Data Analysis with polars

In this course, you will guide a crew of polar bears on a research expedition through arctic underwater worlds. Keep their vessel on course, solve icy data problems and you may eventually find traces of the fabled penguins.

Goals

You will learn to analyze a tabular dataset using the Python programming language. The course introduces you to polars, a modern component of the Python data science stack. You will wrangle challenging data sets, calculate statistical metrics, create enticing plots and combine your results into a live dashboard. The course discusses typical data biases and statistical tools that everybody working with data should have in their toolbelt.

The main goal of the course is to enable you to quickly summarize a dataset and communicate your findings clearly.

Topics

Part 1: Working with Tabular Data

The Python Data Science Ecosystem	Get the Python stack set up and inspect your first data files
Read and Write Data	import and export data in CSV, Excel and JSON format
Select Rows and Columns	apply simple filters and complex selection logic
Data Cleaning	merge tables, fix inconsistencies and deal with missing data in tables

Part 2: Data Visualization

High-Quality Plots	create standard data visualizations
Pivot Tables	create custom aggregations and visualize them
Time Series	apply a standard procedure for time series decomposition
Dashboards	create a visually appealing report with streamlit

Part 3: Statistical Tools

Descriptive Statistics	summarize numerical data using univariate metrics like mean, median and standard deviation
Correlation	calculate bivariate statistics and assess their meaning
Distributions	identify common distribution functions through histograms and tests
Data Ethics	common pitfalls resulting in biased or misleading results

data analysis tools — some statistical and data visualization tools

How the course works

The course consists of interactive lectures. During the lectures, we will discuss key concepts from the field of data science and run example code using Python, polars and seaborn. There will be good and weak examples of data analyses so that you learn to critically observe data presented to you.

The course material contains plenty of exercises that you are expected to work on their own to build practical data processing skills in Python/polars. During classes, you get support with technical questions. You are encouraged to bring your own questions and bugs found during the practical exercises. The course materials are permanently available on under a Creative Commons 4.0 License.

Prerequisites

To participate in the course, fluency in Python is useful but not strictly required. You should be able to write simple instructions and debug smaller pieces of Python code. To work on the exercises and try out the examples, you need to be able to execute code in a Jupyter Notebook. Writing clean code and keeping Jupyter Notebooks tidy matters and is more important than knowing lots of Python.

There is little need for structured programming, loops, functions or classes when exploring data. No programming outside Jupyter is required for this module. No knowledge on advanced statistics, databases or Machine Learning is required for this course.

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