{"id":24317,"date":"2020-07-20T04:57:36","date_gmt":"2020-07-20T04:57:36","guid":{"rendered":"https:\/\/www.inspirenignite.com\/jntuh\/19mba18-data-analytics-syllabus-for-mba-finance-3rd-sem-r19-regulation-jntuh\/"},"modified":"2020-07-20T04:57:36","modified_gmt":"2020-07-20T04:57:36","slug":"19mba18-data-analytics-syllabus-for-mba-finance-3rd-sem-r19-regulation-jntuh","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/jntuh\/19mba18-data-analytics-syllabus-for-mba-finance-3rd-sem-r19-regulation-jntuh\/","title":{"rendered":"19MBA18: Data Analytics Syllabus for MBA (Finance) 3rd Sem R19 Regulation JNTUH"},"content":{"rendered":"

Data Analytics detailed Syllabus for MBA (Finance), R19 regulation has been taken from the JNTUH<\/a> 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.<\/p>\n

For all other MBA (Finance) 3rd Sem Syllabus for R19 Regulation JNTUH, do visit MBA (Finance) 3rd Sem Syllabus for R19 Regulation JNTUH Subjects<\/a>. The detailed Syllabus for data analytics is as follows. <\/p>\n

Course Objectives:<\/h4>\n

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App<\/a>
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b>
\"Get<\/a>. <\/p>\n

Learning Outcome:<\/h4>\n

Students will be able to understand<\/p>\n

    \n
  1. Importance of Analytics<\/li>\n
  2. Understanding the analytical tools<\/li>\n
  3. Application of Analytical tools to solve business problems.<\/li>\n<\/ol>\n

    Unit I<\/h4>\n

    Introduction to Data Analytics: Introduction to Data- Importance of Analytics- Data for Business Analytics -Big Data – Business Analytics in Practice. Data Visualization – Data Visualization tools, Data queries, Statistical methods for Summarizing data, Exploring data using pivot tables.<\/p>\n

    Unit II<\/h4>\n

    For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App<\/a>
    It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b>
    \"Get<\/a>. <\/p>\n

    Unit III<\/h4>\n

    Predictive Analytics: Karl Pearson Correlation Techniques – Multiple CorrelationSpearmans Rank correlation -Simple and Multiple regression -Regression by the method of least squares – Building good regression models – Regression with categorical independent variables – -Linear Discriminant Analysis – One way and Two-Way ANOVA<\/p>\n

    Unit IV<\/h4>\n

    Data Mining: Scope of Data Mining, Data Exploration and Reduction, Unsupervised learning – cluster analysis, Association rules, Supervised learning- Partition Data, Classification Accuracy, prediction Accuracy, k-nearest neighbors, Classification and regression trees, Logistics Regression.<\/p>\n

    Unit V<\/h4>\n

    For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App<\/a>
    It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b>
    \"Get<\/a>. <\/p>\n

    Suggested Readings:<\/h4>\n