School of Computer Engineeringtheory

DATA ANALYTICS

CSS 2103

Syllabus

01Introduction to Analytics: Descriptive, Predictive, Prescriptive Analytics, steps in data analytics projects
02Data exploration: Data sources, data collection, sampling distributions, data types, describing data using measures of central tendency, distributions, data tabulation, and visualization. case studies
03Data Preparation: Data Cleaning, Data Imputation, Multivariate data analysis using correlation, hypothesis tests, ANOVA, and confidence intervals. Feature Engineering, Data Integration, Data Transformations, Dimensionality reduction, PCA
04Recommender Systems: Generating Association Rules using Apriori Algorithm, Measures of Pattern Interestingness, Metrics-Support, Confidence, Lift, cosine similarity. Collaborative Filtering Techniques- User-based Similarity and Item-based Similarity
05Time Series: basics, time zones, period arithmetic, moving window functions. Case studies

Glenn J. Myatt, Wayne P. Johnson, Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining, John Wiley Publication, Second Edition, 2014
Jiawei Han, Micheline Kamber, Jian Pei, Data Mining Concepts and Techniques Morgan Kaufmann Publishers, Third Edition, 2012
U. Dinesh Kumar, Business Analytics: The Science of Data-Driven Decision Making, Second Edition, Wiley Publications, 2021
Joel Grus, Data Science from Scratch: First Principles with Python, O'Reilly Media, 2019
Anil Maheshwari, Data analytics: A comprehensive guide to data analysis and decision-making, Wiley Publications, 2021
https://archive.nptel.ac.in/courses/110/106/110106072/
Introduction to Data Analytics. https://onlinecourses.nptel.ac.in/noc21_cs45/preview
Data Analytics with Python.

Credits Structure

3Lecture

1Tutorial

0Practical

4Total