Data Science Professional Certificate

• Fundamental R programming skills
• Statistical concepts such as probability, inference, and modeling and how to apply them in practice
• Gain experience with the tidyverse, including data visualization with ggplot2 and data wrangling with dplyr
• Become familiar with essential tools for practicing data scientists such as Unix/Linux, git and GitHub, and RStudio
• Implement machine learning algorithms
• In-depth knowledge of fundamental data science concepts through motivating real-world case studies

This course is part of The Professional Certificate® Programme in Data Science. The courses in this series are:

Data Science R Basics:

Build a foundation in R and learn how to wrangle, analyze, and visualize data.

Data Science Visualisation:

Learn basic data visualization principles and how to apply them using ggplot2.

Data Science Probability:

Learn probability theory — essential for a data scientist — using a case study on the financial crisis of 2007–2008.

Data Science Inference and Modelling:

Learn inference and modeling, two of the most widely used statistical tools in data analysis.

Data Science Productivity Tools:

Keep your projects organized and produce reproducible reports using GitHub, git, Unix/Linux, and RStudio.

Data Science Wrangling:

Learn to process and convert raw data into formats needed for analysis.

Data Science Linear Regression:

Learn how to use R to implement linear regression, one of the most common statistical modeling approaches in data science.

Data Science Machine Learning:

Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques.

Data Science Capstone