Introduction to Applied Statistics

Summary

Learn and apply statistical concepts using the Databricks Workspace.

Description

In this course you’ll learn, both in theory and in practice, about statistical techniques that are fundamental to many data science projects. Throughout the course, videos will guide you through the conceptual information you need to know about these statistical concepts, and hands-on lab activities will give you the chance to apply the concepts you learn using the Databricks Workspace. This course is divided into three modules: Introduction to Statistics and Probability, Probability Distributions, and Applying Statistics to Learn from Data.

Learning objectives

  • Contrast descriptive statistics and inferential statistics.

  • Explain fundamental concepts behind discrete probability.

  • Compare and contrast discrete and continuous probability distributions.

  • Explain how discrete and continuous probability distributions can be used to model data.

  • Apply hypothesis testing techniques to learn from data.

Prerequisites

  • Beginning experience using the Databricks Data Science Workspace (familiarity with Spark SQL, experience importing files into the Databricks Data Science Workspace)

  • Beginning experience using Python (ability to follow guided use of the SciPy library)

Learning path

  • This course is part of the Data Scientist learning path.

Proof of completion

  • Upon 80% completion of this course, you will receive a proof of completion.