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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
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