Python training in Data Science

Python is one of the leading programming languages for data analysis. In our trainings, we give you a practical insight into the functional range of the universal language in the field of analytics. Learn how you can implement the entire data science workflow with Python – from data management and the development of analysis models to data visualization.

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Our Python trainings

Introduction to data science with Python

The basic training in Python with the focus on data science is the ideal introduction for analysts to the programming language. In addition to programming paradigms, object types and syntax structures and pandas, the central library for data management. Through the training the participants will be able to implement the data management as well as the first analyses and visualizations in Python.

Table of contents:

  • First steps into Python
  • Concept and philosophy of Python
  • Data structures and their properties
  • Importing data
  • Data management with pandas
  • Data analysis with Python
  • Loops and control elements

Recommended course length: Two days

Machine learning with Python

The aim of the course is to learn machine learning methods by applying them to practice-oriented exercise data sets. During the training, the central steps such as preparatory data management, training of algorithms as well as forecasting and validation are learned and directly implemented in Python. A special focus is put on the Python library scikit-learn, which includes a variety of popular algorithms in the field of machine learning.

During the course, participants will create Python scripts which can be used as templates for their own machine learning applications.

Table of contents:

  • Introduction to the basic concepts of machine learning
  • Dealing with the machine learning framework scikit-learn
  • Introduction to machine learning algorithms such as decision trees, support vector machines or random forests
  • Creation of training and test data
  • Parameter tuning of the models with the help of cross-validations
  • Presentation of relevant processing steps such as one-hot encoding, standardization or imputation
  • Presentation of different metrics of model evaluation
    • For classifications: (Balanced) Accuracy, Sensitivity, Specificity, Area under the curve
    • For regressions: RMSE, MAE
  • Linking of preparation and modeling steps in pipeline objects

The course is aimed at people who have already had some programming experience with Python and have a basic understanding of statistics.

Recommended course length: Two days

Time series analysis with Python

With Python, patterns and structures in data with time dimensions can be recognized and thus reliable forecasts can successfully derive from them. By using practice-oriented data sets, you will learn these analysis steps in our course on time series analysis with Python.

We will introduce you to the background of time series analysis and show you the most important approaches and methods for visualizing, smoothing and predicting time series in Python.

After the course you will be able to use time series analysis with Python independently in your daily work.

Table of contents: 

  • Visualization and decomposition of time series
  • Aggregation and stationary processes of time series
  • Calculation of ARIMA and SARIMA models
  • Calculation of exponential smoothing models
  • Calculation of regressions with SARIMA errors
  • Forecasting

Recommended course length: One day

Text mining with Python

In this course “Text mining with Python” you will learn the whole process of analyzing text data. Import, preparation, analysis: by using practical examples, we will teach you all relevant steps of text mining with Python.

Table of contents:

  • Importing text data into Python
  • Introduction to the use of regular expressions
  • Preparation of text data:
    • Removal of characters / irrelevant words
    • Checking of spelling mistakes
    • Tokenization and n-grams
    • Comparison of different approaches to lemming and stemming
  • Calculation of word frequencies and term frequency-inverse document frequencies
  • Part-of-speech tagging and Named-entity recognition
  • Introduction to sentiment analysis
  • Introduction to topic modeling
  • Introduction to text classification

The course introduces the three leading Python libraries nltk, spacy and gensim.

Recommended course length: Two days

Data visualization with Python

In this course you will learn how to visualize your data in an informative and appealing way with Python. By using application-oriented examples, we will provide you with all the necessary knowledge about the Python libraries Matplolib and Seaborn in order to implement complex graphics quickly and easily.

In addition to the creation of basic visualizations, various customization options are covered. These can be used to shape the appearance of the graphic according to personal requirements. General techniques and methods of visualization will be taught, too.

Table of contents:

  • Graphics with Matplotlib and Seaborn
    • Various one- and two-dimensional graphic types
    • Customization of graphics through individual elements
    • Control of the appearance
    • Graphic export


  • Visualization
    • Principles for creating good graphics
    • Tips & tricks
    • Storytelling

Recommended course length: One day

Date and time

You can book all courses as individual trainings at a suitable date of your choice.


We offer you the possibility to conduct all trainings at your site or via remote.


Please contact us, we will be pleased to submit you an individual offer for your training.

Your contact person for this topic:
Meltem Hekim

Portraitfoto Meltem Hekim