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Learning from Data: R programming

Learning from Data: R programming

Syllabus
Schedule
Content
Examples
Exercises
Assignments
Resources

Overview

Readings, lectures, etc.

**Before we start**
1: Exploratory Data Analysis
2: Data Science Basics
3: Sampling; Probability Distributions
4: Confidence Intervals; reshape data
5: Inferential Statistics
6: Hypothesis Testing
7: Regression Models
8: Workshop on regression models
9. Regression for prediction; regression diagnostics
10. Group Case Presentations

Regression for prediction; regression diagnostics

Read before class on Tuesday, April 6, 2021

Readings

Readings

ModernDive Chapter 6

Last updated on March 2, 2021

BIT: Learning from Data: R programming (Spring 2021)
Burkina Faso Institute of Technology

Kostis Christodoulou
kchristodoulou@london.edu

all days all times
Virtual, hybrid

All content licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Content 2021 Kostis Christodoulou

This site created with the Academic theme in blogdown and Hugo, with template modifications by Andrew Heiss. Interactive tutorials were developed by Mohannad Shaheen, LBS MAM 2020.

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