Lecturer

Course details

  •   Every Thursday
  • Jan 2024 - April 2024
  •   4.00PM- 6.00PM
  •   Virtual

Course Purpose

This course introduces learners to visual tools and techniques used in modern data science to drive data driven insights

Course Outcomes

By the end of the course learners should be able to:

  1. Describe the fundamental concepts of programming

  2. Implement and discuss statistical methods in R

  3. Implement and discuss statistical methods in Python

Course Description

Foundations of computer languages: algorithms, functions, variables, object-orientation, scoping, and assignment. Practical examples from computational social science and social data science. Computer programming: design, write, and debug computer programs using the programming languages R and Python. Algorithm design and program development; data types; control structures; functions and parameter passing; recursion; computational complexity; searching and sorting; and an introduction to the principles of object-oriented programming.

Reference Materials

In this course we will rely on the following books. This does not imply that you cannot consult any other material.

  • Gutierrez, D. D. (2015). Machine learning and data science: an introduction to statistical learning methods with R. Technics Publications

  • Guttag, J. V. (2021). Introduction to Computation and Programming Using Python: With Application to Computational Modeling and Understanding Data. MIT Press.

  • Miller, B. N., & Ranum, D. L. (2011). Problem solving with algorithms and data structures using python Second Edition. Franklin, Beedle & Associates Inc.

Course Software

In this course we will be learning using both R and Python

R and RStudio

We will be using R and RStudio in this course. You can find instructions of installing R and RStudio here

Python and Anaconda

To install and use Python, you can find instructions on installing anaconda here

Course Schedule

The course will run as per the Kirinyaga University Semester Dates

During the course delivery, i encourage you to:

I recommend following this general process for each session:

  • Read what is shared as class notes ()
  • Work out all the examples ()
  • Complete the assignment ()
  • Write and debug codes ()

Motivation

Please watch this video: (How to be consistent while learning data science)