10 tips for data science beginners
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Transcript#
This transcript was generated automatically and may contain errors.
These are 10 tips for beginners starting with Python and data science. Number one is to start messy and then get better. You don't need to write perfect code. You just need to get things working. You'll learn how to clean it up later.
Number two is to use Jupyter Notebooks for learning. Jupyter is a beginner friendly because it lets you run code in chunks and see output instantly.
Number three is AI is your coding copilot. Literally everyone Googles error messages. So don't be afraid to copy code as long as you're trying to understand it too.
Number four is to start with real data. Don't waste too much time on toy data sets. Grab something interesting from Kaggle, UCI, or Tidy Tuesday and try to answer a question you care about.
And number five is to master these libraries early. You got to get comfortable working with Pandas, Matplotlib, Seaborn, and scikit-learn.
Number six is build one solid portfolio project. Pick a data set, define a problem, clean it, analyze it, and maybe try a basic model.
Number seven is GitHub is your resume. Upload your notebooks or projects and then write a simple readme. The recruiters will look at this.
Number eight is don't rush into deep learning. Start with statistics, regression, or decision trees. You'll understand neural networks way better if you have a strong foundation.
You'll understand neural networks way better if you have a strong foundation.
Number nine is learn by explaining. Make slides, record a loom, or write a short blog post about something you learned.
And lastly, number ten is to join a data science community. There are plenty of Slack groups, school clubs, discords, and being around others on the same path will help level you up faster.
Leave any questions in the comments below and follow along for more data science content. Bye!
