In the years 2017 to 2019 Microsoft provided several professional education programs, and named them “Microsoft Professional Program for …”. I took part in the programs for Data Science and Artificial Intelligence, which had some overlap. The programs were set up as online courses through EDX, an online university. There were about 12 courses for each program. Typically you would need from one to four weeks per course, so overall it took nearly 2 years for me to do this besides my normal work.
The (long) screenshot at the end gives an impression about the topics. One very interesting part was the so called capstone. The capstones were “nearly-real-life” projects that had do be performed. At the end a report had to be written and to be evaluated by peers. To make this a little bit of fun, there was also a competition about the lowest errors for predictions.
The capstone was offered every quarter, so Microsoft had to be creative and invent topics like:
- DAT264x: Bumblebee or Honeybee?
- DAT264x: Improving Data Quality in Medical Imaging
- DAT264x: Identifying Appliances from Energy Use Patterns
- DAT264x: Identifying Topics of World Bank Publications
- DAT264x: Identifying Malaria In Blood Imagery
- DAT264x: Identifying Accents in Spectrograms of Speech
- DAT102x: Predict Student Loan Repayment
- DAT102x: Predict Student Earnings
- DAT102x: Predicting Poverty in the United States
- DAT102x: Predicting Earthquake Damage in Nepal
- DAT102x: Predicting Heart Disease Mortality
- DAT102x: Predicting Chronic Hunger
- DAT102x: Predicting Evictions
- DAT102x: Predicting Mortgage Approvals From Government Data
- DAT102x: Predicting Poverty Around the World
I had to take “Predicting Chronic Hunger” as the Data Science capstone and “Identifying Topics of World Bank Publications” (natural language processing) as the Artificial Intelligence capstone.
The professional programs were in contrast with the rather technically oriented certifications like “MCSE”, “MCSA” (Microsoft Certified Sokution Expert / Associate), since it was more oriented towards theory and methods. It’s a pity that they are not existing any more.
This is how the course overview looked like:
For better readability:
Get Started with Data Science
Introduction to Data Science
Get started on your data science journey, as you learn what it takes to become a Data Scientist. Learn to work with and explore data using a variety of visualization, analytical, and statistical techniques.
Analyze and Visualize Data
Option 1: Analyzing and Visualizing Data with Power BI
Learn how to connect and visualize your data with Microsoft Power BI. Find out how to import your data, author reports using Power BI Desktop, and publish those reports to the Power BI service. Create dashboards and share with business users on the web and on mobile devices.
Option 2: Analyzing and Visualizing Data with Excel
Explore tools in Excel that enable the analysis of more data than ever before, with improved visualizations and more sophisticated business logic. Learn how to import data from different sources, create mashups between data sources, and prepare data for analysis.
Communicate Data Insights
Analytics Storytelling for Impact
Learn effective strategies and tools to master data communication in the most impactful way possible—through well-crafted analytics stories. 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.
Apply Ethics and Law in Analytics
Ethics and Law in Data and Analytics
Learn to apply ethical and legal frameworks to initiatives in the data profession. You will explore practical approaches to data and analytics problems posed by work in Big Data, Data Science, and AI. You will also investigate applied data methods for ethical and legal work in Analytics and AI.
Query Relational Data
Querying 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.
Explore Data with Code
Option 1: Introduction to R for Data Science
Learn the basics of R programming. 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.
Option 2: Introduction to Python for Data Science
Learn the basics of Python programming. Starting 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.
Apply Math and Statistics to Data Analysis
Option 1: Essential Math for Machine Learning: R Edition
Learn the essential mathematical foundations for machine learning and artificial intelligence using R. The couse focuses on 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.
Option 2: Essential Math for Machine Learning: Python Edition
Learn the essential mathematical foundations for machine learning and artificial intelligence using Python. The couse focuses on 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.
Option 3: 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.
Explore Data with Code
Option 1: Data Science Research Methods: R Edition
Learn the essential skills and hands-on experience with the science and research aspects of data science work using R, from setting up a proper data study to making valid claims and inferences from data experiments.
Option 2: Data Science Research Methods: Python Edition
Learn the essential skills and hands-on experience with the science and research aspects of data science work using Python, from setting up a proper data study to making valid claims and inferences from data experiments.
Build Machine Learning Models
Option 1: Principles of Machine Learning: R Edition
Get hands-on experience building and deriving insights from machine learning models using R and Azure Notebooks.
Option 2: Principles of Machine Learning: Python Edition
Get hands-on experience building and deriving insights from machine learning models using Python and Azure Notebooks.
Build Predictive Solutions at Scale
Option 1: Developing Big Data Solutions with Azure Machine Learning
Learn how to build predictive web services for Big Data workflows using Azure Machine Learning.
Option 2: 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.
Option 3: 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. Find out how to cleanse and transform data, build machine learning models, and create real-time machine learning solutions using Python, Scala, and R with Apache Spark.
Microsoft Professional Capstone : 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.
Finally, here are the courses from the Artificial Intelligence program. As I wrote earlier, with some overlap to the Data Science Program:
- Introduction to Artificial Intelligence (AI)”
- Introduction to Python for Data Science
- Essential Math for Machine Learning: Python Edition
- Ethics and Law in Data and Analytics
- Data Science Essentials
- Principles of Machine Learning
- Deep Learning Explained
- Reinforcement Learning Explained
- Natural Language Processing (NLP)
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