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Data Science Fundamentals with Python

Data Science Fundamentals

Table of contents

Introduction

This was a course I (Hay Kranen) developed for the Master Data Driven Design at the University of Applied Sciences Utrecht.

The basis is a book with 25 chapters that gives an introduction course to data science using the Python programming language in the form of Jupyter Notebooks. It assumes technical skills from the student, but no previous programming experience is required.

There are also some slides i gave during the course and more examples. This whole course is licensed under an open license.

To use this course i recommend installing the Anaconda Distribution.

Essentials

Slides

These are PDF versions of the slides i gave during classes.

Resources and tips

Feel free to fork this file and add more resources!

Python

Git and the terminal

Tips

  • Google and Stack Overflow are your friends. It’s not a shame to Google even really basic concepts. I have been programming for more than twenty years and i still Google really basic stuff every single day.
  • Your code should be properly commented (use #). Good commenting means you explain why you do something, not what you’re doing.
  • Keep your code DRY: Do not Repeat Yourself. If you copy-paste code, you probably could use a function instead.
  • Keep it tidy! Python is a language where indentation matters. This means that if you don’t format your code properly it won’t work.
  • Make sure you all your code is correctly spelled. Python is very picky! If you write If instead of if (note the capital) your code won’t work.
  • If you don’t understand why something isn’t working, try to make an example that is as simple as possible to pinpoint the cause.
  • Pay close attention to the error output you get when you run a command. Google it if you don’t understand it.
  • Note that Python is case sensitive and most file systems are too. When you create a new file only use lowercase characters, no spaces (use the underscore _ instead).
  • Read all the comments in the examples i provide. 90% of the things asked in the assignments are already solved for you there.

Miscellaneous

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Introduction course to data science using the Python programming language in the form of Jupyter Notebooks

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