest acquisition of India-based Little Eye Labs, which builds performance analysis and monitoring tools specifically for Android apps. The acquisition will help Facebook Boost its own Android app and points to a shifting focus towards the platform.
Android vs. iOS
Regardless of platform, a retailer should include easy, quick checkout in an app.
However, retailers still need to tailor the experience to the different devices.
First of all, the layout and navigation on iOS and Android devices are different. Simply placing a button in a different place could make a huge difference for some users.
Little graphical changes like color schemes, menu buttons and the navigation of where certain buttons lay can all make a huge difference.
“If you make something unfamiliar you’re not going to get them to use the app because they’re going to get frustrated,” Ms. Kingstone said.
Beyond technical differences, each device tends to attract a different type of user.
For instance, Android users tend to be younger and from a lower-income bracket. They also tend to care more about social media and deals.
IBM recently found that iOS beat Android in terms of revenue over the holidays. Ms. Kingstone points to the differences in userbase as a reason for this.
“I think one of the reasons why iOS pulled in more revenue compared to Android has to do with the user,” Ms. Kingstone said. “IOS users have higher incomes and are older, both of which translate to shopping.
“So, it isn’t that people don’t shop on Android, but the users on Android are younger and have less income,” she said.
Beyond navigation and user experience, retailers should also leverage additional features that Android has to offer such as predictive marketing.
“There is also a growing trend to be more predictive and suggestive based on the users’ previous behavior,” said Chris Hill, vice president of marketing at Mobidia, Richmond, BC, Canada. “Google’s Now app is a great example of this.”
Another thing to keep in mind when developing an Android shopping app is how they process payments.
“One of the biggest advantages to Android is the Google Wallet app,” said Li-at Karpel Gurwicz, marketing director at Conduit Mobile. “Some Android phones are even equipped with an NFC chip that can be used for making wireless payments simply by tapping the phone at the checkout counter.”
Retailers can take advantage of this and offer simpler checkout in an Android app using Google’s API.
While retailers should definitely tailor an app towards a specific device, it is still a good idea to test one before the other. That way they can learn from their mistakes on the first try and avoid making them again on the next platform.
Since iOS is more streamlined than Android with only one platform, it makes sense for retailers to begin with iOS and move on to Android afterwards. That does not, however, mean that retailers should assume the same exact app will work on Android.
Retailers need to devote time and money to testing apps for both platforms.
When developing the apps, retailers need to keep a lot in mind.
“You need to decide whether your app will do a broad range of functions at an average level, or just a couple of functions very well,” said Peter Olynick, card and payments practice lead at Carlisle & Gallagher, Charlotte, NC. “You need to decide whether your goal is to retain customers who are shopping in your store or to steal customers who are shopping at other stores.
“Customers will not use apps with poor customer experience,” he said. “They expect the app to be compatible with their existing phone, easy to set-up/use, and make the overall shopping experience better.”
Rebecca Borison is editorial assistant on Mobile Commerce Daily, New York
The best Python online courses make it simple and easy to learn, develop, and advance your programming skills.
Python is one of the most popular high-level, general-purpose programming languages. Named after the comedy troupe Monty Python, the language has a user-friendly syntax that makes it very appealing to beginners. It’s also very flexible and scalable, and has a very vibrant, global community of users.
Thanks to its rich set of tools and libraries you can use Python for just about anything -- from web development and data analysis to artificial intelligence and scientific computing.
According to the TIOBE Index, Python is currently the most popular programming language in the world. In fact, Python is used in some form or the other in virtually all major tech companies around the world, which makes it one of the top-most demanded skills.
If you want to work with Python scripts, you'll need a text editor suitable for coding and an Integrated Developed Environment (IDE) to run them.
We've judged these Python online courses across various parameters, like their pricing plans, the simplicity of their tutorials, the quality of learning support they offered, and what user level they were aimed at. We also evaluated the pace of the courses, the number of learning resources they had, and whether provided useful features like subtitles.
So whether you are new to Python or to programming itself, here are some of the best Python online courses to help you get to grips with the language.
We've also featured the best laptop for programmers.
Skillshare offers several Python tutorials aimed at beginners, but very few are as comprehensive as "Programming in Python for Beginners". The Instructor has designed the course with the assumption that the students have absolutely no clue about programming. He’ll help you get started by setting up your Python development environment in Windows, before explaining all the basic constructs in the language and when to use them.
The course is made up of over 70 lessons for a total runtime of over 11 hours. The lessons will help you learn how the various arithmetic, logical and relational operators work and understand when to use lists, collections, tuples, dictionaries. The primer on functions is pretty usable as it shows you how to avoid common mistakes. The course also touches on some advanced courses like measuring the performance of your code to help write efficient code. There’s an exercise after every few lessons that’ll challenge you to put the newly acquired skills to solve a problem.
Note however that the Polish instructor has an accent, which didn’t bother us but your mileage may vary. Plus we liked the instructor’s engaging diction that made the course really interesting. He also actively engages with students in the discussions page on the course to clarify any doubts and share feedback on the exercises.
In terms of delivery, SkillShare has a rather vanilla player as compared to some of its peers. It does supply you the ability to alter the play speed and add notes, but the lack of support for closed captions is disappointing. SkillShare offers a Free trial during which you can take any course in their library including this one.
Read our full SkillShare learning platform review.
Udemy offers a wide range of excellent courses, but their course, "The Python Mega Course: Build 10 Real World Applications", will be especially good for those who know some Python already. As its name suggests, the course teaches you how to build 10 practical apps using Python, from simple database query apps to web and desktop apps to data visualization dashboard, and more.
The instructor uses the Visual Studio Code IDE in the course that has over 250 videos divided into 33 sections. The first 8 sections cover the fundamentals of Python and another four cover advanced courses before you get to coding the 10 examples in the remainder of the course.
Many of the example apps are preceded by a section or two that teach the crucial elements in the example. For instance, before you build a desktop database app, you’ll learn how to use the Tkinter library to build GUIs and also how Python interacts with databases, particularly, SQLite, PostgreSQL and MySQL. The video lessons are supplemented by coding exercises and quizzes, and there’s also a Q&A section to post your questions to the instructor.
You can pay for the course once on Udemy to get lifetime access. The instructor regularly updates the course and once you’ve bought the course you’ll get these modifications for free. The learning experience is further enhanced by Udemy’s player, which is one of the best in the game. In addition to altering the playback speed, it’ll help you place bookmarks in the lectures.
To help you find areas of interest, it’ll also display popular locations bookmarked by other students. You also get closed captions in over a dozen languages and can even view an auto-scrolling transcript of the lessons. Furthermore, Udemy’s smartphone app has the option to obtain a lesson to the device for offline viewing.
Read our full Udemy learning platform review.
LinkedIn Learning offers a great range of professional development courses, and the course, "Advance your career with Python", is no different.
This course is designed for someone who has limited time and it’s ideal for you if you want a fast paced introduction to Python. The instructor uses the Anaconda distribution of Python and writes code in Jupyter Notebook. She doesn’t skip over any of the building blocks of the language and her lessons are nicely paced and well illustrated.
The good thing about the course is that instead of straightaway diving into coding a construct, which many fast-paced introductory courses do, the instructor begins each lesson by explaining the construct and its use. The course ends with a quick introduction to object-oriented programming.
LinkedIn Learning’s video player supports closed captions and you can also get a transcript for the course that you can use to jump into the lecture. The service also offers a free 1-month trial, which should be more than enough to take this course.
Read our full LinkedIn Learning review.
Coursera is another of our favorite online learning resources, and their "Principles of Computing" is a good course to expand your coding skills with Python. It's presented in two-parts and is offered by Rice University as part of the Fundamentals of Computing Specialization, which has a total of seven courses. The courses divide the lessons across several weeks, each of which has multiple video lectures, readings, practice exercises, homework quizzes, and assignments.
They are conducted by three Computer Science faculty members of Rice University and will upgrade your basic Python skills to help you think like a computer scientist. The courses introduce mathematical and computational principles, and how you can integrate them to solve complex problems, to enable you to write good code.
Coursera has a nice video player that offers closed captions and transcripts. You can also take notes at any point during the video lecture. Best of all you can obtain the video lectures in MP4 format as well as the subtitles and transcripts for offline viewing. You can audit the courses for free or earn a specialization certificate by subscribing to the service.
Read our full Coursera learning platform review.
edX provides an excellent range of free-to-access courses, and their "Analyzing Data with Python" course could be a great way for those with some Python coding skills to really break out into the wider field of data science.
This course equips you with all the skills you need to crunch raw data into meaningful information using Python, and will familiarize you with Python’s data analysis libraries including Pandas, NumPy, SciPy, and scikit-learn.
The self-paced course is divided into five modules with the sixth being the final assignment. Each module begins with a summary of the concepts that it’ll impart before it introduces the libraries and how they’re used to achieve the specified objective. There are quizzes and lab exercises to help you put the newly acquired knowledge to use.
The videos have closed captions as well as English transcripts that you can use to jump into the video. The course is conducted by IBM and requires you to put in 2-4 hours a week for five weeks. You can get a Verified certificate if you score over the specified minimum marks for the various exercises and quizzes.
Read our full edX learning platform review.
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Python online courses are educational programs that teach users about Python, a high-level programming language.
Python is not too difficult to learn and is generally used to develop websites and software, among other things.
When deciding which of the best online Python course to use, first consider what level of competency you are currently at. If you've not learned Python and you've little experience with other programming languages then it would definitely be recommended to start with the beginner courses, as these will break you into the basics you'll need before you cover more advanced tools.
However, if you already have a decent amount of programming experience, especially with Python, then feel free to try your hand with the more advanced courses.
To test for the best online Python courses we searched for a range of popular options as well as took recommendations from people we know who are learning Python or who are already competent with it. Then we followed the tutorials to get an idea of how easy they were to follow, how easy it was to learn essential tools and processes, and additionally what sort of user level the courses were aimed at, such as beginner, medium, or advanced-level users.
More online programming courses:
Apps are getting smarter, which is affecting what developers do and how they do it. While programmers don’t have to be AI experts to include intelligent elements in their app, they should understand something about what they’re building into their app and why.
For example, if you’re trying to Boost a shopping experience or the stickiness of a content site, you’ll likely use a recommendation engine. If you’re building a social app, an agriculture app, or a pet monitoring app, image recognition may make sense. If real-time context is important, such as for search or marketing purposes, location recognition may be worth considering. And regardless of what you’re building, you’ll probably add a conversational interface sooner or later.
The use cases and the opportunities for including AI in mobile apps are practically limitless, but it’s wise to understand the limitations of what you’re using, including how it will affect app performance and user experience.
AI is not one thing
AI often is used interchangeably with other terms including machine learning, deep learning, and cognitive computing, which can be confusing for those who haven’t yet taken the time to understand their differences. Others, such as technology analyst, advisor, and architect Janakiram MSV view the terms more narrowly. In a workshop at the latest Interop conference, he explained the various types of AI and their associations as follows:
For the purpose of this article, AI is used as an umbrella term.
While it’s not necessary for developers to become data scientists to take advantage of AI, they should familiarize themselves with the basics so they can use AI and remediate issues more effectively.
“AI that is integral to new mobile experiences, such as voice-based assistants and location-based services, increasingly require mobile developers to have a rudimentary understanding of AI to be effective,” said Vinodh Swaminathan, principal, Intelligent Automation, Cognitive & AI at professional services firm KPMG. “AI platform providers are increasingly packing a lot of developer-friendly features and architectures [into their products] that take the burden of knowing AI off developers.”
What developers should know
Given the shortage of data science talent, it’s no surprise that there is a growing body of easier-to-use frameworks and platforms, as well as Alexa Skills, APIs and reusable models. Simplicity does not alleviate the need for thought, however.
Rather than just grabbing a machine learning model and plugging it into an application, for example, developers should understand how the model applies to the particular application or use case. According to Swaminathan, that may require getting a better sense of what data was used to train the model and what levers are available to further refine the pre-trained model to Boost its effectiveness and performance.
Most developers haven’t been trained to think in terms of models and algorithms, however. They’ve been taught how to code.
“Mobile applications have been about user experience and not so much about how you make the application more intelligent. It’s only recently that chatbots and intelligent components have started to get exposure,” said Dmitri Tcherevik, CTO of cognitive-first business application development platform provider Progress Software. “If you want to do simple object or image recognition or speech processing, or if you want to build a simple chatbot, there are many tools from which to choose.”
Like anything else, though, what’s available off the shelf may not be exactly what your application requires. Specialized use cases tend to require specialized models and algorithms to yield the desired results. While specialized models and algorithms may also be available off-the-shelf, they may also need some fine-tuning or modification to deliver the value the app is intended to provide.
“If you’re building an end-to-end application, you need to know how to collect data, how to store data, how to move data stored in the cloud, how to clean data, and extract the features to make that suitable for algorithm training and model training,” said Tcherevik.
Data scientists know how to do all of that, but most developers do not. Given that just about everything is data-driven these days, including AI, it’s wise for developers to learn something about working with data, how machines learn, commonly used statistical techniques and the associated ethical issues, all of which are typically included in an introductory data science course or book.
“Depending on the application, there may be liability issues if [the] machine learning is not properly trained and makes a wrong decision,” said Tom Coughlin, IEEE senior member and president of data storage consulting firm Coughlin Associates. “A developer should test the application under all edge conditions to try and find such issues before release, or create some type of fail-safe that can avoid dangerous situations, if the application will be used for mission-critical applications.”
An important thing to understand when working with AI is that things are not static. For example, if a dataset changes, then the model using that dataset will need to be modified or retrained.
“Developers need to understand that the AI is only as good as its model and training. Without constant feedback and input, an AI model can become something else entirely,” said Pam Wickerham, director of Solutions Development at contract management platform SpringCM. “A trained model is never done and will always evolve and change. As an app gets smarter, it’s important to remember that it can evolve to [include] bias. It’s important to have a large trial set and review the feedback and training loop constantly to be sure the focus doesn’t become narrow or go in [an unintended] direction.”
Nir Bar-Lev, co-founder and CEO of deep learning computer vision platform Allegro.ai, thinks developers should understand how the fundamental nature of coding and AI differ. Namely, that with standard code, answers are deterministic and with AI they are statistical.
“AI delivers a prediction answer on a given question with a corresponding statistical score,” said Bar-Lev. “Each score is also a product of the specific API, the specific question, the real ‘noise’ in the environment and the version of the API.”
Why APIs alone aren’t enough
Adding intelligence to a mobile app isn’t necessarily as easy as calling an API because some refinement may be necessary to suit the app and use case. Like anything else, new capabilities should not be added to an app simply because it seems fashionable to do so.
“The more your application requires specific domain knowledge, the less you can rely on AI APIs available today as solutions for your needs,” said Bar-Lev. “AI is a learning paradigm. [The] conventional best practice rule of thumb is that the more data there is, the more accurate the results. However, it’s not only the quantity of data that’s important but also its specificity to the problem or use case the AI model is being asked to address. This means that the more specific or esoteric your domain area is, the less performance and quality should be expected from existing AI APIs.”
He also warns that since AI APIs are mostly cloud-based, connectivity and communications overhead need to be considered.
“AI, and specifically deep learning, are computationally heavy. As such, most of them are provided from the cloud, where there are abundant compute resources,” said Bar-Lev. “Obviously, developers need to consider cloud-based API calls for AI as they would any cloud-base actually making a local on-device call.”
There’s also the question of integrating AI into the SDLC to constantly monitor what’s working and what’s not.
“As with any new feature or new technology that supports the application, it’s important to pay attention to key lifecycle metrics. Why have we added AI? What are we trying to improve? How do we assess the effectiveness of other techniques?” said Pavel Veller, CTO of the Digital Engagement Practice on AI Competency Center and Capabilities at software development service provider EPAM. “It’s easier [to answer those questions] for business processes that have a clear funnel to optimize and evaluate, such as eCommerce, and more difficult for others that focus on engagement. The key, though, remains in identifying KPIs and putting continuous improvement in place.”
As mobile developers know, many factors can impact the effectiveness of an app. AI adds another layer of complexity that needs to be understood, alone and in relation to the mobile app. Getting things “right” is a continuous process of experimentation, measurement, monitoring, and improvement to ensure that the new capabilities not only work, but continue to work.
“Having dedicated AI/data science/machine learning resources focused on this loop is important to ensure you are successful,” said Antonio Alegria, head of Artificial Intelligence at enterprise software provider Outsystems. ‘If you don’t have the resources for this, set up the automated measurement loop and try to make your processes very lean to retrain, test and deploy models. Then, manage your effort in a way that you can keep an eye on monitoring and improving this periodically.”
Developers also need to be aware of IP leaks because with every API call, they risk sharing their core IP with the API provider.
Be mindful of resources
As always, mobile apps need to be developed with resource utilization in mind. What one can do with GPU clusters in the cloud is not the same thing one can accomplish on a mobile phone. Therefore, developers need to consider how the models and algorithms they intend to use would affect resources including battery power and memory usage.
“Often, there’s not a clear path to getting something working quickly on mobile device with the prepackaged libraries such as Apple’s Core ML and Google TensorFlow Lite,” said independent developer Kevin Bjorke. “If I have all of the resources and time I need because the client will pay for it, it’s [a different scenario] than when it has to run inside the Samsung phone. You want to do stuff and execute stuff that doesn’t use all the memory on the phone.”
According to Progress’ Tcherevik, resource utilization is just another parameter that developers should be monitoring.
“It goes back to the developer’s continuous deployment workflow,” said Tcherevik. “Establish a feedback loop, and have a process and culture of continuous evaluation and improvement. Things don’t change solely because of technology. A change in consumer behavior or a market trend can make as negative an impact as wrongly implemented AI.”
Sooner or later something will break or discontinue working as intended, which is another reason why developers need to understand the basics of what it is they’re adding to an app.
“You need to have some knowledge of how the thing that you just put together is working or how it could be made to run faster, because if it has to run on mobile, it needs to be thermally responsible,” said Bjorke. “In production, you really care about how much it costs to generate just this piece of information.”
Know where the data came from
Typically, there’s no shortage of data; however, the existence of data does not mean that the data is available, clean, reliable or can be used as intended.
Data availability, even within a company, can be an issue if the data owner refuses to share the data. In other contexts, the availability of the data may hinge on its intended use, the party using it, the purpose for which its being used and whether payment is required.
Also, data cleanliness is a huge issue. For example, a typical company will have multiple customer records for a single individual, all of which differ slightly. The problem is caused by the field discrepancies in various systems, manual entry errors, corrupt databases, etc. Meanwhile, companies are amassing huge volumes of data that are growing even faster with the addition of streaming IoT data. In many cases, data is stored without regard for its cleanliness, which is one reason data scientists spend so much time cleaning data. While there are tools that help speed and simplify the process, data cleanliness tends to evade the minds of those who have not been taught to think critically about the data they intend to use.
If the data isn’t clean, it isn’t reliable.
Even if the data is available, clean and reliable, it may be that its use is restricted in certain ways or in certain contexts. Personally identifiable information (PII) is a good example of this. It may be fine to use certain information in the aggregate as applied to a population but not an individual. Regulations, terms of service and corporate governance policies cover such information use. However, just because certain types of information can’t be used doesn’t mean that the same information cannot be inferred using data points that have been deemed acceptable to use for other purposes. For example, income level can be inferred from zip code, financial transactions and even the words used in social media posts. Typically, several data points are triangulated to infer a missing piece of data, which can raise ethical issues if not liability issues.
Sadly, the degree to which companies understand their own data can be questionable, let alone third-party data. Ergo, it’s bad practice to just grab data and use it without regard for its origin.
“There’s very little or zero discussion of where data came from, said Bjorke. “Developers are trained to program, so they understand less about data collection.”
Today’s developers are feeling the pressure to embed some sort of intelligence into their mobile apps, whether it’s for competitive reasons or because their companies want to provide new types of value to customers.
“Instead of hand-rolling code, developers should try to leverage the major platform providers such as IBM Watson, Google, etc. [because] they provide easy on-ramps, good economics and great features,” said KPMG’s Swaminathan. “Investing in skills that can ‘explain’ AI will also be important as there will likely be some regulatory scrutiny. Mobile app developers should be able to demonstrate that they not only understand how their AI works, but that they have ‘control’ over it.”
Meanwhile, development managers should accommodate AI development with specific steps in the development lifecycle such as training data curation, corpus management, improvement and refinement of the training models, he said.
There are a lot of resources available now that provide solid overviews of AI, the different types of AI, data science and working with data. While most working professionals are unable attend on-campus courses, there are many online offerings from vendors and highly reputable universities including MIT, many of which are available free of charge.
“Although better tools are lowering the barrier of entry, especially in mobile, developers should have knowledge about how the techniques used work at a high level, how to evaluate the results, how to tune and iterate, how to structure data, etc.” said Outsystems’ Alegria. “Developers should also have the knowledge and experience in data manipulation [and] know how to best transform [the data] to leverage the AI algorithms. Additionally, developers should understand the end-to-end solution from data intake, transformation, algorithm setup, training and evaluation and then how it all maps to a production scenario.”
One misconception is that if machine learning is simply applied to data, magic happens. Also, some underestimate the amount of training data required for machine learning.
“Deploying AI models is still hard not from [the standpoint of] putting the model into production and using it in your app, but deploying all of the code and logic that ensure the data is transformed in the same way that the model saw during training time,” said Alegria. “This can be a bigger challenge in mobile because the stacks used in training and in production could be significantly different, and you might need to handle more than one implementation, such as IoS and Android, which is why doing things in the cloud is still a better option in a lot of cases.”
Of course, the best way to learn is through hands-on experience and observation. Like Agile development or technology pilots, it’s wise to start small and create something simple that tests the entire cycle, in this case, from data gathering to prediction in a production setting. Alegria recommends developers focus on iteration by picking a single metric to improve.
“Ensure you know what metric represents [AI] effectiveness and that you have a way to measure it. Also, ensure you are gathering the data on the AI element’s performance and that you set up your app to optimize getting at that data, such as through user feedback,” said Alegria. “Continuously measure the model performance, across important segments and set up alerts when the performance degrades. Be sure to keep an eye on the failed cases by looking at the data manually to learn if there are some common patterns you’re missing.”
There’s more to AI than may be apparent to the uninitiated, in other words. While the fundamentals are not difficult to grasp, and there are plenty of resources available for review, there’s also no substitute for hands-on experience.
Three machine learning tools to consider
Mobile developers ready to experiment with machine learning or deep learning should consider the following frameworks:
Apple Core ML is a machine learning foundation upon which domain-specific frameworks operate. The domain-specific frameworks include Vision (image analysis), Foundation (natural language processing) and GamePlayKit (which evaluates decision trees). Developers can use Core ML to add pre-trained machine learning models to their apps.
Caffe2 is a lightweight, modular deep-learning open-source framework contributed by Facebook. It allows users to experiment with deep learning and take advantage of the models and algorithms community members have contributed. Caffe2 comes with Python & C++ APIs so developers can prototype immediately and optimize later.
Google Tensorflow is available as two solutions for deploying machine learning applications on mobile and embedded devices. TensorFlow Lite is a modified version of TensorFlow Mobile that allows developers to build apps that have a smaller binary size, fewer dependencies and better performance. However, TensorFlow Lite is available as a developer preview, which does not cover all the use cases TensorFlow Mobile covers. TensorFlow Light also offers less functionality than TensorFlow Mobile. For production cases, developers should use TensorFlow Mobile.
Research seminar for doctoral and Master's students to listen to researchers from academia, industry, and government of research-related courses in civil and environmental engineering. Invited speakers will present latest research advances in fields of environmental engineering, geotechnical engineering, structural engineering and transportation engineering. Attendance is mandatory for doctoral and MS students with thesis option. Thesis requirements and research methods will be introduced in various talks.Computer Based Analysis of Structures (Formerly 14.503)
The course is an introduction to the finite element displacement method for framed structures. It identifies the basic steps involved in applying the displacement method that can be represented as computer procedures. The course covers the modeling and analysis of 2-dimensional and 3-dimensional structures, such as cable-stayed structures, arches, and space trusses, space frames, shear walls, and so on. The analysis is done for both static and dynamic loading. The study is done by using MATLAB, GTSTRUDL, and Mathcad software.Advanced Strength Of Material (Formerly 14/10.504)
Stress and strain at a point; curved beam theory, unsymmetrical bending, shear center, torsion of non-circular sections; theories of failure; selected courses in solid mechanics.Concrete Materials (Formerly 14.505)
This course introduces fundamental and advanced courses on the properties of concrete materials. Fundamental courses include the formation, structure, mechanical behavior, durability, fracture, and deterioration of concrete. Theoretical treatments on the deformation, fracture and deterioration of concrete are also addressed. Advanced courses include the electromagnetic properties of concrete, high performance concrete (HPC), high-strength concrete (HSC), fiber-reinforced concrete, other special concretes, and the green construction of concrete.
Pre-Req: 14.310 Engineering Materials.Practice of Structural Engineering (Formerly 14.508)
This course covers the practice of structural engineering as it deals with the design of structures such as buildings and bridges, the identification of loads, and design variables, and design detailing for concrete and steel structures. The emphasis will be placed on the use and interpretation of the ACI318-09, AISD and AASHTO codes and the GTSTRUDL software.Inspection and Monitoring of Civil Infrastructure (Formerly 14.511)
In this course, principles and applications of inspection and monitoring techniques for the condition assessment of aged/damaged/deteriorated civil infrastructure systems such as buildings, bridges, and pipelines, are introduced. Current nondestructive testing/evaluation (NDT/E) methods including optical, acoustic/ultrasonic, thermal, magnetic/electrical, radiographic, microwave/radar techniques are addressed with a consideration of their theoretical background. Wired and wireless structural health monitoring (SHM) systems for civil infrastructure are also covered. Applications using inspection and monitoring techniques are discussed with practical issues in each application.Structural Stability (Formerly 14.512)
This course provides a concise introduction to the principles and applications of structural stability for their practical use in the design of steel frame structures. Concepts of elastic and plastic theories are introduced. Stability problems of structural members including columns, beam-columns, rigid frames, and beams are studied. Approaches in evaluating stability problems, including energy and numerical methods, are also addressed.Cementitious Materials for Sustainable Concrete
This course is designed for introducing advanced courses in cement hydration chemistry, materials characterization and concrete sustainability. Advanced courses in chemistry of commonly used cementitious materials, micro-structure, mechanical properties, durability ad sustainability will be offered. Students will learn and practice to characterize and analyze the roles of chemical admixtures and supplementary cementitious materials in concrete property improvement. Chemical issues involved in the engineering behavior of concrete will be offered. A service-learning project about sustainable concrete will be provided. Emerging courses such as self-healing concrete, self-consolidating concrete, mart concrete, 3D concrete printing and ultra-high performance concrete will also be covered.
Pre-req: CIVE.3100 Engineering Materials, or CIVE.5050 Concrete Materials, or Permission of Instructor.Reliability Analysis (Formerly 14.521)
A review of the elementary principles of probability and statistics followed by advanced courses including decision analysis, Monte Carlo simulation, and system reliability. In-depth quantitative treatment in the modeling of engineering problems, evaluation of system reliability, and risk-benefit decision management.Geotechnical and Environmental Site Characterization (Formerly 14.527)
This course is designed to supply students a comprehensive understanding of various site investigation and site assessment technologies employed in geotechnical and environmental engineering. The course begins with introduction to site investigation planning and various geophysical methods including: seismic measurements, ground penetrating radar, electrical resistivity, electromagnetic conductivity, time domain reflectometry. Drilling methods for soil, gas and ground water sampling; decontamination procedures; and long term monitoring methods are studied. Emphasis in this course is placed on conventional and state-of-the-art in situ methods for geotechnical and environmental site characterization: standard penetration test, vane shear test, dilatometer test, pressuremeter test and cone penetration tests. Modern advances in cone penetrometer technology, instrumented with various sensors (capable of monitoring a wide range of physical and environmental parameters: load, pressure, sound, electrical resistivity, temperature, PH, oxidation reduction potential, chemical contaminants) are playing a major role in site characterization. Principles underlying these methods along with the interpretation of test data will be covered in detail. The course will also look into emerging technologies in the area of site characterization. (3-0)3Drilled Deep Foundations (Formerly 14.528)
Design and analyses of drilled deep foundations including: Deep foundations classification and historical perspective. Cost analysis of foundations. Construction methods and monitoring techniques. Static capacity and displacement analyses of a single drilled foundation and a group under vertical and lateral loads. Traditional and alternative load test methods - standards, construction, interpretation, and simulation. Integrity testing methods. Reliability based design using the Load and Resistance Factor design (LRFD) methodology application for drilled deep foundations.
Pre-req: CIVE.5310 Advanced Soil Mechanics, or Permission of Instructor.Engineering with Geosynthetics (Formerly 14.529)
Rigorous treatment in the mechanism and behavior of reinforced soil materials. Laboratory and insitu tests for determining the engineering properties of geosynthetics (geotextiles, geomembranes, geogrids and geocomposites). Design principles and examples of geosynthetics for separation, soil reinforcement and stabilization, filtration and drainage.Driven Deep Foundations (Formerly 14.530)
design and analyses of driven deep foundations including: Deep foundations classification and historical perspective. Effects of pile installation. Static capacity and settlement analysis of a single pile and a pile group under vertical loads. Insight of pile resistance including soil behavior and interfacial friction. Driven pile load test standards, construction, interpretation, and simulation. Dynamic analysis of driven piles, the wave equation analysis, dynamic measurements during driving and their interpretation. Reliability based design using the Load and Resistance Factor design (LRFD) methodology application for driven deep foundations.
Pre-req: CIVE.5310 Advanced Soil Mechanics, or Permission of Instructor.Advanced Soil Mechanics (Formerly 14.531)
Theories of soil mechanics and their application. Drained and undrained stress-strain and strength behavior of soils. Lateral earth pressures, bearing capacity, slope stability, seepage and consolidation. Lab and insitu testing.Theoretical & Numerical Methods in Soil Mechanics (Formerly 14.532)
Geotechnical practice employs computer programs that incorporate numerical methods to address problems of stability, settlement, deformation, and seepage. These methods are based on theoretical understanding of the behavior of soils, and correct use of commercial software requires that the engineer understand theoretical bases of the numerical algorithms and how they work. This course addresses the description of stress and strain in the context of geotechnical engineering and the basic concepts of numerical and computational methods, including discretization errors, computational procedures appropriate to different classes of problem, and numerical instability. It will then apply the insights to the three major problems of geotechnical analysis: settlement, stability, and fluid flow.
Pre-req: MATH 2360 Eng. Differential Equations, and CIVE 3300 Soil Mechanics.Advanced Foundation Engineering (Formerly 14.533)
Design and analysis of shallow foundations, excavations and retaining structures including: site exploration, bearing capacity and settlement theories, earth pressures, braced and unbraced excavations, rigid and flexible retaining structures, reinforced earth, dewatering methods and monitoring techniques.Soil Dynamics and Earthquake Engineering (Formerly 14.534)
This course addresses the dynamic properties of soils and basic mechanical theory of dynamic response. It will apply these results to analysis and design of dynamically loaded foundations. A basic understanding of earthquakes - where they occur, their quantitate description, how the complicated patterns of motions are captured by techniques such as the response spectrum, and how engineers design facilities to withstand earthquakes, will be addressed. In particular, the course will consider three courses of current professional and research interest: probabilistic seismic hazard analysis (PHSA), soil liquefaction, and seismically induced displacements. The emphasis will be on geotechnical issues, but some time will be devoted to structural considerations in earthquake resistant design.Soil Engineering (Formerly 14.536)
The study of soil as an engineering material, and its use in earth structures (e.g. dams, road embankments), flow control, and compacted fills. Stability of natural and man made slopes, soil reinforcement and stabilization.Experimental Soil Mechanics (Formerly 14.537)
Application of testing procedures to the evaluation of soil type and engineering properties. Testing for classification, permeability, consolidation, direct and triaxial shear and field parameters. The technical procedures are followed by data analysis, evaluation and presentation. Critical examination of standard testing procedures, evaluation of engineering parameters, error estimation and research devices.Soil Behavior
Study of the physico-chemical and mechanical behavior of soil. courses include: soil mineralogy, formation, composition, concepts of drained and undrained stress-strain and strength behavior, frozen soils.Ground Improvement (Formerly 14.539)
Design and construction methods for strengthening the properties and behavior of soils. Highway embankments, soil nailing, soil grouting, landslide investigation and mitigation, dynamic compaction, stone columns.Urban Transportation Planning (Formerly 14.540)
Objectives and procedures of the urban transportation planning process. Characteristics and current issues of urban transportation in the United States (both supply and demand). Techniques of analysis, prediction and evaluation of transportation system alternatives. Consideration of economic, environmental, ethical, social and safety impacts in the design and analysis of transportation systems.Advanced Highway Geometric Design
Development of the principals of modern roadway design while addressing context specific design requirements and constraints. courses will include guidelines for highway design, design and review of complex geometry, geometric design to address safety and operational concerns, multi-modal design for signalized and un-signalized intersections, complete streets design concepts, and superelevation. Course-work will also include principals to present transportation designs to the public, transportation advocates, and private clients.
Pre-req: CIVE.3400 Transportation Engineering, or Permission of Instructor.Traffic Engineering (Formerly 14.541)
Engineering principles for safe and efficient movement of goods and people on streets and highways, including aspects of (a) transportation planning; (b) geometric design; (c) traffic operations and control; (d) traffic safety, and; (e) management of transportation facilities. courses include: traffic stream characteristics; traffic engineering studies; capacity and level-of-service analysis; traffic control; simulation of traffic operations; accident studies; parking studies; environmental impacts.Hazardous Materials Transportation
Hazmat transportation, safety and security are a convergence of operations, policies and regulation, and planning and design. This course will address the multimodal operations, vessels, technologies, packaging and placarding involved in the safe and secure transportation of hazmat. Safety and security rules, regulations, emergency preparedness and response, industry initiatives and programs, and U.S. government agencies governing hazmat transportation will be included, as well as international impacts on hazmat transportation safety and security.Transportation Network Analysis (Formerly 14.542)
This course is to introduce engineering students to basic transportation network analysis skills. courses covered include fundamentals of linear and nonlinear programming, mathematical representations of transportation networks, various shortest path algorithms, deterministic user equilibrium traffic assignment, stochastic user equilibrium traffic assignment, dynamic traffic assignment, heuristic algorithms for solving traffic assignment problems, and transportation network design.
Pre-req: CIVE 3720 Civil Engineering Systems and CIVE 3400 Transportation Engineering.Traffic Principles for Intelligent Transportation Systems (Formerly 14.543)
The objective of this course is to introduce the student to the traffic principles that are pertinent for the planning, design and analysis of Intelligent Transportation Systems (ITS). The course is oriented toward students that come from different disciplines and who do not have previous background in traffic or transportation principles. It is designed as an introductory course that will enable the student to pursue more advanced courses in transportation systems subsequently.Transportation Economics and Project Evaluation (Formerly 14.544)
The course offers an overview of the fundamental principles of transportation economics. Emphasizes theory and applications concerning demand, supply and economics of transportation systems. Covers courses such as pricing, regulation and the evaluation of transportation services and projects. Prerequisites: Students should have knowledge of transportation systems and basic microeconomics.Public Transit Plan and Design (Formerly 14.545)
Planning and design of public transportation systems and their technical, operational and cost characteristics. Discussion of the impact of public transportation on urban development; the different transit modes, including regional and rapid rail transit (RRT), light rail transit (LRT), buses, and paratransit, and their relative role in urban transportation; planning, design, operation and performance of transit systems (service frequency and headways, speed, capacity, productivity, utilization); routes and networks; scheduling; terminal layout; innovative transit technologies and their feasibility.Pavement Design (Formerly 14.546)
Fundamentals of planning, design, construction and management of roadway and airport pavements. Introduction to the theory and the analytical techniques used in pavement engineering. Principal courses covered: pavement performance, analysis of traffic, pavement materials; evaluation of subgrade; flexible and rigid pavement structural analysis; reliabilitydesign; drainage evaluation; design of overlays; and pavement distresses.Airport Planning and Design (Formerly 14.547)
Planning and design of civil airports. Estimation of air travel demand. Aircraft characteristics related to design; payload, range, runway requirements. Analysis of wind data, runway orientation and obstruction free requirements. Airport configuration, aircraft operations, and capacity of airfield elements. Design of the terminal system, ground access system, and parking facilities.Traffic Management and Control (Formerly 14.548)
The course presents modern methods of traffic management, traffic control strategies and traffic control systems technology. Main courses covered, include: transportation systems management (TSM); traffic control systems technology; control concepts - urban and suburban streets; control and management concepts - freeways; control and management concepts - integrated systems; traveler information systems; system selection, design and implementation; systems management; ITS plans and programs. The course will also include exercises in the use and application of traffic simulation and optimization models such as: CORSIM, TRANSYT and MAXBAND/ MULTIBAND.Traffic Flow and Emerging Transportation Technologies (Formerly 14.549)
Traffic flow theories seek to describe through precise mathematical models (a) the interactions between vehicles and the roadway system and (b) the interactions among vehicles. This course covers both conventional human-driven vehicles and the emerging connected and automated vehicles. Such theories form the basis of the models and procedures used in design and operational analysis of streets and highways. In particular, the course examines the fundamental traffic flow characteristics and the flow-speed-density relationship, as well as time and space headway, string stability, traffic flow stability, popular analytical techniques for traffic stream modeling at both microscopic and macroscopic levels, shock wave analysis, and simulation modeling of traffic systems.
Pre-req: CIVE.3400 Transportation Engineering, or Permission of Instructor.Behavior of Structures (Formerly 14.550)
Classical and matrix methods of structural analysis applied to complex plane trusses. Elementary space truss analysis. Elementary model analysis through the use of influence lines for indeterminate structures. The digital computer and problem oriented languages as analytical tools.Advanced Steel Design (Formerly 14.551)
Elastic and plastic design of structural steel systems, residual stresses, local buckling, beam-columns, torsion and biaxial bending, composite steel-concrete members, load and resistance factor design.Design of Concrete Structures (Formerly 14.552)
The main objective of this course is to expand the students' knowledge and understanding of reinforced concrete behavior and design. Advanced courses at material, element, and system level are built on quick reviews of undergraduate level knowledge and are related to current design codes.Wood Structures (Formerly 14.553)
Review of properties of wood, lumber, glued laminated timber and structural-use panels. Review of design loads and their distribution in wood-frame buildings. Design of wood members in tension, compression and bending; and design of connections.Finite Element Analysis (Formerly 14.556)
Finite element theory and formulation, software applications, static and dynamic finite element analysis of structures and components.Structural Dynamics (Formerly 14.557)
Analysis of typical structures subjected to dynamic force or ground excitation using direct integration of equations of motion, modal analysis and approximate methods.Bridge Design (Formerly 14.558)
Analysis and design of modern bridges, using computer software for the 3-D modeling of trial bridges under dead and live loading and seismic excitation. AASHTO specifications are used for the design of superstructures and substructures (abutments, piers, and bearings) under group load combinations.Design of Masonry Structures (Formerly 14.559)
Fundamental characteristics of masonry construction. The nomenclature, properties, and material specifications associated with basic components of masonry. The behavior of masonry assemblages subjected to stresses and deformations. Design of un-reinforced and reinforced masonry structures in accordance with current codes.Physical Chemical Treatment Processes (Formerly 14.561)
Course provides a theoretical understanding of various chemical and physical unit operations, with direct application of these operations to the design and operation of water and wastewater treatment processes. courses include colloid destabilization, flocculation, softening, precipitation, neutralization, aeration and gas transfer, packed & tray towers, oxidation, disinfection, reverse osmosis, ultrafiltration, settlings, activated carbon adsorption, ion exchange, and filtration.Physical and Chemical Hydrology Geology (Formerly 14.562)
Well hydraulics for the analysis of groundwater movement. A review of the processes of diffusion, dispersion, sorption, and retardation as related to the fate and transport of organic contaminants in groundwater systems. Factors influencing multi-dimensional contaminant plume formation and migration are addressed. It is the goal of this course to provide environmental scientists and engineers with the technical skills required to understand groundwater hydrology and contaminant transport within aquifers. A term paper and professional presentation in class regarding a relevant subject is required.Hydrology & Hydraulics (Formerly 14.564)
This course utilizes engineering principles to quantitatively describe the movement of water in natural and manmade environmental systems. courses include: hydrologic cycle, steam flow and hydrographs, flood routing, watershed modeling, subsurface hydrology, and probability concepts in hydrology, hydraulic structures, flow in closed conduits, pumps, open channel flow, elements of storm and sanitary sewer design will be addressed.Environmental Applications and Implications of Nanomaterials
This course will cover (I) novel properties, synthesis, and characterization of nanomaterials; (II) environmental engineering applications of nanomaterials, with an emphasis on nano-enabled water and wastewater treatment technologies such as membrane processes, adsorption, photo-catalysis, and disinfection; and (III) Health and Environmental impacts of nanomaterials, focusing on potential mechanisms of biological uptake and toxicity.Environmental Aquatic Chemistry (Formerly 14.567)
This course provides environmental understanding of the principles of aquatic chemistry and equilibria as they apply to environmental systems including natural waters, wastewater and treated waters.Environmental Fate and Transport (Formerly 14.568)
The fate of contaminants in the environment is controlled by transport processes within a single medium and between media. The similarities in contaminant dispersion within air, surface water and groundwater will be emphasized. Interphase transport processes such as volatilization and adsorption will then be considered from an equilibrium perspective followed by the kinetics of mass transfer across environmental interfaces. A professional presentation of a select paper or group of paper concerning a course subject is required.Micropollutants in the Environment
This course focuses on the generation, fate and transformation, transport, and the impacts of micropollutants in the environment, with emphasis on soil and water matrices. courses will include nanomaterials and organic micropollutants such as pharmaceuticals, antimicrobials, illicit drugs, and personal care products. Course delivery will be a combination of lectures, experimental analysis, and discussions of assigned studying materials.Wastewater Treatment and Storm Water Management Systems (Formerly 14.570)
The era of massive subsidies for construction of sanitary sewers and centralized, publicly operated treatment works (POTWs) has passed. Non - point pollution from sources such as onsite disposal systems has become a major focus of concern in our efforts to protect and Boost ground and surface water quality. Much of the new construction in areas not already served by centralized collection and treatment must use the alternative technologies. This course is design oriented. The variously available technologies are studied in depth. Students evaluate various technologies as they may be applied to a complex problem for which information is available, and develop an optimum problem solution.Surface Water Quality Modeling (Formerly 14.571)
Theory and application of surface water quality modeling will be combined interactively throughout the course. Data from a stream will be utilized in order to bring a public domain model into operationMarine and Coastal Processes (Formerly 14.572)
This course focuses on the coastal dynamics of currents, tides, waves, wave morphology and their effects on beaches, estuaries, mixing and sediment transport/accretion processes. Generalized global aspects of atmospheric and hydrospheric interactions with ocean currents are also presented.Solid Waste Engineering (Formerly 14.573)
Characterization, handling and disposal of municipal, industrial and hazardous wastes. Technologies such as landfills, recycling, incineration and composting are examined. A term paper and professional presentation in class regarding a relevant subject is required.Groundwater Modeling (Formerly 14.575)
Groundwater Modeling is designed to present the student with fundamentals, both mathematical and intuitive, of analytic and numeric groundwater modeling. An introductory course in groundwater hydrology is a prerequisite for Groundwater Modeling, and the student should be familiar with IBM computers in running text editors and spreadsheets. The semester will start with basic analytic solutions and image theory to aid in the development of more complex numeric models. Emphasis will then switch to numeric ground water flow models (MODFLOW) and the use of particle tracking models (GWPATH) to simulate the movement of solutes in ground water. The numeric modeling process will focus on forming the problem description, selecting boundary conditions, assigning the model parameters, calibrating the model, and preparing the model report. Course courses include: Analytic Methods, Numeric Methods, Conceptual Model and Grid design, Boundary Conditions, Sources, and Sinks, and Particle Tracking.GIS Applications in Civil and Environmental Engineering (Formerly 14.576)
This course is to introduce students to the basic concepts of Geographic Information Systems (GIS) and GIS applications in Civil and Environmental Engineering. courses to be covered include GIS data and maps, queries, map digitization, data management, spatial analysis, network analysis, geocoding, coordination systems and map projections, editing. Examples related to transportation, environmental, geotechnical and structural engineering will be provided to help students better understand how to apply GIS in the real world and gain hands-on experience. This course will consist of lectures and computer work.Biological Wastewater Treatment (Formerly 14.578)
Course covers the theoretical and practical aspects of biological wastewater treatment operations. courses include kinetics of biological growth and substrate utilization, materials balance in chemostats and plug flow reactors, activated sludge process analysis and design, sedimentation and thickening, nitrification and denitrification, phosphorus removal, fixed-film processes analysis and design, anaerobic processes analysis and design, aerated lagoons and stabilization ponds, and natural treatment systems.Green and Sustainable Civil Engineering (Formerly 14.579)
This course focuses on various green and sustainable materials and technologies applicable to five areas of civil engineering: environmental engineering, water resources engineering, structural engineering, transportation engineering, and geotechnical engineering. This course also covers current green building laws and introduces fundamentals of entrepreneurship and patent/copyright laws.Engineering Systems Analysis (Formerly 14.581)
The course presents advanced methods of operations research, management science and economic analysis that are used in the design, planning and management of engineering systems. Main courses covered, include: the systems analysis methodology, optimization concepts, mathematical programming techniques, Network analysis and design, project planning and scheduling, decision analysis, queuing systems, simulation methods, economic evaluation. The examples and problems presented in the course illustrate how the analysis methods are used in a variety of systems applications, such as: civil engineering, environmental systems, transportation systems, construction management, water resources, urban development, etc.Transportation Safety (Formerly 14.585)
Transportation Safety goes beyond the accepted standards for highway design. Providing a safe and efficient transportation system for all users is the primary objective of federal, state, and local transportation agencies throughout the nation. This class addresses fundamentals of highway design and operation, human factors, accident investigation, vehicle characteristics and highway safety analysis.Hazardous Waste Site Remediation (Formerly 14.595)
This course focuses on the principles of hazardous waste site remediation (with an emphasis on organic contaminants) using physical, chemical or biological remediation technologies. Both established and emerging remediation technologies including: bioremediation, intrinsic remediation, soil vapor extraction (SVE), in situ air sparging (IAS), vacuum- enhanced recovery (VER), application of surfactants for enhanced in situ soil washing, hydraulic and pneumatic fracturing, electrokinetics, in situ reactive walls, phytoremediation, and in situ oxidation, will be addressed. A term paper and professional presentation in class regarding a relevant subject is required.Grad Industrial Exposure (Formerly 14.596)
There is currently no description available for this course.Special courses in Civil Engineering (Formerly 14.651)
Course content and credits to be arranged with instructor who agrees to direct the student.Civil Engineering Individual Project (Formerly 14.693)
There is currently no description available for this course.Supervised Teaching in Civil Engineering (Formerly 14.705)
There is currently no description available for this course.Masters Project in Civil Engineering (Formerly 14.733)
There is currently no description available for this course.Masters Project in Civil Engineering (Formerly 14.736)
There is currently no description available for this course.Master's Thesis-Civil Engineering (Formerly 14.741)
There is currently no description available for this course.Master's Thesis - Civil Engineering (Formerly 14.743)
There is currently no description available for this course.Master's Thesis - Civil Engineering (Formerly 14.746)
There is currently no description available for this course.Master's Thesis - Civil Engineering (Formerly 14.749)
There is currently no description available for this course.Doctoral Dissertation (Formerly 14.751)
There is currently no description available for this course.Independent Study in Civil Engineering (Formerly 14.752)
There is currently no description available for this course.Doctoral Dissertation (Formerly 14.753)
There is currently no description available for this course.Doctoral Dissertation/Civil Engineering (Formerly 14.756)
There is currently no description available for this course.Doctoral Dissertation (Formerly 14.757)
There is currently no description available for this course.Doctoral Dissertation (Formerly 14.759)
There is currently no description available for this course.Continued Graduate Research
There is currently no description available for this course.Continued Graduate Research (Formerly 14.763)
There is currently no description available for this course.Continued Graduate Research (Formerly 14.766)
There is currently no description available for this course.Continued Graduate Research (Formerly 14.769)
There is currently no description available for this course.Curricular Practical Training for Engineering Doctoral Candidates
Curricular Practical Training (CPT) is a training program for doctoral students in Engineering. Participation in CPT acknowledges that this an integral part of an established curriculum and directly related to the major area of study or thesis.
Developed in collaboration with leading global blue-chip employers, this course aims to create `the ideal graduate' whose skills cover the challenging middle ground between business and IT.
You will complete a full-year industrial placement after your second year.
Download our work placement guide to find out more.
The uniqueness of the course is recognised by more than 40 employers who maintain its quality and relevance to their sectors.
Accenture, BBC, BT, Credit Suisse, Deloitte, IBM, Unilever, and many other companies help to deliver the course through project work, student mentoring and a range of professional development activities.
Information Technology Management for Business is home to future leaders of the IT industry.
Together with your outstanding fellow students from around the world, you will learn the application of technology within the modern workplace across diverse industries.
Everything on the IT Management for Business course is focused on the application of practice, enabling you to understand the real-life challenges faced by industry.
No previous technology experience is required to join the course, only a passion for harnessing innovation and creativity to Boost the management of IT.
You will normally study four or five course units per semester.
Each week there are usually two hours of lectures for each course unit and a one-hour workshop in alternate weeks, although this varies slightly.
You are expected to double this in private study.
Group work and group or individual presentations will form a regular part of your assignments.
Essays, multiple choice tests, project reports and presentations, in-class tests, and weekly assignments constitute the coursework component of assessment, although the nature and proportion of coursework varies across course units.
The remainder of assessment is by unseen examination.
Depending on the degree course, in your final year you can choose to do a research-based dissertation or project.
We aim to strike a balance between examinations and assessed coursework as well as providing opportunities for feedback on progress through non-assessed work.
You take course units totalling 360 credits over the duration of your studies in order to graduate with Honours (120 credits in each year of study).
Generally, one-semester courses are worth 10 credits and full-year courses are worth 20 credits.
As your studies progress you have increasing flexibility in choosing courses which suit your personal interests and career aspirations.
Project work integrates business and IT throughout the degree.
Our current first-year project is supported by Credit Suisse and involves developing an application to solve a real business problem.
In addition to foundation-level course units in IT, you will also study marketing, economics, and work psychology.
By the end of your first year you will have presented your team project to employers at two employer showcases and participated in skills sessions delivered by companies such as KPMG, Fujitsu, and Bank of America.
The course unit details given below are subject to change, and are the latest example of the curriculum available on this course of study.
During your second year of study, you will undertake core course units in Business Analysis, Digital Strategy, User Experience Design, and Data Analytics.
What sets the ITMB course at Manchester apart is our ability to provide you with the opportunity to customise your programme of study to ensure that it fulfils a learning experience that meets your individual goals.
Our current second-year Integrative Team Project is supported by Procter and Gamble, where you will be tasked with the undertaking of a yearlong team consultancy project, the result of which will be displayed to employers at the end of both semesters.
The course unit details given below are subject to change, and are the latest example of the curriculum available on this course of study.
Industrial experience can be a significant component of the ITMB experience, offering real-world experience with innovative organisations.
Previous students have completed placements with Adobe, Bank of America, Credit Suisse, Deloitte, Hewlett Packard, IBM, Medallia, Microsoft, Morgan Stanley, PepsiCo, PwC, and Vodafone, among others.
Our students have worked in placement roles spanning business analysis, e-commerce, digital marketing, and project management.
Your final year provides an in-depth view of big data and business analytics, IT risk, and architecture.
You will gain practical skills in the design and application of business IT architectures through a core unit developed with IBM, which applies a case study from the global technology giant.
You will also undertake your own final-year research project - the development of an IT solution to a business problem which will encompass investigation, requirement analysis, design, and evaluation of your proposed solution.
Previous ITMB students have studied a variety of courses including how large corporations use technology to manage teams across geographic locations and time zones, IT provision in the treatment of diabetes in the NHS, and the implications of the rise in social networking on management.
The course unit details given below are subject to change, and are the latest example of the curriculum available on this course of study.
The distinctive feature of this group of courses is the strategic involvement of world-class firms within the IT sector that partner with Alliance Manchester Business School to provide input to the course, in the form of prestigious `guru' lectures, real business problems or projects, and mentoring.
The degree has been introduced and directly supported by business and commercial input.
Hear from current ITMB students over on our YouTube channel.
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