Page 27 - REC :: M.E. CSE Curriculum and Syllabus - R2019

CP19P08 DATA SCIENCE Category L T P C
PE 3 0 0 3

Objectives:
⚫ To get understand the basics of data science
⚫ To get thorough knowledge in analytical methods
⚫ To learn and understand data science packages of python
⚫ To learn and apply data science algorithms using R language
⚫ To study various visualization techniques

UNIT-I INTRODUCTION TO DATA SCIENCE 9
Big Data and Data Science Hype-Characteristics of Big Data- Data Science Life Cycle-Statistical Methods-
Probability-Sampling and Sampling Distributions-Statistical Inference-Prediction and Prediction Error-Resampling

UNIT-II ANALYTICAL THEORY AND METHODS 9
Linear Regression-Simple Linear Regression- Multiple Linear Regression-Logistic Regression-Linear Discriminant
Analysis-Bayesian Methods - Introduction to Clustering Techniques -K means- Gaussian Mixture Models and
Expectations – Maximization – agglomerative clustering – evaluation of clustering – Rand index – mutual information
based scores – Fowlkes – Mallows index - Ensemble Techniques – Bagging & Boosting

UNIT-III DATA SCIENCE USING PYTHON 9
Data science packages- Numpy Basics-Pandas-Data Loading-Data Wrangling-Plotting and Visualization-Data
Aggregation and Group Operations – Data Exploration – Visualization using python

UNIT-IV INTRODUCING R LANGUAGE 9
R Basics-R Objects-R Notations- Packages – Indexing Data- Loading Data - Exploratory Data Analysis using R-
Statistical Methods for Evaluation using R - Data Science applications-Time Series Forecasting, Text Mining &
Sentiment Analysis

UNIT-V DATA VISUALIZATION 9
Data Visualization: Basic Principles, Categorical and Continuous Variables – Exploratory Graphical Analysis –
Creating Static Graphs, Animated Visualizations – Loops, GIFs and Videos.

Total Contact Hours : 45

Course Outcomes:
Upon completion of the course, students will be able to:
⚫ Understand the basics of data science
⚫ Acquire knowledge of various analytical methods
⚫ Able to work with data science packages of python
⚫ Able to apply data science algorithms using R language
⚫ Obtain knowledge of various visualization techniques

Reference Books(s) :
1 Cathy O'Neil and Rachel Schutt. “Doing Data Science, Straight Talk From The Frontline”, O'Reilly. 2014.
2 Garrett Grolemund, “Hands on programming with R”,O’Reilly,2014
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, “An introduction to statistical learning with
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application in R”, Springer.
4 Wes McKinney, “Python for Data Analysis”, O'Reilly Media, 2012
5 Sebastian Raschka, “Python Machine Learning”,Packpub.com,2015
6 Michael Berthold, David J. Hand, “Intelligent Data Analysis”, Springer, Second Edition, 2007

7 Nathan Yau, “Data Points: Visualization That Means Something”, Wiley, 2013.