Data science courses

Master Python testing: Learn methods, create checks, and ensure error-free code with pytest and unittest.

Dive in and learn how to create classes and leverage inheritance and polymorphism to reuse and optimize code.

Learn how to implement and schedule data engineering workflows.

Explore data structures such as linked lists, stacks, queues, hash tables, and graphs; and search and sort algorithms!

Learn the fundamentals of working with big data with PySpark.

This course introduces dbt for data modeling, transformations, testing, and building documentation.

Learn the nuts and bolts of LLMs and the revolutionary transformer architecture they are based on!

Apply PyTorch to images and use deep learning models for object detection with bounding boxes and image segmentation generation.

This course focuses on feature engineering and machine learning for time series data.

Learn how to use MLflow to simplify the complexities of building machine learning applications. Explore MLflow tracking, projects, models, and model registry.

Learn how to clean data with Apache Spark in Python.

Prepare for your next coding interviews in Python.

Start your reinforcement learning journey! Learn how agents can learn to solve environments through interactions.

Build your OOP skills with descriptors, multilevel inheritance, and abstract base classes!

In this course, you’ll explore the modern MLOps framework, exploring the lifecycle and deployment of machine learning models.

Learn techniques to extract useful information from text and process them into a format suitable for machine learning.

In this interactive course, you’ll learn how to use functions for your Tableau calculations and when you should use them!

Discover the exciting world of Deep Learning for Text with PyTorch and unlock new possibilities in natural language processing and text generation.

Learn how to manipulate data and create machine learning feature sets in Spark using SQL in Python.

Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.

Learn about ARIMA models in Python and become an expert in time series analysis.

Learn to conduct image analysis using Keras with Python by constructing, training, and evaluating convolutional neural networks.

Learn about risk management, value at risk and more applied to the 2008 financial crisis using Python.

Elevate your Machine Learning Development with CI/CD using GitHub Actions and Data Version Control

Learn how to calculate meaningful measures of risk and performance, and how to compile an optimal portfolio for the desired risk and return trade-off.

Learn how to approach and win competitions on Kaggle.

Learn the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.

Manage the complexity in your code using object-oriented programming with the S3 and R6 systems.

Learn and use powerful Deep Reinforcement Learning algorithms, including refinement and optimization techniques.

Prepare for your next statistics interview by reviewing concepts like conditional probabilities, A/B testing, the bias-variance tradeoff, and more.