250+ Python and Data Science Tips — Covering Pandas, NumPy, ML Basics, Sklearn, Jupyter, and More.
Being a data scientist demands expertise in plenty of areas. You need to be good at using appropriate tools, like Pandas, NumPy, Sklearn, etc.
These are indispensable to the development life cycle of many data-driven projects, making them essential skills to begin/maintain a career in data science.
What’s more, SQL is pivotal to almost all data science roles today.
Additionally, data storytelling is equally essential to effectively convey your findings and insights to a broader audience.
One must also possess a firm understanding of statistics to perform data analysis and make data-driven decisions.
And of course, you can never forget ML fundamentals.
All in all, it’s a lot, isn’t it?
But it’s fun. A lot of fun, in fact.
To simplify this data science journey and make it appear less intimidating and more accessible, I have been sharing daily tips for over 250 days.
And after completing 250 days, I made a full PDF archive, which lists all the posts I have written.
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