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Applied Data Science for Data Analysts

Databricks via Coursera


In this course, you will develop your data science skills while solving real-world problems. You'll work through the data science process to and use unsupervised learning to explore data, engineer and select meaningful features, and solve complex supervised learning problems using tree-based models. You will also learn to apply hyperparameter tuning and cross-validation strategies to improve model performance.

NOTE: This is the third and final course in the Data Science with Databricks for Data Analysts Coursera specialization. To be successful in this course we highly recommend taking the first two courses in that specialization prior to taking this course. These courses are: Apache Spark for Data Analysts and Data Science Fundamentals for Data Analysts.


  • Welcome to the Course
  • Applied Unsupervised Learning
  • Feature Engineering and Selection
  • Applied Tree-based Models
  • Model Optimization

Taught by

Kevin Coyle, Mark Roepke and Emma Freeman

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