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By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.Learn to distinguish real differences from random noise, and explore psychological crutches we use that interfere with our rational decision making.
Learn how to use RNNs to classify text sentiment, generate sentences, and translate text between languages.
Learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.
Learn to analyze and visualize network data with the igraph package and create interactive network plots with threejs.
Step into the role of CFO and learn how to advise a board of directors on key metrics while building a financial forecast.
Learn the bag of words technique for text mining with R.
Learn how to analyze survey data with Python and discover when it is appropriate to apply statistical tools that are descriptive and inferential in nature.
Use survival analysis to work with time-to-event data and predict survival time.
Learn to use the Census API to work with demographic and socioeconomic data.
Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.
Extend your regression toolbox with the logistic and Poisson models and learn to train, understand, and validate them, as well as to make predictions.
Automate data manipulation with KNIME, mastering merging, aggregation, database workflows, and advanced file handling.
Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.
Learn how bonds work and how to price them and assess some of their risks using the numpy and numpy-financial packages.
Unlock your datas potential by learning to detect and mitigate bias for precise analysis and reliable models.
Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.
Learn how to design, automate, and monitor scalable forecasting pipelines in Python.
Take vital steps towards mastery as you apply your statistical thinking skills to real-world data sets and extract actionable insights from them.
Learn how to analyze business processes in R and extract actionable insights from enormous sets of event data.
Practice Tableau with our healthcare case study. Analyze data, uncover efficiency insights, and build a dashboard.
Ensure data consistency by learning how to use transactions and handle errors in concurrent environments.
Learn to read, explore, and manipulate spatial data then use your skills to create informative maps using R.
Learn about MLOps, including the tools and practices needed for automating and scaling machine learning applications.
In this course youll learn how to perform inference using linear models.
Learn how to pull character strings apart, put them back together and use the stringr package.
Learn to develop R packages and boost your coding skills. Discover package creation benefits, practice with dev tools, and create a unit conversion package.
Learn to solve increasingly complex problems using simulations to generate and analyze data.
This course covers the basics of financial trading and how to use quantstrat to build signal-based trading strategies.
Dive into our Tableau case study on supply chain analytics. Tackle shipment, inventory management, and dashboard creation to drive business improvements.
Discover the power of discrete-event simulation in optimizing your business processes. Learn to develop digital twins using Pythons SimPy package.