An Introduction to Data Science in R from Stanford University

Introduction :

This course presents a high-level overview of three main topics in data science: basic analysis and visualization of data, introductory machine learning concepts, and basic programming in R (a programming language that is widely used for data analysis). The course will include lectures and hands-on, interactive problem-solving. Examples will come from real-world problems in weather, marketing, biology, stocks, neuroscience, medicine, and other disciplines. By the end of the course, students will be able to apply data science techniques to real-world applications in order to draw meaningful conclusions.

Target Candidates:

No computer science experience is necessary.

Tentative Weekly Content Outline:

Week 1 - What is Data Science, Getting Started with R
Week 2 - Data Visualization, Basics in Programming in R
Week 3 - Interactive Visualization, Compare Distributions
Week 4 - Data Science and Statistical Analysis
Week 5 - Predictive Modelling, Regression Analysis
Week 6 - Predictive Modelling, Classification
Week 7 - Feature Selection and Finding the Best Predictive Model
Week 8 - Clustering and Association Rule Mining
Week 9 - Advanced Topics in R: R Selenium and Shiny
Week 10 - Advanced Machine Learning, Deep Learning
Weekly Module Length: 1 hour. Total Training 10 hours

The course attendance:

This is a live on-line course conducted using Zoom over a 10 week period, for one hour a week. Attendance at Zoom sessions is optional. Zoom sessions will be recorded and made available to enrolled students who are unable to attend.