The MSc Business Analytics is a one-year specialist programme for graduates with a bachelor's degree with a substantial quantitative component, and highly qualified graduates from other backgrounds with demonstrable advanced quantitative skills. It will suit graduates or early career professionals who wish to pursue a career in business analytics across sectors such as digital marketing, human resources, logistics, retail, finance, banking, insurance, and healthcare.
The MSc Business Analytics has been created in partnership with industry professionals from IBM, LV & UCL/IBM Industry Exchange Network. It provides students with the opportunity to work on business analytics projects and provide data-driven solutions to a real business problem or challenge, where possible, in partnership with IBM and other private, charity, and public sector organisations.
Students will gain a critical understanding of organisational, societal, and ethical issues in the use of Business Analytics. These issues are crucial for many organisations that seek to provide data-driven services while trying to balance innovation and competitiveness with public trust and corporate social responsibility. Examples of projects include optimisation of resource allocation, people analytics to support hiring decisions, customer segmentation, and sentiment analysis to Strengthen a business strategic direction.
At the end of the programme, students will have learned technical skills in data preparation (such as identification, extraction, and cleaning of data); the use of statistical and machine learning techniques to perform data mining and predictive analytics; the formulation and execution of statistical and mathematical models to optimise complex business decisions; the visualisation, interpretation, and reporting/communication of results from statistical analysis. Students will learn how to perform ad-hoc data analytics in Python and through specialised software such as IBM SPSS Modeler and IBM CPLEX.
You will be taught by leading academics whose research tackles the major issues in business analytics. 88% of our Business and Management research is rated as world leading or internationally excellent (REF 2021), reflecting its impact in shaping policy and practice. Bristol is a vibrant, ambitious and entrepreneurial city and home to SETSquared, the world's top university business incubator (UBI Global).
Published: Published Date - 07:20 PM, Fri - 14 October 22
Warangal: Vaagdevi College of Engineering has organised an industry interaction meeting with IBM Career Education Software Group-India/South Asia at their campus here on Friday in an attempt to prepare the next generation of IT professionals for careers in Artificial Intelligence, Machine Learning, Big Data, Electric Vehicles, etc.
Delivering a presentation at the programme on AI, ML, Big-data, Analytics, Blockchain, Cloud, and Cybersecurity tools, IBM Regional Manager, RD Madhusudhana Rao said the IBM Career Education Programme aids students in developing skills in a variety of cutting-edge emerging technologies, including Artificial Intelligence/Machine Learning, Analytics, Blockchain, Cloud, Cybersecurity, etc.
“I hope IBM collaborates at various levels, whether it be to co-create learning paths, develop software skills, build capabilities, or engage in experiential learning, to customize offerings to ensure the best outcomes. IBM provides a cutting-edge educational platform with the most accurate software content, real-world business knowledge, practical labs, and best practices,” he said.
“In this process, the Industry Institute Interaction Cell (IIIC) is playing a pivotal role in addressing this skill gap need, and we are excited to partner with the leading engineering schools to address the burgeoning skills gap issue faced by the IT sector,” Rao said. Principal Dr K Prakash has given his observations on the programme.
IBM Partners, Vice-Principal Dr Thirupathy Rao, Dean Administration Dr Shishidhar, Industry Institute Interaction Cell (IIIC) Coordinator Professor Chintakindi Raju, senior faculty, and IIIC members attended the programme.
Understanding complex events in today’s business world is critical. The explosion of data analytics—and the desire for insights and knowledge—represents both an opportunity and a challenge.
At the center of this effort is predictive analytics. The ability to transform raw data into insights and make informed business decisions is crucial. Today, predictive analytics plays a role in almost every corner of the enterprise, from finance and marketing to operations and cybersecurity. It involves pulling data from legacy databases, data lakes, clouds, social media sites, point of sale terminals and IoT on the edge.
It’s critical to select a predictive analytics platform that generates actionable information. For example, financial institutions use predictive analytics to understand loan and credit card applications, and even grant a line of credit on the spot. Operations departments use predictive analytics to understand maintenance and repairs for equipment and vehicles, and marketing and sales use it to gauge interest in a new product.
Top predictive analytics platforms deliver powerful tools for ingesting and exporting data, processing it and delivering reports and visualizations that guide enterprise decision-making. They also support more advanced capabilities, such as machine learning (ML), deep learning (DL), artificial intelligence (AI) and even digital twins. Many solutions also provide robust tools for sharing visualizations and reports.
Predictive models are designed to deliver insights that guide enterprise decision-making. Solutions incorporate techniques such as data mining, statistical analysis, machine learning and AI. They are typically used for optimizing marketing, sales and operations; improving profits and reducing costs; and reducing risks related to things like security and climate change.
Also see: Best Data Analytics Tools
Four major types of predictive models exist:
A regression model estimates the relationship between different variables to deliver information and insight about future scenarios and impacts. It is sometimes referred to as “what if” analysis.
For example, a food manufacturer might study how different ingredients impact quality and sales. A clothing manufacturer might analyze how different colors increase or decrease the likelihood of a purchase. Models can incorporate correlations (relationships) and causality (reasons).
These predictive models place data and information in categories based on past histories and historical knowledge. The data is labeled, and an algorithm learns the correlations. The model can then be updated with new data, as it arrives. These models are commonly used for fraud detection and to identify cybersecurity attacks.
A cluster model assembles data into groups, based on common attributes and characteristics. It often spots hidden patterns in systems. In a factory, this might mean spotting misplaced supplies and equipment and then using the data to predict where it will be during a typical workday. In retail, a store might send out marketing materials to a specific group, based on a combination of factors such as income, occupation and distance.
As the name implies, a time-series model looks at data during a given period, such as a day, month or year. Using predictive analytics, it’s possible to estimate what the trend will be in an upcoming period. It can be combined with other methods to understand underlying factors. For instance, a healthcare system might predict when the flu will peak—based on past and current time-series models.
In more advanced scenarios, predictive analytics also uses deep learning techniques that mimic the human brain through artificial neural networks. These methods may incorporate video, audio, text and other forms of unstructured data. For instance, voice recognition or facial recognition might analyze the tone or expression a person displays, and a system can then respond accordingly.
Also see: Top Business Intelligence Software
All major predictive analytics platforms are capable of producing valuable insights. It’s important to conduct a thorough internal review and understand what platform or platforms are the best fit for an enterprise. All predictive analytics solutions generate reports, charts, graphics and dashboards. The data must also be embedded into automation processes that are driven by other enterprise applications, such as an ERP or CRM system.
An internal evaluation should include the types of predictive analytics you need and what you want to do with the data. It should also include what type of user—business analyst or data scientist—will use the platform. This typically requires a detailed discussion with different business and technical groups to determine what types of analytics, models, insights and automations are needed and how they will be used.
The capabilities of today’s predictive analytics platforms is impressive—and companies add features and capabilities all the time. It’s critical to review your requirements and find an excellent match. This includes more than simply extracting value from your data. It’s important to review a vendor’s roadmap, its commitment to updates and security, and what quality assurance standards it has in place. Other factors include mobile support and scalability, Internet of Things (IoT) functionality, APIs to connect data with partners and others in a supply chain, and training requirements to get everyone up to speed.
Critical factors for vendor selection include support for required data formats, strong data import and export capabilities, cleansing features, templates, workflows and embedded analytics capabilities. The latter is critical because predictive analytics data is typically used across applications, websites and companies.
A credit card check, for example, must pull data from a credit bureau but also internal and other partner systems. The APIs that run the task are critical. But there are other things to focus on, including the user interface (UI) and usability (UX). This extends to visual dashboards that staff uses to view data and understand events. It’s also vital to look at licensing costs and the level of support a vendor delivers. This might include online resources and communities as well as direct support.
Here are 10 of the top predictive analytics solutions:
Key Insight: The drag-and-drop platform is adept at generating rich visualizations and excellent dashboards. It ranks high on flexibility, with support for almost any type of desired chart or graphics. It can generate valuable data for predictive analytics by connecting to numerous other data sources, including Microsoft SQL and Excel, Snowflake, SAP HANA, Salesforce and Amazon Redshift. It also includes powerful tools for selecting parameters, filtering data, building data-driven workflows and obtaining results. While the platform is part of Google Cloud and it is optimized for use within this environment, it supports custom applications.
Key Insight: The predictive analytics platform is designed to put statistical data to work across a wide array of industries and use cases. It includes powerful data ingestion capabilities, ad hoc reporting, predictive analytics, hypothesis analysis, statistical and geospatial analysis and 30 base machine learning components. IBM SPSS Modeler includes a rich set of tools and features, accessible through sophisticated dashboards.
Key Insight: Qlik Sense is a cloud-native platform designed specifically for business intelligence and predictive analytics. It delivers a robust set of features and capabilities, available through dashboards and visualizations. Qlik Sense includes AI and machine learning components; embedded analytics that can be used for websites, business applications and commercial software; and strong support for mobile devices. The solution supports hundreds of data types, and a broad array of analytics use cases.
Key Insight: The widely used CRM and sales automation platform includes powerful analytics and business intelligence tools, including features driven by the company’s AI-focused Einstein Analytics. It delivers insights and suggestions through specialized AI agents. A centralized Salesforce dashboard offers charts, graphs and other insights, along with robust reporting capabilities. There’s also deep integration with the Tableau analytics platform, which is owned by Salesforce.
Key Insight: Formerly known as BusinessObjects for cloud, SAP Analytics Cloud can pull data from a broad array of sources and platforms. It is adept at data discovery, compilation, ad-hoc reporting and predictive analytics. It includes machine learning and AI functions that can guide data analysis and aid in modeling and planning. A dashboard offers flexible options for displaying analytics data in numerous ways.
Key Insight: The low-code cloud solution is designed to serve as a “single application for reporting, data exploration and analytics.” It imports data from numerous sources; supports rich dashboards and visualizations, with strong drill-down features; includes augmented analytics and ML; and includes robust collaboration and data sharing features. The platform also includes natural language chatbots that aid business users and other non-data scientists in content creation and management.
Key Insight: The enormous popularity of Tableau is based on the platform’s powerful features and its ability to generate a wide range of appealing and useful charts, graphs, maps and other visualizations through highly interactive real-time dashboards. The platform offers support for numerous predictive analytics frameworks, including regression models, classification models, clustering models, time-series models. Non data scientists typically find its user interface accommodating and easy-to-learn.
Key Insight: Teradata Vantage delivers powerful predictive analytics capabilities, including the ability to use data from both on-premises legacy hardware sources and multicloud public frameworks, including AWS, Azure and Google Cloud. The solution also works across virtually any data source, including data lakes and data warehouses. It supports sophisticated AI and ML functionality and includes no-code and low-code drag and drop components for building models and visuals. The company is especially known for its fraud prevention analytics tools, though it offers numerous other predictive tools and capabilities.
Key Insight: TIBCO Spotfire offers a powerful platform for performing predictive analytics. It connects to numerous data sources and includes real-time feeds that can be highly filtered and customized. The solution is designed for both business users and data scientists. It includes rich visualizations and supports customizations through R and Python.
Key Insight: The analytics cloud delivers highly flexible self-service predictive analytics. The query engine is designed to search on virtually any data format and understand complex table structures over billions of rows. It offers powerful search and filtering capabilities that extend to natural language queries. ThoughtSpot also provides a powerful processing engine that generates a range of visualizations, including social media intelligence. The solution includes a machine learning component that ranks the relevancy of results.
Also see: Top Cloud Companies
|Looker||Excellent drag-and-drop interface with appealing visualizations and a high level of flexibility||Support is almost entirely online|
|IBM||SPSS Modeler||Excellent open-source extensibility; strong drag-and-drop functionality||Not as user friendly as other analytics solutions|
|Qlik||Qlik Sense||Powerful and versatile platform with strong modeling features||Some complaints about customer support|
|Salesforce||Salesforce||Highly customizable; strong integration with other platforms and data sources||Expensive|
|SAP||Analytics Cloud||Supports extremely large datasets and has powerful capabilities||Visualizations are not always as appealing as other platforms|
|SAS||Visual Analytics||High performance platform that supports numerous data types and visualizations||May require customization|
|Tableau||Tableau Desktop||Outstanding UI and UX, with deep Salesforce/CRM integration||Can drain CPUs and RAM|
|TIBCO||Spotfire||Powerful predictive analytics platform with excellent visual output||Steep learning curve|
|Teradata||Advantage||Highly flexible with a powerful SQL engine; generates rich visualizations||Some users complain about an aging UI.|
|ThoughtSpot||ThoughtSpot||Flexible, powerful capabilities with a strong UI and UX||Visualizations sometimes lag behind competitors|
Submitted by IBM
WASHINGTON, October 14, 2022 /CSRwire/ - The Hispanic Heritage Foundation (HHF) announced today its collaboration with IBM (NYSE: IBM) which includes leveraging IBM SkillsBuild – a free education program that helps students and adult learners develop valuable new skills and access career opportunities in technology fields – by providing digital content, personalized mentoring, and the experiential learning they need to gain technical, critical thinking, and creative problem-solving skills. The program will be offered for FREE to HHF Network, is completely digital, and includes IBM-branded digital credentials that are recognized by the market to create direct pathways to tech jobs. The effort will be open to high school students, college students, young professionals, and adult learners.
“This IBM SkillsBuild collaboration has been a transformational goal of our tech pathways strategy and goal for years,” said Jose Antonio Tijerino, President, and CEO of HHF. “Our community has a tremendous value proposition for America’s workforce and through this innovative collaboration, America can benefit from the talent we have always had to offer. Our collective mission is to provide training and opportunities for our community to make an impact in the tech sector.
We are grateful to IBM for allowing us to leverage their expertise and pathways in preparing the Latinx community for jobs that desperately need to be filled. As Latinos, we’re ready as we always have been.”
The learning pathways available through IBM SkillsBuild include courses on workplace skills, such as communication and leadership skills designed for any beneficiary wishing to understand how to work in the digital world, as well as courses on data analytics, cybersecurity, cloud computing, and many other technical disciplines. The program will also help early school leavers and long-term unemployed to gain what is required to re-enter the workforce. Courses are available in English and Spanish, providing Hispanic learners with a better and deeper understanding of course materials, to help ensure completion and professional competency.
“As a Latina, I am very excited and honored to be partnering with the Hispanic Heritage Foundation to provide free education and career readiness resources to Hispanics nationwide,” said Claudia Cortes Romanelli, Director of Corporate Social Responsibility at IBM. “I see every day the great opportunity to invest in skilling the next generation of STEM talent from the Hispanic community. We look forward to working with HHF as part of our commitment to equitably skill 30 million people worldwide.”
The Hispanic Heritage Foundation award-winning LOFT (Latinos on Fast Track) program is a leadership and workforce development program and network with a focus on various sectors or “tracks,” including tech. HHF’s broad network and beyond will be exposed to IBM SkillsBuild to learn, and build skills in artificial intelligence, data science, cloud, security, information technology, and more, with opportunities for mentoring and networking in the tech space as well as earning certifications and placements into the workforce.
IBM and HHF’s collaboration is part of IBM’s commitment to equitably skill 30 million people globally by 2030.
About the Hispanic Heritage Foundation
HHF’s mission focuses on education, the workforce, identity, and social impact through the lens of leadership and culture. For more information, visit www.hispanicheritage.org and follow the Hispanic Heritage Foundation on Instagram, Facebook, Twitter, and TikTok
About IBM Education
As part of the company's Corporate Social Responsibility efforts, IBM's education portfolio takes a personalized, diverse, and deep approach to STEM career readiness. IBM's pro bono programs range from education and support for teens at public schools and universities to career readiness resources for aspiring professionals and job seekers. IBM believes that education is best achieved through the collaboration of the public, private, and not-for-profit sectors.
IBM SkillsBuild is a free education program focused on underrepresented communities, that helps adult learners, and high school and university students and faculty, develop valuable new skills and access career opportunities. The program includes an online platform that is complemented by customized practical learning experiences delivered in collaboration with a global network of partners. The online platform offers over 1,000 courses in 19 languages on cybersecurity, data analysis, cloud computing, and many other technical disciplines — as well as in workplace skills such as Design Thinking. Most importantly, participants can earn IBM-branded digital credentials recognized by the market. The customized practical learning experiences could include project-based learning, expert conversations with IBM volunteers and mentors, premium content, specialized support, connection with career opportunities, and access to IBM software. IBM SkillsBuild operates in 168 counties and has supported 2.2M learners.
Innovation – joining invention and insight to produce important, new value – is at the heart of what we are as a company. And, today, IBM is leading an evolution in corporate citizenship by contributing innovative solutions and strategies that will help transform and empower our global communities.
Our diverse and sustained programs support education, workforce development, arts and culture, and communities in need through targeted grants of technology and project funds. To learn more about our work in the context of IBM's broader corporate responsibility efforts, please visit Innovations in Corporate Responsibility.
More from IBM
A leading provider of Financial Performance Management solutions seeks a strong technical IBM Planning Analytics (TM1) Consultant/Developer to build TM1 cubes, dimensions and rules and setup ETL processes while also engaging with clients to understand their requirements. You will support the existing client base, as well as implement new solutions at new clients. The successful incumbent must at least have 1st-year tertiary level in Accounting and Information Systems or Computer Science. You will require 3+ years’ experience building TM1 models and dealt with clients, 2+ years’ experience & understanding of SQL fundamentals and able to write complex SQL queries and 2+ years’ Excel and VBA. You also need a strong understanding of the fundamentals of financial accounting, systems design and implementation.
While we would really like to respond to every application, should you not be contacted for this position within 10 working days please consider your application unsuccessful.
When applying for jobs, ensure that you have the minimum job requirements. OnlySA Citizens will be considered for this role. If you are not in the mentioned location of any of the jobs, please note your relocation plans in all applications for jobs and correspondence. Please e-mail a word copy of your CV to [Email Address Removed] and mention the reference numbers of the jobs. We have a list of jobs on [URL Removed] Datafin IT Recruitment – Cape Town Jobs.
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Market insights reports recently added a new report on Global Marketing Analytics Market, which an in-depth study is providing complete study of the industry for the period 2022 to 2027. It provides complete overview of Global Marketing Analytics Market industry as all the major industry trends, market subtleties and good set-up. Besides this, the report also provides key statistics on the Marketing Analytics Market status of the leading market players, key trends, and potential growth openings in the market. These study reports are planned with the goal to help the reader in favorable Strengthen data and make decisions that are helpful to grow their business.
The Marketing Analytics Market was valued at USD 2.13 billion in 2020 and is expected to reach USD 4.68 billion by 2027, at a CAGR of 14% over the forecast period (2022 – 2027) .
Click here to get the latest demo PDF copy of the report: –
The major players covered in the reports are:
– IBM Corporation
– Microsoft Corporation
– Oracle Corporation
– Salesforce.Com Inc.
– Accenture PLC
– Adobe Systems Incorporated
– SAS Institute Inc.
– Teradata Corporation
– Neustar, Inc.
– Pegasystems Inc.
– Tableau Software
– Google LLC
They said research study covers in-depth analysis of multiple market segments based on type, application, and studies different topographies. The report is also inclusive of competitive profiling of the leading Marketing Analytics Market product vendors, and their latest developments. This report has been segmented by type, by application and by geography and also includes the market size and forecast for all these segments. Compounded annual growth rates for all segments have also been provided for 2022 to 2027.
Browse the full report description and summary: –
Regional Analysis: –
Major regions covered in the report include North America, Asia Pacific, Europe, East & Africa, and South America. In addition, the report provides country level analysis for 25+ major countries including US, Germany, UK, Japan, China, India, UAE, South Korea, South Africa, and Brazil. Regional analysis provides regional as well as country level information about the market highlighting the dynamics of the market by various segments covered in the report.
Industry News and Updates:
In Jul 2017, Teradata announced the acquisition of StackIQ, a prominent developer of cloud analytics software, which has managed the deployment of cloud and analytics software at millions of servers in data centers around the world. The acquisition is expected to strengthen the R&D capabilities of the company. Further In Jun 2018, Microsoft signed a MoU with New Sales Wales to trial a major data science project based on procurement analytics.
Some of the key questions answered in this report:
Highlights of Global Marketing Analytics Market Report: –
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Sports Analytics Market is expected to witness market growth during the forecast period of 2022 to 2028. Market Insights Reports analyzes the market willingness to grow at a CAGR of 28.1% during the mentioned forecast period above.
Sports Analytics Market report 2022 includes top countries data, detailed overview of current market, it also delivers snapshot of key competition
Sports Analytics Market report includes growth strategy, upcoming trend, top key companies’ analysis, dynamics, drivers, restraints, challenges and future opportunities. This report also focuses on volume and value at the global level, regional level, and company level. From a global perspective, this report represents overall market size and share by analyzing historical data and future prospect. This report also includes coverage of various features and applications that may affect the business in the future.
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“Final Report will add the analysis of the impact of COVID-19 on this industry”
List Of Top Key Players in Sports Analytics Market Report Are – IBM, SAP SE, Oracle, SAS Institute, Tableau Software, Stats Perform, Prozone Sports, Opta Sports, Sportingmindz Technology, Trumedia Networks, Catapult, Exasol, TruMedia Networks, DataArt, Orreco, Quant4sport, Physimax, Qualitas Global, iSportsAnalysis, ICEBERG Sports Analytics
Key Industry Development:
June 2021 – Catapult Group acquired a U.K.-based sports data company, SBG Sports, for improved investment in data science, technology, and scale capability to boost its growth strategy.
This market study covers the global and regional market with an in-depth analysis of the overall growth prospects in the market. Furthermore, it sheds light on the comprehensive competitive landscape of the global market.
Sports Analytics market is segmented by type and by application. Key players, stakeholders, and other participants in the global Sports Analytics market will be able to gain the upper hand as they use the report as a powerful resource. The segmental analysis focuses on production capacity, revenue and forecast by Type and by Application for the period 2022-2028.
Sports Analytics Market Size by Types:
Sports Analytics Market Size by Applications:
Player Fitness and Safety
Player and Team Valuation
Purchase this Report at –
Sports Analytics Market report provides comprehensive analysis of:
Key Questions Answered in The Report:
Geographical Regions covered in Sports Analytics market report are:
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Sports Analytics Market TOC Covers the Following Points:
1 Study Coverage
1.1 Sports Analytics Product Introduction
1.2 Market by Type
1.2.1 Global Sports Analytics Market Size by Type, 2017 VS 2021 VS 2028
1.3 Market by Application
1.4 Study Objectives
1.5 Years Considered
2 Global Sports Analytics Production
2.1 Global Production Capacity (2022-2028)
2.2 Global Sports Analytics Production by Region: 2022 VS 2028
2.3 Global Sports Analytics Production by Region
2.3.1 Global Sports Analytics Historic Production by Region (2022-2028)
2.3.2 Global Sports Analytics Forecasted Production by Region (2022-2028)
2.4 North America
3 Global Sports Analytics Sales in Volume and Value Estimates and Forecasts
3.1 Global Sales Estimates and Forecasts 2022-2028
3.2 Global Revenue Estimates and Forecasts 2022-2028
3.3 Global Revenue by Region: 2022 VS 2028
3.4 Global Sales by Region
3.4.1 Global Sales by Region (2016-2022)
3.4.2 Global Sales by Region (2022-2028)
3.5 Global Revenue by Region
3.5.1 Global Revenue by Region (2022-2028)
3.5.2 Global Revenue by Region (2022-2028)
3.6 North America
3.9 Latin America
3.10 Middle East and Africa
4 Competition by Manufactures
4.1 Global Production Capacity by Manufacturers
4.2 Global Sales by Manufacturers
4.2.1 Global Sales by Manufacturers (2022-2028)
4.2.2 Global Sales Market Share by Manufacturers (2022-2028)
4.2.3 Global Top 10 and Top 5 Largest Manufacturers of Sports Analytics in 2022
4.3 Global Revenue by Manufacturers
4.3.1 Global Revenue by Manufacturers (2022-2028)
4.3.2 Global Revenue Market Share by Manufacturers (2022-2028)
4.3.3 Global Top 10 and Top 5 Companies by Sports Analytics Revenue in 2022
4.4 Global Sales Price by Manufacturers
4.5 Analysis of Competitive Landscape
4.5.1 Manufacturers Market Concentration Ratio
4.5.2 Global Market Share by Company Type (Tier 1, Tier 2, and Tier 3)
4.5.3 Global Manufacturers Geographical Distribution
4.6 Mergers and Acquisitions, Expansion Plans
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– Free Competitive analysis of any 5 key market players.
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