TL:DR? Microsoft’s Professional Degree in Data Science is a very well curated set of courses that feed the mind!
Over the summer, I decided to push myself and was lucky to stumble upon the Microsoft Professional Degree in Data Science (currently running in beta at Microsoft before it goes public). They needed some miners’ canaries to test out the “Accelerated Version” – quicker sprints, compressed timeframes, etc. – and I said yes! All the course materials are administered through https://courses.edx.org/ and are available today.
That’s my wife with greasy hair and sweatpants at 5:30 am one day, coding in R (she’s never been more attractive to me!). She’s a data scientist and I wanted to court her again…so I needed to know AUC, ANOVA, and lots of R.
I was a data zero (sure I took the required Math/Stats in school but have since forgotten everything) and was nervous that I wouldn’t have the brain power to make this happen. To my surprise, the program has been curated to be very approachable – you do need to want to learn and you have to work hard. However, classic rules apply: If I can do it, so can you.
The program consists of some required courses with a few electives. I took:
- Data Science Orientation
- Querying with Transact-SQL (banged this out in a few hours given my dev background)
- Analyzing and Visualizing Data with Excel (Could have chosen Power BI as a replacement)
- Introduction to R for Data Science (Choose between R or Python)
- Data Science Essentials
- Principles of Machine Learning
- Programming in R for Data Science (Choose between R or Python)
- Developing Intelligent Apps (Elective)
- Statistical Thinking for Data Science and Analytics (Truly deep Stats course taught by Ivy League professors – spent the longest time here)
- The Final Project
When I started the program, I didn’t realize how quickly the payoff would be for my portfolio of work. For example, one of the electives in the Develop Intelligent Solutions module offered hands on labs in Machine Learning, Creating Bots and Stream Analytics – all extremely germane to my client work.
The Final Project
Work got in the way 🙂 during the compressed timeframe of this program, so the final project – which builds upon the skills gained through all the other courses – presented a double challenge to me. First, I had to pass the course by gaining a 70% score on the final Machine Learning experiment *and* do it in about a week (normally you have 1 month to complete this). So I took a week off and immersed myself in Azure Machine Learning. I don’t remember the last time I was so obsessed with the problem at hand and was required to use ingenuity to get things done. The final project was modelled after competitions at http://kaggle.com. It truly was an enriching experience for me and am so proud to have placed 205 out of 354 competitors – let’s face it, the last person to graduate medical school is still called “Doctor” 🙂 What a fantastic journey this has been and what a lot of prep and care was taken to create this final project. Kudos to the team!
Why it worked for me and What’s Next
The program added structure and gave me deadlines – mini-projects that forced me to focus, sacrifice other activities and get things done. The rigor of the program and the community created by the fellow students, the program development/support team, and teaching assistants, spurred me on when other activities sought equal attention for my time. I made sure that client work was always prioritized and that family came first (except for that last week!). The structure of the program gave me mile markers to keep me going and the overall program has left me with a renewed, action-oriented love of learning that I will continue to build upon.
So what is next? I’m most likely going to continue taking courses via https://courses.edx.org/. Happy Learning and see you R-ound!