Applied Data Analysis

Title Applied Data Analysis (53120)
Quarter Spring 2020
Instructor Davender Singh Sahota (


This course provides a self-contained introduction to computational data analysis from an applied perspective. It is intended as a standalone course for students who are not pursuing the full data analysis sequence in the MPCS. As such, students who have taken MPCS 53110 Foundations of Computational Data Analysis and received a grade of B or higher should take MPCS 53111 Machine Learning. Students that have taken or are currently enrolled in MPCS 53111 Machine Learning cannot register for this class. 

The course will cover topics in basic probability theory, statistical inference, and basic machine learning models typically used in data analysis. Each topic will be accompanied by example illustrations using computational packages and software. Many of the topics covered form the basis of almost all algorithms and machine learning methods used in big data analysis. Emphasis will be given on using these techniques for problem solving.  

Textbook: An Introduction to Statistical Learning

Week 1: Elementary Probability Statistics

  • Course overview
  • Probability theory
  • Random variables
  • Distributions and densities
Week 2: Software Platforms
  • Variables, objects, and functions in Python
  • Working with data frames
  • Data pre-processing and visualization
Week 3: Linear Models/Statistical Inference
  • Least-squares regression
  • Logistic regression
  • Hypothesis testing
Week 4: Model Assessment and Selection

Week 5: Machine Learning Models I
  • Perceptron classifier
  • Neural networks
  • Decision trees/Random forests
Week 6: Midterm

Week 7: Clustering 
  • Unsupervised clustering
  • Supervised clustering
Week 8: Machine Learning Models II
  • Support vector machines
Week 9 : Computational Frameworks
  • Common machine learning frameworks
  • Big data analytics
Week 10: TBD

Week 11: Project presentations
Prerequisites (Courses)

MPCS 50103 Discrete Mathematics and Core Programming

Prerequisites (Other)

Knowledge of Python is required for this class.




Wednesday 5:30-8:30PM


Harper C03