What can be done to enhance my career in the field of Data Science?
Everyone learns differently. But to know what works for you, choose a system that you found successful in school or college. Often we used to sit with a resource (usually a book) and read and take notes. Do the same now. The process is the same but the resources have changed.
Is it essential to take a master's degree to build a career in the field of Data Science?
You can progress in data science by increasing know-how or knowledge and continuous practice.
If you find yourself struggling to self-study, then maybe a course can be useful. Choosing a degree program requires a choice to be made across many levers (college experience, cost, improvement in career prospects, location, etc.). Some online degree programs may be more favourable compared to a traditional Masters’ program. Before committing to a 1 or 2 yr program, identify smaller programs specific to the area you want to improve on (see nano degree programs from Udacity). Find quick courses on Udemy/Coursera/EdX that can give you an overview within a few weeks or a month of the target topic.
If you are looking to acquire a Masters’s degree for credentials, then maybe first explore other forms of credentials that the industry has begun to pay attention to. E.g., you can share public Tableau dashboards, RShiny dashboards, Kaggle kernels, Kaggle rankings, and Git repositories of your work to “prove” merit in the chosen area (see)
For a newbie in data analytics what holistic approach in terms of learning you would suggest?
Everyone learns differently. But to know what works for you, choose a system that you found successful in school or college. Often we used to sit with a resource (usually a book) and read and take notes. Do the same now. The process is the same but the resources have changed.
You can choose to view a YouTube video on SQL window functions (every Analyst should know this IMHO) and as you view the video, you can take notes. You can then go back and do more research and expand on your notes. Furthermore, you can practice (like lab sessions in college) what you have learned to make your knowledge sticky.
There are many resources out there, such as blogs, newsletters, YouTube videos, online courses, Notebooks, Git repositories, that you can learn from. See this approach on how to read technical articles.
The discussion shared above was part of many Q&A sessions Harsh Singhal conducted with Data teams at various companies and colleges.
