Explainable AI in Python
Gain the essential skills using Scikit-learn, SHAP, and LIME to test and build transparent, trustworthy, and accountable AI systems.
Follow short videos led by expert instructors and then practice what you’ve learned with interactive exercises in your browser.
Gain the essential skills using Scikit-learn, SHAP, and LIME to test and build transparent, trustworthy, and accountable AI systems.
Build robust, production-grade APIs with FastAPI, mastering HTTP operations, validation, and async execution to create efficient data and ML pipelines.
Learn how to create and assess measurement models used to confirm the structure of a scale or questionnaire.
Learn to develop R packages and boost your coding skills. Discover package creation benefits, practice with dev tools, and create a unit conversion package.
Get hands-on experience making sound conclusions based on data in this four-hour course on statistical inference in Python.
Learn to set up a secure, efficient book recommendation app in Azure in this hands-on case study.
Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.
Learn how to produce interactive web maps with ease using leaflet.
Use survival analysis to work with time-to-event data and predict survival time.
Unlock your datas potential by learning to detect and mitigate bias for precise analysis and reliable models.
Learn how to use PostgreSQL to handle time series analysis effectively and apply these techniques to real-world data.
In this course youll learn techniques for performing statistical inference on numerical data.
Learn how to create interactive data visualizations, including building and connecting widgets using Bokeh!
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.
Learn how to use Python parallel programming with Dask to upscale your workflows and efficiently handle big data.
This course is for R users who want to get up to speed with Python!
Learn the basics of A/B testing in R, including how to design experiments, analyze data, predict outcomes, and present results through visualizations.
Learn to build pipelines that stand the test of time.
Learn to solve increasingly complex problems using simulations to generate and analyze data.
Master strategic data management for business excellence.
Discover the power of discrete-event simulation in optimizing your business processes. Learn to develop digital twins using Pythons SimPy package.
Learn how to import, clean and manipulate IoT data in Python to make it ready for machine learning.
Learn how to manipulate, visualize, and perform statistical tests through a series of HR analytics case studies.
Transition from MATLAB by learning some fundamental Python concepts, and diving into the NumPy and Matplotlib packages.
Advance you R finance skills to backtest, analyze, and optimize financial portfolios.
Explore HR data analysis in Tableau with this case study.
Learn to distinguish real differences from random noise, and explore psychological crutches we use that interfere with our rational decision making.
Learn all about how DataCamp builds the best platform to learn and teach data skills.
Gain an overview of all the skills and tools needed to excel in Natural Language Processing in R.
Strengthen your knowledge of the topics covered in Manipulating Time Series in R using real case study data.