Training: 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

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Recommended course length: Two days

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.

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Recommended course length: Two days

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

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Recommended course length: One day