Data Science & Modelling

Learning Outcomes: 
After learning the course the students should be able to
• Learn the fundamentals of data analytics and the data science pipeline
• Learn how to scope the resources required for a data science project
• Apply statistical methods, regression techniques, and machine learning algorithms to make sense out of data sets both large and small
• Demonstrate knowledge of statistical data analysis techniques utilized in business decision making.
• Apply principles of Data Science to the analysis of business problems.
• Apply data mining software to solve real-world problems.
• Apply algorithms to build machine intelligence.
• Employ cutting edge tools and technologies to analyse Big Data
Syllabus: 
Unit NoTopics
1

Descriptive Statistics

Introduction to the course, Descriptive Statistics, Probability Distributions

2

Inferential Statistics

Inferential Statistics through hypothesis tests, Permutation & Randomization Test

3

Regression & ANOVA

Regression, ANOVA (Analysis of Variance)

4

Machine Learning Introduction and Concepts

Differentiating algorithmic and model based frameworks, Regression: Ordinary Least Squares, Ridge Regression, Lasso Regression, K Nearest Neighbours, Regression & Classification

5

Supervised Learning with Regression and Classification techniques

Bias-Variance Dichotomy, Model Validation Approaches, Logistic Regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Regression and Classification Trees, Support Vector Machines, Ensemble Methods: Random Forest, Neural Networks, Deep learning

6

Unsupervised Learning  and data modelling

Clustering, Associative Rule Mining, Logical Modelling :  Converting a conceptual model to logical model , Integrity constraints,  Normalization

Text Books: 
Name : 
The elements of statistical learning. Vol. 2. No. 1.
Author: 
Hastie, Trevor, et al.
Publication: 
springer
Edition: 
2009
Reference Books: 
Name: 
Applied statistics and probability for engineers
Author: 
Montgomery, Douglas C.
George C. Runger
Publication: 
. John Wiley & Sons, 2010
Name: 
Scaling up Machine Learning
Author: 
Bekkerman et al.
Name: 
Mining of Massive Datasets
Author: 
AnandRajaraman and Jeffrey David Ullman
Publication: 
Cambridge University
Edition: 
2012
Name: 
Developing Analytic Talent: Becoming a Data Scientist
Author: 
Vincent Granville
Publication: 
wiley
Edition: 
2014
Syllabus PDF: 
branch: 
BDA
Course: 
2018
Stream: 
B.Tech