There are at least 60 data science degree programs offered by universities worldwide that costs anywhere between $50,000 to $270,000 and takes 1 to 4 years to complete. It might be a good option for those yet to join a college for higher studies, and it certainly has its own benefits over other programs in similar or not-to-so similar disciplines. But for those who have already invested in some higher education program or are professionals wiling to make a career switch to analytics, committing such huge sum of money as well as few years is not a viable option.
There are few good summer programs, fellowships and boot camps available for a switch to data science profession but they are either impossible to get in, or require a PhD or advanced degree. Some would cost between $15,000 to $25,000 for 2 months or so which is again an expensive proposition. While these are very good options for recent Ph.D. graduates to gain some real industry experience, we have yet to see their quality and performance against an experienced analytics professional. So here’s an in depth analysis of some MOOCs available online for free that’ll help you in starting a career in analytics. I’ll keep adding more to this list with time.
CS 109- Data Science course from Harvard School of Engineering and Applied Sciences will give you a good understanding of the field. However the data sets provided for practice are usually very clean and convenient compared to what you’ll face in the real life as a Data Scientist. Also, the analysis that you’ll perform may not be related to your domain of interest but and focus on general topics of interest with widely available data sets. I’ll suggest you use this course to build a foundation and then focus on practical problem solving with platforms like Kaggle (covered later in this post).
Machine Learning (Stanford University): John Hopkin’s Data Science Specialization on Coursera will give you a fair idea about writing code and can be a good starting point. Once you have completed the courses on R programming and Data Scientist Toolkit You can move on to the other courses like Exploratory Data Analysis Using R on udacity which goes deeper into the application of the languages and reasoning behind it.
Big Data University offers multiple learning paths like Scala, Hadoop, Spark, Big data analytics etc. It doesn’t charge for any of its courses and it takes a business-centric approach to learning which sets it apart from many other online open course providers. It provides the advantage to Students for signing up independently at home and work at their own speed, just as they can with Coursera andFuturelearn or other similar services. It has signed up over 400,000 students.
One of the most popular courses on machine learning taught by one of the best professors in machine learning domain and co-founder of coursera, Andrew Ng. The course is well-organized and covers all core concepts of machine learning. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).
Data Analysis and Statistical Inference from DataCamp is really good for beginners.
After you have completed this, head over to the courses in Data Analyst Nanodegree Program. These are free online courses but you can join only at certain time when the course is getting started. A common issue that people face is that the courses are very generic and it might not fit your individual needs. So I have listed down some of the common aspects that data scientists should look for in various courses and certain specific aspects that vary depending on your industry and specialization.