Data Science Track – Microsoft Professional Program

Microsoft Data Science Track

DataChangers Microsoft Data Science TrackOpportunities for data scientists—one of today’s hottest jobs—are rapidly growing in response to the exponential amounts of data being captured and analyzed. Companies hire data scientists to find insights and to solve meaningful business problems. Therefore we offer the Microsoft Data Science track.

Start with the Microsoft Data Science Track to discover this world of data! With these online courses it is possible to start a course wherever and whenever you want. All the courses are part of the Microsoft Professional Program.

The Microsoft Data Science Track exists of 11 steps to learn the basic skills of a data scientist. You don’t have to follow them in the specific order, but some courses are related. Some steps offer your various options, so you can use i.e. your preferred programming language. For every course you can obtain an official Microsoft Professional Program certificate, issued by Microsoft, for which you can buy a voucher from us (in collaboration with MD2C). All you need is your Windows LiveID* to register with on the DataChangers Academy, and you will be ready to start your journey!

We also offer this track as part of a 3-months fulltime program with the Future Skills Lab. This program also includes softskills and “garage days” on which you have workshops, talks, hackathons, etc.

If you have any questions regarding this track, please drop us a note.

*Please use a Windows Live ID email address to register at the DataChangers Academy if you want to obtain a certificate after finishing the courses.

Data Science Track courses

Data Science Fundamentals

  1. Introduction to Data Science
    To prepare yourself for step 2, you can follow Introduction to Data Analysis using Excel
  2. Analyze and Visualize Data with Excel or Analyze and Visualize Data with PowerBI
  3. Analytics Storytelling for Impact
  4. Ethics and Law for Data and Analytics

    Core Data Science

  5. Querying with Transact-SQL
  6. Introduction to R for Data Science or Introduction to Python for Data Science
  7. Essential Math for Machine Learning: R Edition OR Essential Math for Machine Learning: Python Edition OR Essential Statistics for Data Analysis using Excel
  8. Data Science Research Methods: R Edition OR Data Science Research Methods: Python Edition

    Applied Data Science

  9. Principles of Machine Learning: R Edition OR Principles of Machine Learning: Python Edition
  10. Predictive Analytics with Spark in Azure OR Analyze Big Data with Microsoft R OR Developing Big Data Solutions with Azure Machine Learning
  11. Microsoft Professional Capstone: Data Science (you need to finish the prior courses first)

Explore the Microsoft Data Science Courses


Introduction to Data Science Microsoft Professional ProgramIntroduction to Data Science

Get started on your Data Science journey.

About This Course
Learn what it takes to become a data scientist.
This is the first stop in the Data Science curriculum from Microsoft. It will help you get started with the program, plan your learning schedule, and connect with fellow students and teaching assistants. Along the way, you’ll get an introduction to working with and exploring data using a variety of visualization, analytical, and statistical techniques.

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Introduction to Data Analysis using ExcelIntroduction to Data Analysis using Excel

Learn the basics of Excel, one of the most popular data analysis tools, to help visualize and gain insights from your data.

About This Course
The ability to analyze data is a powerful skill that helps you make better decisions. Microsoft Excel is one of the top tools for data analysis and the built-in pivot tables are arguably the most popular analytic tool.

In this course, you will learn how to perform data analysis using Excel’s most popular features. You will learn how to create pivot tables from a range with rows and columns in Excel. You will see the power of Excel pivots in action and their ability to summarize data in flexible ways, enabling quick exploration of data and producing valuable insights from the accumulated data.

Pivots are used in many different industries by millions of users who share the goal of reporting the performance of companies and organizations. In addition, Excel formulas can be used to aggregate data to create meaningful reports. To complement, pivot charts and slicers can be used together to visualize data and create easy to use dashboards.

You should have a basic understanding of creating formulas and how cells are referenced by rows and columns within Excel to take this course. If required, you can find many help topics on Excel at the Microsoft Office Support Site. You are welcome to use any supported version of Excel you have installed in your computer, however, the instructions are based on Excel 2016. You may not be able to complete all exercises as demonstrated in the lectures but workarounds are provided in the lab instructions or Discussion forum. Please note that Excel for Mac does not support many of the features demonstrated in this course.

After taking this course you’ll be ready to continue to our more advanced Excel course, Analyzing and Visualizing Data with Excel.

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Analyzing and Visualizing Data with Excel Microsoft Professional ProgramAnalyzing and Visualizing Data with Excel

Develop your skills with Excel, one of the common tools that data scientists depend on to gather, transform, analyze, and visualize data.

About This Course
Excel is one of the most widely used solutions for analyzing and visualizing data. It now includes tools that enable the analysis of more data, with improved visualizations and more sophisticated business logics. In this data science course, you will get an introduction to the latest versions of these new tools in Excel 2016 from an expert on the Excel Product Team at Microsoft.

Learn how to import data from different sources, create mashups between data sources, and prepare data for analysis. After preparing the data, find out how business calculations can be expressed using the DAX calculation engine. See how the data can be visualized and shared to the Power BI cloud service, after which it can be used in dashboards, queried using plain English sentences, and even consumed on mobile devices.

Do you feel that the contents of this course is a bit too advanced for you and you need to fill some gaps in your Excel knowledge? Do you need a better understanding of how pivot tables, pivot charts and slicers work together, and help in creating dashboards? If so, check out Introduction to Data Analysis using Excel.

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Analyzing and Visualizing Data with Power BI Microsoft Professional ProgramAnalyzing and Visualizing Data with Power BI

Learn Power BI, a powerful cloud-based service that helps data scientists visualize and share insights from their organizations’ data.

About This Course
Power BI is quickly gaining popularity among professionals in data science as a cloud-based service that helps them easily visualize and share insights from their organizations’ data.

In this data science course, you will learn from the Power BI product team at Microsoft with a series of short, lecture-based videos, complete with demos, quizzes, and hands-on labs. You’ll walk through Power BI, end to end, starting from how to connect to and import your data, author reports using Power BI Desktop, and publish those reports to the Power BI service. Plus, learn to create dashboards and share with business users—on the web and on mobile devices.

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Analytics Storytelling for Impact Microsoft Professional ProgramAnalytics Storytelling for Impact

This course is about to learn the art and science of data storytelling and achieve greater analytics impact.

About this course

All analytics work begins and ends with a story. Storytelling with data is the analytics professional’s missing link in delivering the essence of date signals and insights to executives, management, and other stakeholders.

In this analytics storytelling course, you’ll learn effective strategies and tools to master data communication in the most impactful way possible—through well-crafted analytics stories.

You’ll explore what a story is and, perhaps more importantly, what a story is not. Find out how stories create value and why they matter. Learn to craft stories, command the room, finish strong, and assess your impact. Get practical help applying these ideas to your data analytics work. Plus, you’ll learn guidelines and best practices for creating high-impact reports and presentations.

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Ethics and law in data and analytics Microsoft Professional ProgramEthics and Law in Data and Analytics

This course is about Analytics and AI: powerful tools that have real-word outcomes. Learn how to apply practical, ethical, and legal constructs and scenarios so that you can be an effective analytics professional.

About This Course

Corporations, governments, and individuals have powerful tools in Analytics and AI to create real-world outcomes, for good or for ill.

Data professionals today need both the frameworks and the methods in their job to achieve optimal results while being good stewards of their critical role in society today.

In this course, you’ll learn to apply ethical and legal frameworks to initiatives in the data profession. You’ll explore practical approaches to data and analytics problems posed by work in Big Data, Data Science, and AI. You’ll also investigate applied data methods for ethical and legal work in Analytics and AI.

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Querying Transact-SQL Microsoft Professional ProgramQuerying Data with Transact-SQL

From querying and modifying data in SQL Server or Azure SQL to programming with Transact-SQL, learn essential skills that employers need.

About This Course
Transact-SQL is an essential skill for data professionals and developers working with SQL databases. With this combination of expert instruction, demonstrations, and practical labs, step from your first SELECT statement through to implementing transactional programmatic logic.Work through multiple modules, each of which explore a key area of the Transact-SQL language, with a focus on querying and modifying data in Microsoft SQL Server or Azure SQL Database. The labs in this course use a sample database that can be deployed easily in Azure SQL Database, so you get hands-on experience with Transact-SQL without installing or configuring a database server.

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introduction to R for data science Microsoft Professional ProgramIntroduction to R for Data Science

Learn the R statistical programming language, the lingua franca of data science in this hands-on course.

About This Course
R is rapidly becoming the leading language in data science and statistics. Today, R is the tool of choice for data science professionals in every industry and field. Whether you are full-time number cruncher, or just the occasional data analyst, R will suit your needs.

This introduction to R programming course will help you master the basics of R. In seven sections, you will cover its basic syntax, making you ready to undertake your own first data analysis using R. Starting from variables and basic operations, you will eventually learn how to handle data structures such as vectors, matrices, data frames and lists. In the final section, you will dive deeper into the graphical capabilities of R, and create your own stunning data visualizations. No prior knowledge in programming or data science is required.

What makes this course unique is that you will continuously practice your newly acquired skills through interactive in-browser coding challenges using the DataCamp platform. Instead of passively watching videos, you will solve real data problems while receiving instant and personalized feedback that guides you to the correct solution.

Enjoy!

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Introduction to Python for Data ScienceIntroduction to Python for Data Science

The ability to analyze data with Python is critical in data science. Learn the basics, and move on to create stunning visualizations.

About This Course
Python is a very powerful programming language used for many different applications. Over time, the huge community around this open source language has created quite a few tools to efficiently work with Python. In recent years, a number of tools have been built specifically for data science. As a result, analyzing data with Python has never been easier.

In this practical course, you will start from the very beginning, with basic arithmetic and variables, and learn how to handle data structures, such as Python lists, Numpy arrays, and Pandas DataFrames. Along the way, you’ll learn about Python functions and control flow. Plus, you’ll look at the world of data visualizations with Python and create your own stunning visualizations based on real data.

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Essential Statistics for Data Analysis using Excel - Microsoft Professional ProgramEssential Statistics for Data Analysis using Excel

Gain a solid understanding of statistics and basic probability, using Excel, and build on your data analysis and data science foundation.

About This Course
If you’re considering a career as a data analyst, you need to know about histograms, Pareto charts, Boxplots, Bayes’ theorem, and much more. In this applied statistics course, the second in our Microsoft Excel Data Analyst XSeries, use the powerful tools built into Excel, and explore the core principles of statistics and basic probability—from both the conceptual and applied perspectives. Learn about descriptive statistics, basic probability, random variables, sampling and confidence intervals, and hypothesis testing. And see how to apply these concepts and principles using the environment, functions, and visualizations of Excel.

As a data science pro, the ability to analyze data helps you to make better decisions, and a solid foundation in statistics and basic probability helps you to better understand your data. Using real-world concepts applicable to many industries, including medical, business, sports, insurance, and much more, learn from leading experts why Excel is one of the top tools for data analysis and how its built-in features make Excel a great way to learn essential skills.

Before taking this course, you should be familiar with organizing and summarizing data using Excel analytic tools, such as tables, pivot tables, and pivot charts. You should also be comfortable (or willing to try) creating complex formulas and visualizations. Want to start with the basics? Check out Introduction to Data Analysis using Excel. As you learn these concepts and get more experience with this powerful tool that can be extremely helpful in your journey as a data analyst or data scientist, you may want to also take the third course in our series, Analyzing and Visualizing Data with Excel. This course includes excerpts from Microsoft Excel 2016: Data Analysis and Business Modeling from Microsoft Press and authored by course instructor Wayne Winston.

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Microsoft Professional Program - Essential Math for Machine Learning R EditionEssential Math for Machine Learning R Edition

This course is about learning the essential mathematical foundations for machine learning and artificial intelligence.

This course is part of the Microsoft Professional Program – Data Science track.

About this course

Want to study machine learning or artificial intelligence, but worried that your math skills may not be up to it? Do words like “algebra’ and “calculus” fill you with dread? Has it been so long since you studied math at school that you’ve forgotten much of what you learned in the first place?

You’re not alone. Machine learning and AI are built on mathematical principles like Calculus, Linear Algebra, Probability, Statistics, and Optimization; and many would-be AI practitioners find this daunting. This course is not designed to make you a mathematician. Rather, it aims to help you learn some essential foundational concepts and the notation used to express them. The course provides a hands-on approach to working with data and applying the techniques you’ve learned.

This course is not a full math curriculum. It’s not designed to replace school or college math education. Instead, it focuses on the key mathematical concepts that you’ll encounter in studies of machine learning. It is designed to fill the gaps for students who missed these key concepts as part of their formal education, or who need to refresh their memories after a long break from studying math.

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Microsoft Professional Program - Essential Math for Machine Learning Python EditionEssential Math for Machine Learning Python Edition

This course is about learning the essential mathematical foundations for machine learning and artificial intelligence.

This course is part of the Microsoft Professional Program – Data Science track and the Microsoft Professional Program – Artificial Intelligence track.

About this course

Want to study machine learning or artificial intelligence, but worried that your math skills may not be up to it? Do words like “algebra” and “calculus” fill you with dread? Has it been so long since you studied math at school that you’ve forgotten much of what you learned in the first place?

You’re not alone. machine learning and AI are built on mathematical principles like Calculus, Linear Algebra, Probability, Statistics, and Optimization; and many would-be AI practitioners find this daunting. This course is not designed to make you a mathematician. Rather, it aims to help you learn some essential foundational concepts and the notation used to express them. The course provides a hands-on approach to working with data and applying the techniques you’ve learned.

This course is not a full math curriculum; it’s not designed to replace school or college math education. Instead, it focuses on the key mathematical concepts that you’ll encounter in studies of machine learning. It is designed to fill the gaps for students who missed these key concepts as part of their formal education, or who need to refresh their memories after a long break from studying math.

Learn more…


Microsoft Professional Program - Data Science Research Methods R EditionData Science Research Methods: R Edition

This course will give you hands-on experience with the science and research aspects of data science work, from setting up a proper data study to making valid claims and inferences from data experiments.

About this course

Data scientists are often trained in the analysis of data. However, the goal of data science is to produce good understanding of some problem or idea and build useful models on this understanding. Because of the principle of “garbage in, garbage out,” it is vital that the data scientist know how to evaluate the quality of information that comes into a data analysis. This is especially the case when data are collected specifically for some analysis (e.g., a survey).

In this course, you will learn the fundamentals of the research process—from developing a good question to designing good data collection strategies to putting results in context. Although the data scientist may often play a key part in data analysis, the entire research process must work cohesively for valid insights to be gleaned.

Developed as a language with statistical analysis and modeling in mind, R has become an essential tool for doing real-world Data Science. With this edition of Data Science Research Methods, all of the labs are done with R, while the videos are tool-agnostic. If you prefer your Data Science to be done with Python, please see Data Science Research Methods: Python Edition.

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Microsoft Professional Program - Data Science Research Methods Python EditionData Science Research Methods Python Edition

This course will give you hands-on experience with the science and research aspects of data science work, from setting up a proper data study to making valid claims and inferences from data experiments.

This course is part of the Microsoft Professional Program – Data Science track and the Artificial Intelligence track.

About this course

Data scientists are often trained in the analysis of data. However, the goal of data science is to produce a good understanding of some problem or idea and build useful models on this understanding. Because of the principle of “garbage in, garbage out,” it is vital that a data scientist know how to evaluate the quality of information that comes into a data analysis. This is especially the case when data are collected specifically for some analysis (e.g., a survey).

In this course, you will learn the fundamentals of the research process-from developing a good question to designing good data collection strategies to putting results in context. Although a data scientist may often play a key part in data analysis, the entire research process must work cohesively for valid insights to be gleaned.

Developed as a powerful and flexible language used in everything from Data Science to cutting-edge and scalable Artificial Intelligence solutions, Python has become an essential tool for doing Data Science and Machine Learning. With this edition of Data Science Research Methods, all of the labs are done with Python, while the videos are language-agnostic. If you prefer your Data Science to be done with R, please see Data Science Research Methods: R Edition.

Learn more…


Microsoft Professional Program - Principles of Machine Learning R EditionPrinciples of Machine Learning R Edition

This course will give you hands-on experience building and deriving insights from machine learning models using R and Azure Notebooks.

About this course

Machine learning uses computers to run predictive models that learn from existing data in order to forecast future behaviors, outcomes, and trends.In this data science course, you will be given clear explanations of machine learning theory combined with practical scenarios and hands-on experience building, validating, and deploying machine learning models. You will learn how to build and derive insights from these models using R, and Azure Notebooks.

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Microsoft Professional Program - Principles of Machine Learning Python EditionPrinciples of Machine Learning Python Edition

This course will give you hands-on experience building and deriving insights from machine learning models using Python and Azure Notebooks.

About this course

Machine learning uses computers to run predictive models that learn from existing data in order to forecast future behaviors, outcomes, and trends.

In this data science course, you will be given clear explanations of machine learning theory combined with practical scenarios and hands-on experience building, validating, and deploying machine learning models. You will learn how to build and derive insights from these models using Python, and Azure Notebooks.

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Microsoft Professional Program - Developing Big Data Solutions with Azure Machine LearningDeveloping Big Data Solutions with Azure Machine Learning

This course teaches you how to build predictive solutions for big data using Microsoft Azure Machine Learning.

About this course

The past can often be the key to predicting the future. Big data from historical sources is a valuable resource for identifying trends and building machine learning models that apply statistical patterns and predict future outcomes.

This course introduces Azure Machine Learning, and explores techniques and considerations for using it to build models from big data sources, and to integrate predictive insights into big data processing workflows.

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Implementing Predictive Analytics with Spark in Azure HDInsight Microsoft Professional ProgramImplementing Predictive Analytics with Spark in Azure HDInsight

Learn how to use Spark in Microsoft Azure HDInsight to create predictive analytics and machine learning solutions.

About This Course
Are you ready for big data science? In this course, learn how to implement predictive analytics solutions for big data using Apache Spark in Microsoft Azure HDInsight. See how to work with Scala or Python to cleanse and transform data and build machine learning models with Spark ML (the machine learning library in Spark),

Note: To complete the hands-on elements in this course, you will require an Azure subscription and a Windows client computer. You can sign up for a free Azure trial subscription (a valid credit card is required for verification, but you will not be charged for Azure services). Note that the free trial is not available in all regions.

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Analyzing Big Data with Microsoft RAnalyzing Big Data with Microsoft R

Learn how to use Microsoft R Server to analyze large datasets using R, one of the most powerful programming languages.

About This Course
The open-source programming language R has for a long time been popular (particularly in academia) for data processing and statistical analysis. Among R’s strengths are that it’s a succinct programming language and has an extensive repository of third party libraries for performing all kinds of analyses. Together, these two features make it possible for a data scientist to very quickly go from raw data to summaries, charts, and even full-blown reports. However, one deficiency with R is that traditionally it uses a lot of memory, both because it needs to load a copy of the data in its entirety as a data.frame object, and also because processing the data often involves making further copies (sometimes referred to as copy-on-modify). This is one of the reasons R has been more reluctantly received by industry compared to academia.

The main component of Microsoft R Server (MRS) is the RevoScaleR package, which is an R library that offers a set of functionalities for processing large datasets without having to load them all at once in the memory. RevoScaleR offers a rich set of distributed statistical and machine learning algorithms, which get added to over time. Finally, RevoScaleR also offers a mechanism by which we can take code that we developed on our laptop and deploy it on a remote server such as SQL Server or Spark (where the infrastructure is very different under the hood), with minimal effort.

In this course, we will show you how to use MRS to run an analysis on a large dataset and provide some examples of how to deploy it on a Spark cluster or a SQL Server database. Upon completion, you will know how to use R for big-data problems.

Since RevoScaleR is an R package, we assume that the course participants are familiar with R. A solid understanding of R data structures (vectors, matrices, lists, data frames, environments) is required. Familiarity with 3rd party packages such as dplyr is also helpful.

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Microsoft Professional Capstone - Data ScienceMicrosoft Professional Capstone- Data Science

Solve a real-world data science problem in this capstone project for the Microsoft Professional Program in Data Science.

About this course

This course is part of the Microsoft Professional Program Certificate in Data Science.Showcase the knowledge and skills you’ve acquired during the Microsoft Professional Program for Data Science, and solve a real-world data science problem in this program capstone project. The project takes the form of a challenge in which you will explore a dataset and develop a machine learning solution that is tested and scored to determine your grade.Note: This course assumes you have completed the previous courses in the Microsoft Professional Program for Data Science. For details, go to Microsoft Professional Program Certificate in Data Science.
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Explore also our other tracks, like Entry Level Software Development, Big Data, Artificial Intelligence, Cloud Administration, IT-Suport and DevOps!