Data Science with Python
Learn how to use Python in the context of Big Data and Machine Learning
Python is one of the leading programming languages for data analysis. In our Python training courses we give you practical insights into the functional scope of the general-purpose programming language. Learn from us how you can implement the entire data science workflow with Python, from data management and the development of analysis models to data visualization. We are currently offering two different Python courses:
Introduction to Data Science with Python
The basic training in Python focused on data science is the ideal introduction to the programming language for analysts. Topics include programming paradigms, object types and syntax structures as well as the central library for data management called pandas. The training will enable participants to implement data management as well as initial analyses and visualizations in Python.
- The concept and philosophy of Python
- Overview of the Python editors, e.g. Jupyter, Pycharm, Spyder
- Creating Python projects: folder structure, installing packages and modules
- The Python hierarchy: packages, modules, classes, function constructs and source code
- Data structures and their properties
- Functions and control structures
- Access to object orientation: classes, methods and attributes
- Object types: series and data frame
- Data management, descriptive statistics, first visualizations
Machine Learning with Python
The aim of this course is to master machine learning methods by applying them to practice-oriented exercise data sets. During the training, participants will learn central steps such as the preparatory data management, the training of algorithms as well as forecasting and validation and implement them directly in Python. A special focus of the course is on the Python library scikit-learn, which includes a variety of popular machine learning algorithms.
Participants will create Python scripts which they can use as templates for their own machine learning applications.
- Basics of machine learning / data mining
- Overview of models and methods, of the problem of forecasting
- Supervised vs. unsupervised learning
- Overfitting, underfitting and parameter tuning – modeling techniques
- Basic problem, simple cross-validation, 3-fold technique, k-fold validation, cross-validation for time series
- Classification methods
- Decision Trees, Random Forest, Gradient Boosting Machines, Neural Networks, Support Vector Machines
- Evaluation of classification procedures (ROC curves, cut-off value, precision, sensitivity, specificity)
- Regression problems
- Linear Regression, Regression Trees, Neural Networks, Support Vector Machines, Random Forest
- Evaluation of regression problems
- Cluster analyses
- k-nearest neighbors, k-means, agglomerative cluster analysis, visualizations
The two Python courses are available in English as in-house trainings. Please contact us – we will be happy to make you an individual offer.