Objective:
Introduction to programming with Python

Description:
Participants familiarize themselves with the interactive learning environment – the StackFuel Data Lab – and the Python programming language.

Chapter 1 – Python Basics:
Participants navigate through the Data Lab for the first time and get to know the basics of programming. They learn to store numbers and text as variables in Python and to store these as groups in lists. To complete these basics, participants also learn how to read error messages correctly.

Chapter 2 – Programming Basics:
Participants continue to build their fundamental programming skills. This chapter focuses on applying functions and, as well as conditional flow controls.

Chapter 3 – Loops and Functions:
The last chapter of the basics module is dedicated to flow control using loops. Participants broaden their abilities by importing additional Python packages and gain insight into code versioning with Git. By the end of the chapter, participants know the most important programming concepts that are important working as a data analyst.

Objective:
Independent collection, analysis and visualization of data
with Python


Description:
Participants learn to access, filter, and merge new data sources.
They practice making company data available in attractive
visualizations tailored to the target audience, and independently
carry out classic data processing (importing, filtering, cleaning,
and visualizing data).


Chapter 1 – Data Pipelines (Pandas):
This chapter teaches the efficient use of Pandas – the standard
data analysis tool in Python. Participants learn to use it to read,
clean, and aggregate data in CSV files.


Chapter 2 – Data Exploration (Matplotlib):
Participants practice visualizing different types of data using
marketing data. Numeric data is represented as histograms and
scatter plots, while categorical data is represented as column
and pie charts.


Chapter 3 – Predictions (Statistics):
Participants learn statistical concepts such as the median and
quartiles using product ratings. They identify outliers and make
simple predictions using linear and logistic regression.


Chapter 4 – Internal Data (SQL):
Participants learn to read databases using a human resources
database as an example and formulate standard SQL queries.


Chapter 5 – External Data (API):
Participants use Python to access information such as web pages
and APIs designed by StackFuel on the Internet.


Chapter 6 – Advanced Jupyter:
Participants learn Jupyter functionalities and solve advanced
visualization problems such as live updates and interactivity in
the context of a stock market scenario.


Chapter 7 – Exercise Project:
Participants analyze a New York taxi data set with over one million
trips and use their Python skills as independently as possible to
answer certain questions.


Chapter 8 – Final Project:
Participants analyze customer churn for a telecommunications
company. They work through the entire data pipeline
independently and answer typical questions. They then present
their project in a 1-on-1 feedback session with the StackFuel
mentor team