200+ Python and Data Science Tips — Covering Pandas, NumPy, ML Basics, Sklearn, Jupyter, and More
<p>Being a data scientist demands expertise in plenty of areas. You need to be good at using appropriate tools, like Pandas, NumPy, Sklearn, etc.</p>
<p>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.</p>
<p>What’s more, SQL is pivotal to almost all data science roles today.</p>
<p>Additionally, data storytelling is equally essential to effectively convey your findings and insights to a broader audience.</p>
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<p>Data Science Toolkit (Image by Author)</p>
<p>One must also possess a firm understanding of statistics to perform data analysis and make data-driven decisions.</p>
<p>And of course, you can never forget ML fundamentals.</p>
<p>All in all, it’s a lot, isn’t it?</p>
<p>But it’s fun. A lot of fun, in fact.</p>
<p>To simplify this data science journey and make it appear less intimidating and more accessible, I have been sharing daily tips for over 200 days.</p>
<p>And after completing 200 days, I made a full PDF archive, which lists all the posts I have written.</p>
<p><a href="https://medium.datadriveninvestor.com/200-python-and-data-science-tips-covering-pandas-numpy-ml-basics-sklearn-jupyter-and-more-b657f039e89e">Click Here</a></p>