Studying Data Science: Reflecting on the Path

Since beginning the journey to improve my skills and eventually focusing on the data science path I have come across and explored many ways to reach my destination.

Data science is not an exact discipline therefore there are many different paths to take, depending also on the type of work you want to do: academic, research, industry, business (sales etc) or tech. The more academic routes usually utilize the R programming language, whilst education focused on commercial use tends to use Python. There are paths that equate data science to analytics, or predictive analytics, or machine learning. There are others that are project based, so you make progress by completing the projects not by simply taking the class. In all honesty, the amount of information and opinions on data science is completely overwhelming.

Below is a Prezi presentation I have created to try to put things in order and perspective. I intend to keep working on it to fine tune the contents both in terms of completeness and presentation. I do hope it will be helpful to others as I try to map all the data science education offerings.

My own path until now has gone as follows:

In addition, I have also taken edX course The Analytics Edge by MITx (more than 60% completed but no certificate), as well as exploring a few other courses on edX and Udacity.

Finally, I have enrolled and cancelled the Udacity Data Analyst nanodegree. At the moment I am once again enrolled and have paid for the first month. The reason I keep reverting back to Udacity is:

  • The UI is excellent on Windows, Android and IOS.
  • The presentation and lectures are detailed without assuming anything – videos are short so you can skip easily from a small chunk of the lecture to the next one.
  • There are projects to complete which at the end will provide an excellent portfolio.
  • It uses and teaches (mostly) Python (specifically Anaconda) and other open source tools which can be utilised immediately.

Cons are that it is expensive at 200 USD per month, and the course is demanding. However, you get 50% of your fees back when the course is completed. The course is self-paced, so missing a deadline is not the end of the world.

There is another small course (again self-paced) that I will take from edx, Analyzing and Visualizing Data with Excel.

As I learn more I will add to the Prezi and share the revised version. In the meantime, Udacity will be my second home until I complete the nanodegree. Feel free to share your own experiences and ideas.

Michael Lazarou
Michael Lazarou
Michael Lazarou manages revenue assurance and fraud at Epic, a Cypriot telco, having joined their RA function in 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. Michael's accumulated experience of online training also led him to volunteer for the role of Coordinator of the RAG Learning online education platform.