Predictive analytics: data-based forecasts with AI and prediction models

1. why time series help to understand reality

• The concept of predictive analysis with time series
• The four facets of time series analysis
• The role of variables in everyday issues
• The time series metrics

2. regression models in detail

• Statistics and dealing with perspectives
• Predictions through regression
• Multivariate regression
• How does a regression model learn?

3. predictions with neural networks

• Introduction to neural networks
• Activation functions for neurons
• Create deep neural networks
• Deep versus wide learning

4. predictions with Transformer models

• Forecasting process
• Making data visible to machines
• Attention control
• Softmax function and causality

How do you learn in this course?

This course offers you a digital blended concept that has been developed for part-time learning. You will learn in a combination of self-study units, live webinars and practical exercises. With a time budget of at least 3-4 hours per week, you are sure to reach your goal: 

Self-study phases: Learn independently, at your own pace and whenever you want. Our courses offer you didactically high-quality learning material with clear textbooks, interactive exercises, quizzes and learning checks.

Live webinars: In regular online seminars, you will meet your trainers in person. You will receive answers to your questions, specific assistance and instructions on how to deepen your knowledge and apply the skills you have acquired in practical exercises.

Practical tasks: In order to learn the skills in practice, you will be given access to Jupyter notebooks, which will enable you to apply the knowledge you have learned in practice yourself. Through these exercises, you will gain a deep understanding of working with data and develop methods and techniques that you can apply in your everyday work.

Learning community: A digital learning community is available to you throughout the course. Exchange ideas with other participants and the trainers and ask questions.

Certificate of attendance and Open Badge: As a graduate of the course, you will receive a certificate and an Open Badge that you can easily share in professional networks (e.g. LinkedIn).

The following third-party tool is used in the training event :

• Jupyter Notebooks

To participate, you should have the following prior knowledge:

• Basic knowledge of Python syntax (variables, operators, loops, conditions, functions, etc.)
• Basic mathematical knowledge of vector calculus
• Knowledge of PyTorch and scikit-learn is not required, but is an advantage.

To participate, we would like to ask you to make the following preparations:

• Install Python 3 on your computer.
• Set up the virtual environment "venv" and activate "venv".
• Install Jupyter Notebooks on your computer.
• Install the numpy, pandas and matplotlib libraries.
• Create a new notebook in Jupyter Notebooks and import the libraries.

These steps will set up Python 3 with a virtual environment in which Jupyter Notebooks and the numpy, pandas and matplotlib libraries will be installed and available for you to use in the course.