Docker

• Introduction to Docker (16.04.2023)
• Containerization and Virtualization Concepts (06.09.2024)

Excel

• Data Analysis in Excel (31.05.2020)

GIT

• Introduction to Git for Data Science (10.10.2018)

Julia

• Introduction to Julia (14.12.2022)

Kafka

• Introduction to Kafka (01.07.2024)

Python

• Advanced Deep Learning with Keras in Python (12.12.2018)
• AI Fundamentals (03.06.2021)
• Analyzing IoT Data in Python (15.11.2020)
• Analyzing Police Activity with pandas (30.01.2021)
• Big Data Fundamentals with PySpark (21.03.2020)
• Building Recommendation Engines with PySpark (27.05.2020)
• Cleaning Data in Python (25.01.2021)
• Cleaning Data with PySpark (19.03.2020)
• Cluster Analysis in Python (24.05.2021)
• Data Manipulation with pandas (24.09.2020)
• Data Types for Data Science in Python (14.05.2021)
• Deep Learning in Python (03.12.2018)
• Exploratory Data Analysis in Python (28.01.2021)
• Extreme Gradient Boosting with XGBoost (23.05.2021)
• Feature Engineering with PySpark (29.03.2020)
• Intermediate Data Visualization with Seaborn (08.12.2020)
• Intermediate Importing Data in Python (13.01.2021)
• Intermediate Python for Data Science (21.11.2018)
• Introduction to Data Engineering (22.03.2021)
• Introduction to Data Science in Python (29.07.2020)
• Introduction to Data Visualization with Matplotlib (05.08.2020)
• Introduction to Data Visualization with Plotly in Python (24.02.2021)
• Introduction to Data Visualization with Seaborn (24.11.2020)
• Introduction to Databases in Python (16.02.2021)
• Introduction to Deep Learning with Keras (29.07.2020)
• Introduction to Importing Data in Python (09.01.2021)
• Introduction to PySpark (11.03.2020)
• Introduction to Python (11.11.2018)
• Introduction to Statistics in Python (14.12.2020)
• Introduction to Tensorflow in Python (17.07.2023)
• Joining Data with pandas (31.10.2020)
• Linear Classifiers in Python (18.05.2021)
• Machine Learning for Time Series Data in Python (17.12.2020)
• Machine Learning with PySpark (10.05.2020)
• Machine Learning with Tree-Based Models in Python (22.05.2021)
• pandas Foundations ( 10.09.2020)
• Practicing Machine Learning Interview Questions in Python (19.06.2021)
• Preparing for Statistics Interview Questions in Python (03.09.2019)
• Python Data Science Toolbox (Part 1) (28.08.2020)
• Python Data Science Toolbox (Part 2) (29.08.2020)
• Python for R Users (12.04.2020)
• Recurrent Neural Networks for Language Modeling in Python (17.04.2020)
• Software Engineering for Data Scientists in Python (06.10.2020)
• Statistical Simulation in Python (09.01.2021)
• Statistical Thinking in Python (Part1) (20.04.2021)
• Streamlined Data Ingestion with Pandas (17.02.2021)
• Supervised Learning with scikit-learn (24.12.2018)
• Supervised Learning with scikit-learn (21.08.2023)
• Time Series Analysis in Python (07.06.2021)
• Unsupervised Learning in Python (12.01.2019)
• Working with Dates and Times in Python (08.01.2021)
• Working with the OpenAI API (22.09.2023)
• Writing Efficient Python Code (27.07.2021)
• Writing Functions in Python (10.08.2021)

R

• A/B Testing in R (12.04.2019)
• Anomaly Detection in R (07.06.2019)
• ARIMA Modeling with R (04.10.2018)
• Bond Valuation and Analysis in R (31.03.2019)
• Building Dashboards with flexdashboard (07.03.2019)
• Building Dashboards with shinydashboard (27.02.2019)
• Building Web Applications in R with Shiny (22.08.2018)
• Building Web Applications in R with Shiny – Case Studies (04.09.2018)
• Case Study: Exploring Baseball Pitching Data in R (27.08.2020)
• Cleaning Data in R (03.03.2018)
• Cluster Analysis in R (06.12.2021)
• Communicating with Data in the Tidyverse (05.01.2020)
• Correlation and Regression (09.09.2019)
• Data Manipulation with dplyr in R (02.09.2019)
• Data Manipulation with data.table in R (03.06.2020)
• Data Privacy and Anonymization in R (22.11.2019)
• Data Visualization in R (02.01.2020)
• Data Visualization with ggplot2 (Part1) (14.09.2019)
• Data Visualization with ggplot2 (Part2) (08.07.2020)
• Data Visualization with ggplot2 (Part3) (19.07.2020)
• Defensive R Programming (29.09.2022)
• Developing R Packages (17.11.2019)
• Exploratory Data Analysis (27.10.2018)
• Feature Engineering in R (25.11.2021)
• Forecasting Product Demand in R (14.10.2018)
• Forecasting Using R (05.10.2018)
• Foundations of Probability in R (13.06.2019)
• Foundations of Inference (06.09.2019)
• Fundamentals of Bayesian Data Analysis in R (09.06.2019)
• Importing and Cleaning Data in R: Case Studies (02.07.2019)
• Importing and Managing Financial Data in R (30.03.2019)
• Importing Data in R (Part 1) (28.02.2018)
• Importing Data in R (Part 2) (20.04.2018)
• Inference for Categorical Data (07.09.2019)
• Inference for Numerical Data (11.09.2019)
• Interactive Data Visualization with plotly in R (29.06.2019)
• Intermediate Data Visualization with ggplot2 (09.10.2022)
• Intermediate R (26.02.2018)
• Intermediate R – Practice (05.03.2018)
• Intermediate R for Finance (20.03.2019)
• Introduction to Bioconductor (17.05.2019)
• Introduction to Data (04.09.2019)
• Introduction to Data Visualization with ggplot2 (04.10.2021)
• Introduction to Machine Learning (05.05.2018)
• Introduction to Natural Language Processing in R (11.02.2023)
• Introduction to Portfolio Analysis in R (01.05.2019)
• Introduction to R (24.02.2018)
• Introduction to R for Finance (15.03.2019)
• Introduction to Regression in R (19.11.2021)
• Introduction to Spark in R using sparklyr (25.07.2018)
• Introduction to Statistics in R (17.06.2023)
• Introduction to TensorFlow in R (06.02.2020)
• Introduction to the Tidyverse (14.10.2018)
• Introduction to Time Series Analysis (03.10.2018)
• Linear Algebra for Data Science in R (31.10.2018)
• Machine Learning in the Tidyverse (28.11.2021)
• Machine Learning with caret in R (09.10.2022)
• Manipulating Time Series Data in R with xts & zoo (25.03.2019)
• Object-Oriented Programming in R: S3 & R6 (22.04.2019)
• Optimizing R Code with Rcpp (27.10.2019)
• Parallell Programming in R (29.11.2019)
• Probability Puzzles in R (17.06.2019)
• Programming with dplyr (22.09.2022)
• Reporting with R Markdown (28.02.2019)
• Reshaping Data with tidyr (22.04.2023)
• Statistical Modeling in R (Part 1) (13.11.2018)
• String Manipulation in R with stringr (14.12.2019)
• Supervised Learning in R: Classification (08.03.2019)
• supervised Learning in R: Regression (20.04.2023)
• Text Mining: Bag of Words (01.06.2019)
• Unsupervised Learning in R (20.06.2019)
• Visualization Best Practices in R (23.07.2019)
• Web Scraping in R (22.12.2022)
• Working with the RStudio IDE (Part 1) (08.10.2018)
• Working with the RStudio IDE (Part 2) (09.10.2018)
• Working with Web Data in R (07.10.2018)
• Writing Efficient R Code (05.10.2019)
• Writing Functions in R (15.03.2019)

Scala

• Introduction to Scala (29.04.2020)

Shell

• Conda Essentials Course (28.01.2019)
• Introduction to Bash Scripting (05.08.2021)
• Introduction to Shell for Data Science (13.10.2018)

SQL

• Database Design (20.06.2021)
• Intro to SQL for Data Science (23.01.2019)
• Joining Data in SQL (31.01.2019)
• Introduction to Relational Databases in SQL (14.09.2019)
• Introduction to SQL Server (28.03.2021)

Tableau

• Introduction to Tableau (04.07.2020)

Theory

• AWS Cloud Concepts (22.02.2022)
• Cloud Computing for Everyone (30.08.2020)
• Communicating Data Insights (26.10.2022)
• Dashboard Design Concepts (20.04.2023)
• Data Communication Concepts (15.07.2022)
• Data-Driven Decision Making for Business (12.06.2021)
• Data Engineering for Everyone (10.05.2020)
• Data Science for Everyone (24.05.2020)
• Data Science for Managers (30.06.2019)
• Data Storytelling Concepts (20.03.2023)
• Data Visualization for Everyone (28.05.2020)
• Generative AI for Business (02.10.2023)
• GitHub Concepts (29.10.2022)
• Implementing AI Solutions in Business (09.07.2023)
• Introduction to ChatGPT (23.03.2023)
• Introduction to Data (06.02.2023)
• Introduction to Data Literacy (19.10.2022)
• Introduction to Data Quality (01.04.2023)
• Introduction to Data Warehousing (28.01.2023)
• Introduction to DevOps (17.02.2023)
• Introduction to Programming Paradigms (19.02.2023)
• Introduction to Statistics (11.08.2022)
• Large Language Models (LLMs) Concepts (01.07.2023)
• Large Language Models for Business (24.11.2023)
• Machine Learning for Business (20.04.2020)
• Machine Learning for Everyone (25.05.2020)
• Marketing Analytics for Business (13.10.2022)
• MLOps Concepts (31.12.2022)
• MLOps Deployment and Life Cycling (24.12.2022)
• MLOps for Business (27.12.2022)
• NoSQL Concepts (22.12.2021)
• Streaming Concepts (01.11.2022)
• Understanding Artifical Intelligence (04.08.2023)
• Understanding Modern Data Architecture (09.07.2024)
• Understanding the EU AI Act (18.08.2024)