Data Science with Python - Introduction to Python

Course overview

Introduction Brief background on Python and Anaconda Installing new packages Working with Jupyter Lab Introduction to the Data Science package pandas Elements of a pandas data.frame Accessing, adding, deleting, and modifying a row/column Boolean indexing Creating an empty data.frame Overview of methods to adjust a data.frame (changing column names, replacing values, sorting a data.frame) Basic statistics with pandas Overview of statistics that can be calculated with a data.frame (number of valid observations, sum, mean, median, minimum, maximum, variance,...) Calculating a contingency table Grouping analyses with groupby Filling in missing values Reading data Setting the working directory in Python Reading and writing a CSV and overview of the most useful parameters for this Python's pickle module for saving Python objects Converting different data types (integer, unsigned integer, float, string) Visualization with matplotlib and seaborn The main elements when plotting with matplotlib Customizing a plot (changing x and y axes, labels, legend, and title) and saving Choosing colors Creating scatter plots, line plots, bar plots, box plots with seaborn Control Flows The range() function Writing a custom function Setting default parameters in a function For loops If-Else conditions Logical comparison operators Assignment operators Overview of Machine Learning What is Machine Learning Classification of algorithms in Machine Learning (supervised learning - unsupervised learning - reinforcement learning) Overfitting, Train-Test-Split, and cross-validation Machine Learning Algorithms (Theory and Practice) Introduction to the algorithm (Linear Regression, Decision Tree) Train-Test Split of the data Implementing the algorithm in Python Validating the results (including confusion matrix, sensitivity, accuracy) Optional: Additional Machine Learning Algorithms in Python Explanation of Random Forest and K-means Implementing these algorithms in Python Validating the results Cross-validation

Upcoming dates

• 2026-10-27T23:00:00.000Z · 2026-10-29T23:00:00.000Z · Köln · Seminar
• 2027-01-24T23:00:00.000Z · 2027-01-26T23:00:00.000Z · Online-Seminar
• 2027-01-24T23:00:00.000Z · 2027-01-26T23:00:00.000Z · Köln · Seminar
• 2027-02-21T23:00:00.000Z · 2027-02-25T23:00:00.000Z · Online-Seminar