Introduction to Data Science with Python
Welcome
Welcome to the course website for
Introduction to Data Science with Python
This website serves as the central repository for all course materials. Here, you will find all slides, lecture materials, and links to your online development environment.
Course Objective
Data analysis plays a critical role in many academic disciplines, and the Python programming language has become one of the standard tools within the Data Science community. This course will introduce programming with Python and how to use it for data analysis. After successfully completing this course, you will be able to understand the fundamentals of the Python programming language. This skill set includes basic data analysis by data wrangling, data visualization, and implementing simple statistical models in Python.
Our goal is to show you the scope of possibilities within Python and leave you with the impression that you can confidently implement your own empirical projects in Python.
Course Description
This course aims at Python beginners. Hence, we will cover the fundamentals of programming and Python, such as variables, loops, and logic statements, before we dive into the topic of Data Science. This course will not cover deeper statistical or theoretical concepts as we focus on applied coding.
This course introduces:
- Syntax and basics of Python
- How to use Notebooks as a development environment
- Data analysis, data wrangling, and data visualization using
numpy,pandasandmatplotlib - Introduction to implementing simple statistical models in Python with
scikit-learnand a preview of neural networks intensorflow
Course Organization and Schedule
The courses is structured in four parts. The four parts are based on each other but can also be used as reference by people with prior experience with Python.
| Part | Content |
|---|---|
| 1 | Course Introduction and Case Study |
| 2 | Fundamentals of Python |
| 3 | Data wrangling and visualizations using pandas and matplotlib |
| 4 | Advanced visualizations with seaborn, modeling with scikit-learn |
We want you to make your hands dirty — that means we want you to code! Just following along fancy slides won’t magically transfer the skill of coding to you. But you actively engaging with the course content in your development environment will more likely do just that.
That’s why we need you to prepare accordingly: Please ensure that you have access to Google Colab before the course. We will use Google Colab for the coding parts, such that we can use Python without (sometimes time-consuming) pre-configuration or installation on your machine. To use Google colab, you need a Google account (same account which is used for Gmail, YouTube, etc.)
If you have any questions, please reach out to one of us through the e-mail addresses on the bottom of this page.
Acknowledements
Thanks to Felix Schneider (Github), who initially developed this course and granted permission adapt and use the course materials.
The case study used as motivation is built upon material from datasciencebox.org by Mine Çetinkaya-Rundel.