COURSE SUMMARY
Course Title:
Data Analytics
Course Code:
18CSA732
Year Taught:
2018
Semester:
2
Degree:
School:
School of Arts and Sciences

'Data Analytics' is an elective course offered in the M. Phil. in Computer Science & IT (Part time) at School of Arts and Science, Amrita Vishwa Vidyapeetham.

#### PREREQUISITES

This course requires that you are familiar with high-school level linear algebra, and calculus. Knowledge of probability theory, statistics, and programming is desirable.

#### SYLLABUS

Unit I:

Introduction to data analytics (DA), data preparation, data cleaning. Data types and measures of similarity, Data Preprocessing and numerosity reduction, Data Governance

Unit II:

Descriptive Statistics, Probability Distributions, Inferential Statistics through hypothesis tests, Permutation & Randomization Test, Regression, ANOVA (Analysis of Variance)

Unit III:

Machine Learning: Introduction and Concepts, Differentiating algorithmic and model based frameworks, Frequent pattern mining Regression : Ordinary Least Squares, Ridge Regression, Lasso Regression, K Nearest Neighbours Regression & Classification

Unit IV:

Supervised Learning with Regression and Classification techniques -1: Bias-Variance Dichotomy, Model Validation Approaches, Logistic Regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Regression and Classification Trees, Support Vector Machines.

Supervised Learning with Regression and Classification techniques -2: Ensemble Methods: Random Forest, Neural Networks, Deep learning

Unit V:

Unsupervised Learning and Challenges for Big Data Analytics : Clustering, Associative Rule Mining, Challenges for big data analytics.

Prescriptive analytics: Creating data for analytics through designed experiments, Creating data for analytics through Active learning, Creating data for analytics through Reinforcement learning.(R, Weka or any tool)

#### REFERENCES

1. Hastie, Trevor, et al. The elements of statistical learning. Vol. 2. No. 1. New York: springer, 2009.
2. Montgomery, Douglas C., and George C. Runger. Applied statistics and probability for engineers. John Wiley & Sons, 2010