My 2 cents on “Professional Certificate in Data Science from Harvardx” (Part 2)
Continuing from my previous article — https://mansimransinghanand.medium.com/my-2-cents-on-professional-certificate-in-data-science-from-harvardx-part-1-d088ab37ae15?source=your_stories_page----------------------------------------
Here I am going to go ahead and give my opinion on the remaining modules of the Professional Data Science course of Harvardx on the EDX platform.
5. Data Science: Productivity Tools — I found this course to be relatively straightforward having being from a technical background and have some experience in the industry these tools are our Bread and Butter. The first section was about Working with basic Unix. We went through some of the common commands that might essential to a Data Science professional. Next up there was some discussion about the relevance of using Git and GitHub and how important versioning control is. Lastly we walked through on how to use Reproducible Reports and some Advanced Unix. The tests that we had were also based on these common Unix and GitHub commands. As I mentioned earlier I found this relatively simple as I use these commands everyday
6. Data Science: Wrangling — Next up there was this course on Wrangling. This you could say was the first course where we started getting our hands dirty on some Data Science stuff. We understand the different techniques of Data importing to our file. Different mechanisms to Tidy the data for example Reshaping the Data, combining columns and Scrapping the data. This section was pretty important because the more structured your data is before it goes into your model, the faster and more productive it will be. Next we saw different ways to process the String data something which is very important if you are working with textual data. And lastly we looked at how to play around with Time and Data data. This was a very very interesting module as it gave initial glimpse of the fun you can have with Data science.
7. Data Science: Linear Regression — Now is where you start getting into the meat of the Data Science learning. Your data is all tided up, clean and structured and now we can start using this data to make awesome models. This module begins with the introduction to Regression and Correlation and their importance in DS. And then we worked on our first Linear model. This was further resulted with Least Square Estimates. This was further practically applied on a Baseball data set. This module was then concluded by understanding what Confounding is and its relevance in DS.
8. Data Science: Machine Learning — This module was by far the best module amongst all. This is probably what you came for when you took up this course. In this module we had covered various concepts. Started of with Importance of Machine Learning and its Basics. Then we at the concepts of Linear Regression and using it for Predictions and Smoothing etc. We had done some stuff about Regression but now is the time for some Classification concepts. We looked into KNN, Cross validation and Generative models. Next up we looked at how to go about with Classification when there are more than 2 classes you are dealing with. This was then rounded up with using all the skills learnt into building a Recommender System.
9. Data Science: Capstone — With that we have come to our last module. This module is quite different from the previous modules. There was no more learning that we did here but practically applying those skills to some real world projects. The first data set we received was the “Movie Lens” Data set. Here we had to use all the learning's we had gained right through the course to get the desired output. The second data set that we could choose was something we could choose on our own. I took the “Analysis of Census Income“ Data set to predict in which class a person belongs to with respect to his earnings. A very interesting use case.
So then, this rounds up all the modules that were there in this Data Science. Below is my certificate I had earned after completing all the modules.
So if you are just starting Data Science or are aware of only the basics, this is the right course with you. If you are an Expert then you would probably now most of the stuff in here.
