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An intro to Python & programming for wanna-be data scientists
| Date | Stars |
|---|---|
| 2026-07-24 | 981 |
| 2026-07-25 | 981 |
| 2026-07-28 | 981 |
| 2026-07-30 | 981 |
| 2026-08-06 | 981 |
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# An Introduction to Python and Programming
This project is a *thorough* introductory course
in programming with **[Python <img height="12" style="display: inline-block" src="static/link/to_py.png">](https://www.python.org/)**.
### Table of Contents
The following is a high-level overview of the contents.
For a more *detailed version* with **clickable links**
see the [CONTENTS.md](CONTENTS.md) file.
- *Chapter 0*: Introduction
- **Part A: Expressing Logic**
- *Chapter 1*: Elements of a Program
- *Chapter 2*: Functions & Modularization
- *Chapter 3*: Conditionals & Exceptions
- *Chapter 4*: Recursion & Looping
- **Part B: Managing Data and Memory**
- *Chapter 5*: Numbers & Bits
- *Chapter 6*: Text & Bytes
- *Chapter 7*: Sequential Data
- *Chapter 8*: Map, Filter, & Reduce
- *Chapter 9*: Mappings & Sets
- *Chapter 10*: Arrays & Dataframes
- *Chapter 11*: Classes & Instances
#### Videos
Presentations of the chapters are available on this [YouTube playlist <img height="12" style="display: inline-block" src="static/link/to_yt.png">](https://www.youtube.com/playlist?list=PL-2JV1G3J10kRUPgP7EwLhyeN5lOZW2kH).
The recordings are about 25 hours long in total
and were made in spring 2020
after a corresponding in-class Bachelor course was cancelled due to Corona.
### Objective
The **main goal** is to **prepare** students
for **further studies** in the "field" of **data science**,
including but not limited to topics such as:
- algorithms & data structures
- data cleaning & wrangling
- data visualization
- data engineering (incl. SQL databases)
- data mining (incl. web scraping)
- linear algebra
- machine learning (incl. feature generation & deep learning)
- optimization & (meta-)heuristics (incl. management science & operations research)
- statistics & econometrics
- quantitative finance (e.g., option valuation)
- quantitative marketing (e.g., customer segmentation)
- quantitative supply chain management (e.g., forecasting)
- web development (incl. APIs)
### Prerequisites
To be suitable for *beginners*, there are *no* formal prerequisites.
It is only expected that the student has:
- a *solid* understanding of the **English** language,
- knowledge of **basic mathematics** from high school,
- the ability to **think conceptually** and **reason logically**, and
- the willingness to **invest** around **90-120 hours** on this course.
## Getting started
If you are a total beginner,
follow the instructions in the "Installation" section next.
If you are familiar with
the [git](https://git-scm.com/)
and [poetry](https://python-poetry.org/docs/) command-line tools,
you may want to look at the "Alternative Installation" section further below.
### Installation
To follow this course, an installation of **Python 3.11** or higher is expected.
A popular and beginner friendly way is
to install the [Anaconda Distribution](https://www.anaconda.com/download)
that not only ships Python itself
but also comes pre-packaged with a lot of third-party libraries.
<img src="static/anaconda_download.png" width="50%">
Scroll down to the "Anaconda Installers" section
and install the latest version for your operating system
(i.e., *2024-02* with Python 3.11 at the time of this writing).
After installation,
you find an entry "[Anaconda Navigator](https://docs.anaconda.com/free/navigator/)"
in your start menu.
Click on it.
<img src="static/anaconda_start_menu.png" width="50%">
A window opens giving you several options to start various applications.
In the beginning, we will work mostly with [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/).
Click on "Launch".
<img src="static/anaconda_navigator.png" width="50%">
A new tab in your web browser opens:
The website is "localhost" and some number (e.g., 8888).
This is the [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) application
that is used to display the course materials.
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