Data Science

Course Introduction: Data Science with R

This course serves a solid foundation of techniques and tools essential for modern data science, with a special focus on utilizing R, one of the most powerful and widely used programming languages in the data science community.

Course Overview

Through a series of lectures, hands-on labs, and a data science challenge, we will cover a broad spectrum of topics:

  • Introduction to R: Starting with the basics, you'll become proficient in R programming, understanding its syntax and data structures.
  • Dimensionality Reduction: Learn to navigate the complexities of high-dimensional data, applying techniques like PCA (Principal Component Analysis) to simplify datasets while retaining their essential characteristics.
  • Clustering: Delve into unsupervised learning by grouping data points based on similarity. Explore algorithms such as K-Means and hierarchical clustering.
  • Classification: Explore classification, a core technique of supervised learning, involves predicting categorical outcomes.
  • Regression: Understand how to predict continuous outcomes using linear and non-linear regression models. This section will equip you with the skills to analyze trends and make predictions from data.
  • Data Wrangling: Acquire the ability to clean, manipulate, and prepare data for analysis. Learn to tackle common data challenges, transforming raw data into a format suitable for analysis.
  • Data Visualization: Unleash the potential of your data through visualization. Learn to create compelling, informative graphics and plots in R to communicate your findings effectively.
  • Generative AI: Get introduced to the frontier of AI with generative models. Providing a glimpse into the future of data science.

Learning Goals

By the end of this course, you will have:

  • Developed a comprehensive skill set in data science through the lens of R programming.
  • Gained hands-on experience with real datasets, applying theoretical knowledge to solve practical problems.
  • Learned to visualize and communicate your findings effectively, a key skill in any data scientist's toolkit.