Learn how to compute and interpret some of the most powerful statistical measures across a variety of industries. From mean, median and mode to variance and percentiles, make an impact efficiently in businesses and organizations.

This course introduces the most important methods and concepts from statistics with applications in the R programming language. We cover the fitting of statistical models to data, statistical testing, and prediction.

This course covers techniques from inferential statistics, including hypothesis testing, t-tests, and Pearson’s chi-squared test, along with ANOVA, which is used to analyze effects across categorical variables and the interaction between variables.

Machine Learning is changing the world and at the very core of machine learning are advanced statistical models. With this course, you will know how to create an ML application for problems that appear at your work and understand the basis behind it

Data is everywhere and we always hear about statistics, even if we do not realize it! Over this course we will shape up our statistical knowledge; going from zero to hero analyzing complex patterns of everyday real-world problems.

Learn about what transformers are (the T of GPT) and how to work with them using Hugging Face libraries

Understand the principles of data analysis, including techniques for data collection, cleaning, and interpretation.

Learn how to use Matplotlib to clarify your data with meaningful charts and other data visualizations.

Build basic deep learning models in TensorFlow.

Improve machine learning models with hyperparameter tuning.

Explore inferential statistics methods for making predictions and generalizations based on sample data.

Build the basic language models in Python.