Data Engineers and Data Scientists need data for their Day-to-Day job. Of course, It could be for Data Analytics, Data Prediction, Data Mining, Building Machine Learning Models Etc., All these are taken care of by the respective team members and they need to work towards identifying relevant data sources, and associated with the business problems. Data Sources can be identified in two different ways. With respect to functional aspects, it can be sub-divided into Primary and Secondary sources. Both above said is nothing but in the form of non-digital form.

What is the best way to master Data Science? What will get me hired? My answer remains constant: There is no alternative to working on portfolio-worthy projects. Even after clearing the TensorFlow Developer Certificate Exam, I'd say no certificates, no courses, nothing, you can only prove your competency with projects that showcase your research, programming skills, mathematical background, etc. In my post, how to build an effective Data Science Portfolio, I shared many project ideas and other tips to prepare a kickass portfolio.