Statistics for data science

• Introduction to statistics for data science: Learn how statistics is used in the context of data science and machine learning and get an overview of statistical techniques.

• Data types and data collection: Explore different data types, methods for data collection and strategies for data quality assurance.

• Descriptive Statistics: Learn the basics of descriptive statistics, including measures of central tendency and dispersion.

• Probability: Dive into the world of probability distributions and random variables and apply probability theory to real-world problems.

• Probability theory: Learn to calculate with probabilities and understand basic concepts such as Bayes' theorem and the central limit theorem.

• Hypothesis testing: Understand and perform statistical tests, interpret results and recognize risks of error.

• Confidence intervals: Calculate and interpret confidence intervals and apply them in data science practice.

• Machine Learning with Statistical Methods: Combine statistical techniques with machine learning and explain supervised and unsupervised learning with statistical concepts.

Duration of the course: 2 days

After completing the course, you will have the statistical skills necessary for a deep understanding of the models used in data science. You will be able to perform data analysis, test hypotheses and use advanced statistical models for forecasting and decision making.