Intermediate Predictive Analytics in Python
Learn how to prepare and organize your data for predictive analytics.
Follow short videos led by expert instructors and then practice what you’ve learned with interactive exercises in your browser.
Learn how to prepare and organize your data for predictive analytics.
Learn the basics of cash flow valuation, work with human mortality data and build life insurance products in R.
Learn the fundamentals of valuing stocks.
Unlock your datas potential by learning to detect and mitigate bias for precise analysis and reliable models.
Explore the latest techniques for running the Llama LLM locally, fine-tuning it, and integrating it within your stack.
Manipulate text data, analyze it and more by mastering regular expressions and string distances in R.
Learn statistical tests for identifying outliers and how to use sophisticated anomaly scoring algorithms.
Learn mixture models: a convenient and formal statistical framework for probabilistic clustering and classification.
Master the essential skills of data manipulation in Julia. Learn how to inspect, transform, group, and visualize DataFrames using real-world datasets.
Learn defensive programming in R to make your code more robust.
Master data visualization in Julia. Learn how to make stunning plots while understanding when and how to use them.
Advance your Alteryx skills with real fitness data to develop targeted marketing strategies and innovative products!
Predict employee turnover and design retention strategies.
Explore Power BI Service, master the interface, make informed decisions, and maximize the power of your reports.
Continue learning with purrr to create robust, clean, and easy to maintain iterative code.
Use C++ to dramatically boost the performance of your R code.
Learn how to predict click-through rates on ads and implement basic machine learning models in Python so that you can see how to better optimize your ads.
Learn to build simple models of market response to increase the effectiveness of your marketing plans.
Learn how to write scalable code for working with big data in R using the bigmemory and iotools packages.
Learn strategies for answering probability questions in R by solving a variety of probability puzzles.