Shortage of true data-scientists and the increasing demand of AI-ML world, enforced companies like Microsoft, Google, H2O.ai and Data Robots to automate even the machine learning process to make it simpler for people like me or you who has been recruiting novice data scientists and spending money and more importantly time to implement a simple work in the AI-ML world. A few years back when I was in India, everyone coming in the IT field from any stream of education, used to claim themselves as "Engineers". Today's scenario is kind of the same, as many of us are not at all a true data scientist. Just by doing a 12 to 16 months course and learning a few algorithms, one doesn't become "a Scientist". A true scientist is born and grown with a passion for science and that can't be achieved just by doing an online course.

I work with people who write C/C programs that generate GBs of data, people who manage TBs of data distributed across giant databases, people who are top notch programmers in SQL, Python, R, and people who have setup an organization wide databases working with Hadoop, Sap, Business Intelligence etc. Learn all the basics from Coursera, but if I really have to compare what you would get out of Coursera compared to the vastness of data science, let us say Coursera is as good as eating a burrito at Chipotle Mexican Grill. You certainly can satiate yourself, and you have a few things to eat there. The pathway to value adding data science is really quite deep, and I consider it equivalent to a five star buffet offering 20 cuisines and some 500 different recipes. Coursera is certainly a good starting point, and one should certainly go over these courses, but I personally never paid any money to Coursera, and I could easily learn a variety of things bit by bit over time. Kaggle is a really good resource for budding engineers to look at various other people's ideas and build on them. Learn all the basics from Coursera, but if I really have to compare what you would get out of Coursera compared to the vastness of data science, let us say Coursera is as good as eating a burrito at Chipotle Mexican Grill. You certainly can satiate yourself, and you have a few things to eat there. The pathway to value adding data science is really quite deep, and I consider it equivalent to a five star buffet offering 20 cuisines and some 500 different recipes. Coursera is certainly a good starting point, and one should certainly go over these courses, but I personally never paid any money to Coursera, and I could easily learn a variety of things bit by bit over time. Kaggle is a really good resource for budding engineers to look at various other people's ideas and build on them. Here is an overall sequence of how I progressed myself. The first thing I want to inspire anyone and everyone is to learn the "science".