Data Analytics Syllabus for MCA 3rd Year 1st Sem R19 Regulation JNTUH

Data Analytics detailed Syllabus for Master of Computer Applications(MCA), R19 regulation has been taken from the JNTUH official website and presented for the students affiliated to JNTUH course structure. For Course Code, Subject Names, Theory Lectures, Tutorial, Practical/Drawing, Credits, and other information do visit full semester subjects post given below. The Syllabus PDF files can also be downloaded from the universities official website.

For all other MCA 3rd Year 1st Sem Syllabus for R19 Regulation JNTUH, do visit MCA 3rd Year 1st Sem Syllabus for R19 Regulation JNTUH Subjects. The detailed Syllabus for data analytics is as follows.

Course Objectives:

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Course Outcomes:

After completion of this course students will be able to

1. Learn basics of R language and learn how to use R to handle the files with data.
2. Understand different files formats like .csv and .txt and learn how access these files.
3. Design Data Architecture
4. Understand various Data Sources

Unit I

Data Management: Design Data Architecture and manage the data for analysis, understand various sources of Data like Sensors/Signals/GPS etc. Data Management, Data Quality(noise, outliers, missing values, duplicate data) and Data Processing and Processing.

Unit II

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Unit III

Regression:Concepts, Blue property assumptions, Least Square Estimation, Variable Rationalization, and Model Building etc. Logistic Regression: Model Theory, Model fit Statistics, Model Construction, Analytics applications to various Business Domains etc.

Unit IV

Object Segmentation: Regression Vs Segmentation – Supervised and Unsupervised Learning, Tree Building – Regression, Classification, Overfitting, Pruning and Complexity, Multiple Decision Trees etc. Time Series Methods: Arima, Measures of Forecast Accuracy, STL approach, Extract features from generated model as Height, Average Energy etc and Analyze for prediction

Unit V

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
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Text Books:

1. Students Handbook for Associate Analytics – II, III.
2. Data Mining Concepts and Techniques, Han, Kamber, 3rd Edition, Morgan Kaufmann Publishers.

Reference Books:

1. Introduction to Data Mining, Tan, Steinbach and Kumar, Addision Wisley, 2006.
2. Data Mining Analysis and Concepts, M. Zaki and W. Meira
3. Mining of Massive Datasets, Jure Leskovec Stanford Univ. Anand RajaramanMilliway Labs Jeffrey D Ullman Stanford Univ.

For detail Syllabus of all other subjects of Master of Computer Applications 3rd Year, visit MCA 3rd Year Syllabus Subjects.

For all MCA results, visit JNTUH MCA all years, and semester results from direct links.