Follow short videos led by expert instructors and then practice what you’ve learned with interactive exercises in your browser.
Evaluate portfolio risk and returns, construct market-cap weighted equity portfolios and learn how to forecast and hedge market risk via scenario generation.
Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.
Learn to perform linear and logistic regression with multiple explanatory variables.
Conquer NoSQL and supercharge data workflows. Learn Snowflake to work with big data, Postgres JSON for handling document data, and Redis for key-value data.
Leverage the power of tidyverse tools to create publication-quality graphics and custom-styled reports that communicate your results.
Discover the exciting world of Deep Learning for Text with PyTorch and unlock new possibilities in natural language processing and text generation.
Learn about how dates work in R, and explore the world of if statements, loops, and functions using financial examples.
Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.
Practice Power BI with our healthcare case study. Analyze data, uncover efficiency insights, and build a dashboard.
Are customers thrilled with your products or is your service lacking? Learn how to perform an end-to-end sentiment analysis task.
Learn about ARIMA models in Python and become an expert in time series analysis.
In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.
Shift to an MLOps mindset, enabling you to train, document, maintain, and scale your machine learning models to their fullest potential.
Learn to conduct image analysis using Keras with Python by constructing, training, and evaluating convolutional neural networks.
Shiny is an R package that makes it easy to build interactive web apps directly in R, allowing your team to explore your data as dashboards or visualizations.
Learn techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.
This course will show you how to integrate spatial data into your Python Data Science workflow.
Discover different types in data modeling, including for prediction, and learn how to conduct linear regression and model assessment measures in the Tidyverse.
Take your reporting skills to the next level with Tableau’s built-in statistical functions.
Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks.
Learn the theory behind responsibly managing your data for any AI project, from start to finish and beyond.
Use Seaborns sophisticated visualization tools to make beautiful, informative visualizations with ease.
Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.
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.
Elevate your Machine Learning Development with CI/CD using GitHub Actions and Data Version Control
Learn about risk management, value at risk and more applied to the 2008 financial crisis using Python.
Learn about LLMOps from ideation to deployment, gain insights into the lifecycle and challenges, and learn how to apply these concepts to your applications.
Visualize seasonality, trends and other patterns in your time series data.
Parse data in any format. Whether its flat files, statistical software, databases, or data right from the web.
Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!
