Becoming A Data Analyst

A few months ago, a fellow aspiring Data Analyst reached out to me asking for some guidance/insights that I might have gained during my journey of becoming a Data Analyst at Google. Below is my reply to their request, that I wanted to share, hoping to help other aspiring students/job-seekers/analysts starting out in the field of Data Science.

A good data scientist needs to have the following skills:

1. Data Engineering skills

2. Visualization skills

3. Modeling Techniques

4. Presentation skills

For aspiring data scientists, I would suggest that a good place to start would be to learn SQL, R, Python and Tableau. With basics mastered in these four tools, you will be quite well-equipped to solve data science problems.

SQL SQL for Newbs is a great course for a quick introduction to MySQL and setting up your SQL environment. After that I would suggest taking the SQL & Database Management A-Z course by SuperDataScience. It is a great course for learning different types of database structures and especially the clarity with which Kirill Eremenko explains the 1st, 2nd and 3rd normal forms of databases is really helpful. It will give you a solid foundation for your journey ahead. Follow this by The Complete SQL Bootcamp by Jose Marcial Portilla which is a great complementary course to the ones mentioned above. It will fill in the few gaps/areas not covered by the other courses; thus giving you a complete repertoire of skills required to solve SQL/databases/data engineering problems.

R vs Python There have been many discussions on whether R or Python is the best language for data science, but I would suggest learning both, as both of them have their strengths. R is really good for creating visualizations, data cleaning and working with vectors & matrices. Python is really good for Machine Learning and Object Oriented Programming. Both have great packages and a great community. For mastering R Programming, I would suggest R Programming: A-Z and R Programming: Advanced by the SuperDataScience team (SDS). And for Python, Complete Python Bootcamp by Jose Marcial Portilla is a great in-depth course to learn about classes and Object Oriented Programming (for extra practice, there are more than 100 projects, with solutions, that you can work on after the end of the course) along with Python A-Z by SDS.

Tableau For Tableau, I would suggest all the Tableau courses on the SuperDataScience website and engaging with the Tableau community on Twitter (they are quite active). Here’s an interactive roadmap that SuperDataScience provides to guide you through your journey.

Overall Approach: It's a Marathon I would suggest learning the basics for each course and then adding layers on top of that. If you then want to sharpen your skills, you can solve exercises on Leetcode, HackerRank and Kaggle and then work on the Workshops offered by SDS (I specifically recommend these as they have tutors showing you how to work on real world problems). It may seem a lot at first but as you move through the coursework it will feel like a natural progression to learn and solve new & exciting problems. Plus, it is a great way to prepare your portfolio to showcase your skills to potential employers or even to get a better role at your current company.

It has personally helped me a lot during interviews to showcase my website (www.rudrabasu.com) which connects to my portfolio of projects on Tableau and GitHub. To be honest, after nearly 5 years in the field of data science there are still some interviews where I don’t know a specific function or an algorithm which they are currently using, but the skills that I acquired through my learnings have made it possible for me to come up with solutions quickly and give me the confidence that I can definitely interview better next time. So, that pushes me to learn, grow and face the next challenge even stronger. Side note: The barrier to entry in Data Science is high. It is difficult to tick all the checkboxes and expectations a recruiter has for their Data Scientist role. So being prepared and having the patience is the key to making a career in Data Science. And most important of all… don’t stop growing.

A great way to stay motivated, that certainly helped me, was listening to the SuperDataScience podcast whenever I got the chance. And I would highly recommend it to you as it will give you an idea about the different kinds of careers that are possible in Data Science. It will inform you about the different kinds of people that have made a successful career in the field and how they leveraged their diverse skills and background to solve Data Science problems. It will give you a blueprint as to how you can use your unique skills to make a difference. This, along with your practice projects will give you a better idea as to what your area of expertise is, may it be making industry level visualizations in Tableau or predictive models in R/Pythons using complex Machine Learning algorithms. The world is your oyster. Although for many roles, there is more emphasis on quant or math-heavy backgrounds, there are tons of opportunities and roles for people with varied backgrounds and skills as Data Science is one of the fastest growing fields in the world.

So, I wish you the best of luck and hope you find your niche!

#keeplearning