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OPEN ELECTIVES OFFERED BY DEPT OF INFORMATION TECHNOLOGY
S. COURSE COURSE TITLE CATEGORY CONTACT L T P C
NO. CODE PERIODS
1. OIT1701 Data Science OE 3 3 0 0 3
Advanced Python
2. OIT1702 OE 3 3 0 0 3
Programming
3. OIT1703 Business Intelligence OE 3 3 0 0 3
4. OIT1704 Computer Vision OE 3 3 0 0 3
5. OIT1705 Cyber Security OE 3 3 0 0 3
Machine Learning and R
6. OIT1706 OE 3 3 0 0 3
Programming
OIT1701 DATA SCIENCE LT P C
3 0 0 3
OBJECTIVES:
The student should be able to
Able to apply fundamental algorithmic ideas to process data.
Learn to apply hypotheses and data into actionable predictions.
Document and transfer the results and effectively communicate the findings using visualization
techniques.
UNIT I INTRODUCTION TO DATA SCIENCE 9
Data science process – roles, stages in data science project – working with data from files – working
with relational databases–exploring data–managing data–cleaning and sampling for modeling and
validation – introduction to NoSQL. (Ref. Book 1: Chapter 1-4)
UNIT II MODELING METHODS 9
Choosing and evaluating models – mapping problems to machine learning, evaluating models,
validating models – cluster analysis – K-means algorithm, Naïve Bayes – Memorization Methods –
Linear and logisticregression – unsupervised methods. (Ref. Book 1: Chapter 5-8)
UNIT III INTRODUCTION TO R 9
Reading and getting data into R – ordered and unordered factors – arrays and matrices – lists and data
frames – reading data from files – probability distributions – statistical models in R - manipulating
objects – data distribution. (Ref. Book 3: Chapter 2,5 & Ref. Book 4: Chapter 5-8,11)
UNIT IV MAP REDUCE 9
Introduction–distributed file system–algorithms using mapreduce, Matrix-Vector Multiplication by
Map Reduce–Hadoop-Understanding the Map Reduce architecture- Writing Hadoop Map Reduce
Programs-Loading data into HDFS – Executing the Map phase-Shuffling and sorting - Reducing
phase execution. (Ref. Book 2: Chapter 2 & Ref. Book 8: Chapter 1-8)
Curriculum and Syllabus | Open Electives | R 2017 | REC Page 70

