Data Science Track – Microsoft Professional Program

Microsoft Data Science Track

DataChangers Microsoft Data Science Track EN with linksOpportunities 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.

Change your world with DataChangers and 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 10 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!

You can also download this track as pdf: Data Science Track - Microsoft Professional Program (66 downloads) .

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.

These are the steps of the Data Science track:

Data Science Fundamentals

  1. Introduction to Data Science
  2. Querying Data with Transact-SQL
    To prepare yourself for step 3, you can follow Introduction to Data Analysis using Excel
  3. Analyze and Visualize Data with Excel or Analyze and Visualize Data with PowerBI
  4. Essential Statistics for Data Science with Excel

    Core Data Science

  5. Introduction to R for Data Science or Introduction to Python for Data Science
  6. Data Science Essentials
  7. Principles of Machine Learning

    Applied Data Science

  8. Programming with R for Data Science or Programming with Python for Data Science
  9. Predictive Analytics with Spark in Azure or Analyze Big Data with Microsoft R
  10. Microsoft Professional Capstone – Data Science (you need to finish the prior courses first)

During this year, the program will be changed a little.

Data Science Track, upcoming changes as from July 1st (some courses are not yet available)

  1. Introduction to Data Science
    To prepare yourself for step 3, 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
  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: REdition OR Data Science Research Methods: Python Edition
  9. Principles of Machine Learning: R Edition OR Principles of Machine Learning: Python Edition
  10. Developing Big Data Solutions with Azure Machine Learning OR Predictive Analytics with Spark in Azure OR Analyze Big Data with Microsoft R
  11. Microsoft Professional Capstone: Data Science

Explore the Microsoft Data Science Courses

DataChangers Data Science Courses - Data Science Orientation

Introduction 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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DataChangers Data Science Courses - Query DataQuerying 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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DataChangers Data Science Courses - Introduction to Data Analysis with Excel

Introduction 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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DataChangers Data Science Courses - Analyzing and Visualizing Data with Excel

Analyzing 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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DataChangers Data Science Courses - Analyzing and Visualizing Data with Power BI

Analyzing 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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DataChangers Data Science Courses - Essential Statistics for Data Analysis using Excel

Essential 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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DataChangers Data Science Courses - Introduction to R for Data Science

Introduction 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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DataChangers Data Science Courses - Introduction to Python for Data Science

Introduction 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.

:earn more…


DataChangers Data Science Courses - Data Science Essentials

Data Science Essentials

Explore data visualization and exploration concepts with experts from MIT and Microsoft, and get an introduction to machine learning.

About This Course
Demand for data science talent is exploding. Develop your career as a data scientist, as you explore essential skills and principles with experts from Duke University and Microsoft.

In this data science course, you will learn key concepts in data acquisition, preparation, exploration, and visualization taught alongside practical application oriented examples such as how to build a cloud data science solution using Microsoft Azure Machine Learning platform, or with R, and Python on Azure stack.

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DataChangers Data Science Courses - Principles of Machine Learning

Principles of Machine Learning

Get hands-on experience building and deriving insights from machine learning models using R, Python, and Azure Machine Learning.

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, Python, and Azure Machine Learning.

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DataChangers Data Science Courses - Programming with R for Data Science

Programming with R for Data Science

Learn the fundamentals of programming with R, from reading and writing data to customizing visualizations and performing predictive analysis.

About This Course
In this computer science course from Microsoft, developed in collaboration with the Technical University of Denmark (DTU), get the knowledge and skills you need to use R, the statistical programming language for data scientists, in the field of your choice.

In this course you will learn all you need to get up to speed with programming in R. Explore R data structures and syntaxes, see how to read and write data from a local file to a cloud-hosted database, work with data, get summaries, and transform them to fit your needs. Plus, find out how to perform predictive analytics using R and how to create visualizations using the popular ggplot2 package.

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DataChangers Data Science Courses - Programming with Python for Data Science

Programming with Python for Data Science

Traverse the data analysis pipeline using advanced visualizations in Python, and make machine learning start working for you.

About This Course
This practical course, developed in partnership with Coding Dojo, targets individuals who have introductory level Python programming experience. The course teaches students how to start looking at data with the lens of a data scientist by applying efficient, well-known mining models in order to unearth useful intelligence, using Python, one of the popular languages for Data Scientists. Topics include data visualization, feature importance and selection, dimensionality reduction, clustering, classification and more! All of the data sets used in this course are gathered live-data or inspired by real-world domains that can benefit from machine learning.

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DataChangers Data Science Courses - Implementing Predictive Analytics with Spark on Azure

Implementing 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 R - Microsoft Professional Program

Analyzing 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 Program – Data Science
Microsoft 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!