Studying Data Science: Udacity statistics and edX Microsoft courses

My data science studies continue on two fronts: Udacity statistics courses and edX Microsoft courses.

The hardest thing to decide when taking a course on a topic you have done in the past is which content to examine in detail, and which to skim over. I am having a hard time deciding between the three statistics courses offered by Udacity as I continue to take the courses before registering for the full-blown nanodegree. There are three stats courses offered: Intro to Statistics, Intro to Descriptive Statistics and Intro to Inferential Statistics. I’ve started with the first one – which is the obvious choice – taught by Sebastian Thrun himself (founder of Udacity).

I will be honest, in that I have not made a lot of progress on any of these courses, mostly because these are beginner courses and they assume students have no background in the topic at all. Therefore, I am previewing them to get a feel for the pace and content. I have found the pace to be slow, but I have previous experience with the topics. Personally, I feel I need to review some topics before beginning payments for the full nanodegree. However, the content is presented in a fun manner, with instructors (or the equivalent of TAs) going out to survey people on the topics discussed, while the instructor is actually writing on the screen as he presents (there is nobody standing there with a tie blabbing on while a powerpoint presentation is lazily shown – thank goodness).

On Udacity the plan is to complete the statistics related courses and fill in the gaps, before proceeding with more Python and the data analysis/science courses. This is a strategic decision since otherwise I will never do the stats. For me they’re like the greens you add to your plate next to that juicy steak and mashed potatoes; complimentary and essential, but complimentary all the same.

Further to Udacity I am continuing the courses on edX. I have found that the courses offered by Microsoft are of high quality and that the content is directly applicable to the workplace. While there are numerous data science courses, they are straightforward and practical (they are offered in cooperation with Datacamp for the exercises).

I have started the Introduction to R programming course, which follows an almost identical format to the Introduction to Python course reviewed in my previous post. In addition, I have registered for Data Science and Machine Learning Essentials, and hope to review it next time. These courses are the perfect fit for me as I can complete them quickly (they are relatively easy) and they provide a great intro to the topic.

In addition, I find it very interesting and refreshing that Microsoft has changed its strategy and practices by endorsing open source tools and seeing the value in utilizing them. It also signifies a clear direction towards analytics/data science followed by most tech giants nowadays.

Finally, I will quickly mention that Microsoft offers its own MOOC courses on Microsoft Virtual Academy. I have not explored it in depth but it offers a wealth of courses split into four categories: Developers, IT Pros, Data Pros and Students. The courses offered on edX are also offered under the “Data Pros” section, in addition to others. The quality, wealth and amount of courses offered online is truly amazing. There is no limit to what you can learn from top universities and vendors.

Michael Lazarou
Michael Lazarou
Michael Lazarou has worked as a Revenue Assurance Analyst for MTN Cyprus since March 2011. His background includes a double major in Computer Science and Economics, as well as an MBA. Before being lured into the exciting world of telecoms he worked as a software developer.

Michael is interested to gain a better understanding of different aspects of RA and data analysis. He shares his insights on training courses he participates in with Commsrisk.