This repository contains all the links to colab notebooks (as well as jupyter notebooks) used in the Python for Data Science Bootcamp by Turing Students Rotterdam. Python for Datascience is a 2-week, intensive bootcamp taught at Erasmus University Rotterdam. It teaches the basics of programming in Python, with a focus on the applications in Data Science.
- What are Jupyter Notebooks
- Basic python syntax
- String variables & formatting
- Boolean operators
- If statements
- Functions
- Challenges
Colab notebook of Session 1 including challenges.
To use the notebook, click on the link > open in playground > save copy in drive. Now you have your own version of the notebook and can customise it however you want it.
- Recap Session 1
- Lists and Tuples
- Loops
- List comprehensions
- In-class project
- Challenges
- Introduction to Object-oriented progamming (OOP)
- Imperative & Declarative Programming
- Classes and Objects
- Methods, Attributes, Init, Self
- Inheritance
- Super
- Challenges
- Recap Session 3
- Going through OOP challenges from students
- Data Analysis with Pandas
- Exploring Numpy
- Challenges
Datasets
- Charts and variables
- Plot data with matplotlib
- Exploring a big dataset
- Visualising features of the data
Save your boss
It is a Thursday afternoon in the Drieloitte office, 17.46 to be precise. As it was a busy week, you are about to pack your stuff and have a beer with a few friends. But then your boss walks towards you, slightly panicking. That doesn’t look good, you think to yourself. “I need you to do a data analysis right now” he says. “We promised our very important client Woogle to have their data explored and analysed by this morning. Now, our best Data Scientist Ben quit his job this morning! We didn’t know what to do but then I remember you did this awesome Data Science course with Python. Can you please help us out??”
For a moment, you think about your beer in the pub, and the struggles this challenge will bring you. But then, you turn around and say with a determined voice: “Let’s explore that dataset.”
You can choose between 4 datasets;
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The Kaggle Survey dataset used in Session 5
Your task is to pick one of the above datasets and load it in ( kaggle / colab / jupyter notebook ). And do a data analysis where you explore the most important features of the dataset. Make sure that you visualise what you find and also interpret it (i.e. explain your findings).
We will pay attention to the techniques you use, libraries, whether you comment your code, and explain what you are doing. You are free to use any resources on the internet, such as the notebooks from the sessions, and stackoverflow. And remember, be creative. You want to show the skills you have learned in the last two weeks.
Bonus ( only if you have time left )
Remember the creative challenge from session 3?
Here you can create any type of class you want! The only criteria are that your class needs is use;
one parent class two child classes 4 attributes 3 methods inheritance #End of the exam
Happy coding and good luck!*
I believe that to improve your coding skills, you need some type of goal or project that makes sure you will keep coding. There are many great resources out there, here I will provide you with some tips and resources that will set you up for success.
Then codewars is for you. Codewars is a platform full of short challenges called "katas". They are sorted by difficulty and cover various topics such as logic, algortihms, math, games and more. As you get points per solved kata, it is addicting! Another cool feature is that you get to see other people's solutions after solving the problem.
The best way is to just start. Of course you will get stuck, but you are (almost) never the only one with a problem. You just need motivation, time and stackoverflow. But if you like to work with a book, I'd recommend automate the boring stuff
Our own Machine Learning Bootcamp, Bletchley, starts in February. Other than joining that you could:
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- Make a Kaggle account and start participating in competitions. Apply your knowledge!
The materials of Python for Data Science were created and taught by; Patrick, Alexandru and Daphne