250+ 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>
<p><img alt="" src="https://miro.medium.com/v2/resize:fit:700/1*ECXtukKnaen91UztCmPnXA.png" style="height:363px; width:700px" /></p>
<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>
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