PG Diploma in Data Analytics

Department of Business Intelligence offers short term courses of one year Post Graduate Diploma Programmes. The target of the programmes is those persons who want to increase their employability or improve their professional skills but did not have the opportunity to do so earlier in a formal manner. The focus of the programmes will be to impart the required knowledge and skills. These programmes will be conducted during the evening hours so that employed persons or students pursuing other programmes can also join without affecting their normal pursuits. In other words, even Post Graduate students pursuing other full timeprogrammes can also join any of these programmes


  • In-take : The minimum intake will be 10 and the maximum number will be 30 students per course.
  • No of Papers : 4
  • Credit : 16 (Each Paper have 4 credit)
  • Duration: One year Eligibility : Minimum graduation in any discipline (Students pursuing any part time or full time programme after their graduation/employed persons who are graduates). One participant can undergo only one programme at a time.
  • Fees: Rs. 20,000/- to be paid at the time of admission. Examination fees - Rs. 575/- would be paid to the University separately for each university exam
  • Timings: Two hours per day, three-four days a week (7:00 pm to 9:00 pm)
  • Reservation: As per Gujarat University rules
  • Examination Pattern: Internal Examination: 20% (Two hours) Continuous evaluation: 30% (Quizzes, Presentation, Attendance Assignments and Project)


Introduction to Research Methods:-
Introduction to Research and Problem Formulation, Research Process and Research Design, Sampling Theory, Data Collection, Preparation, Analysis And Reporting

Basics of statistics:-
Sample And Population, Population Parameter And Sample Statistic, Descriptive Statistics And Inferential Statistics, Ungrouped Data And Grouped Data, Dependent And Independent Variable, Scale Of Measurement( Nominal, Ordinal, Interval And Ratio)

Introducing Software Interface:-
Data View And Variable View), Measurement Scales, How To Export Data From Excel To Software, Entering, Saving And Printing Data, Viewing A Few Cases, Merge File With Cases, Merge File With Variables, Sort Cases, Spilt File, Select Cases, How To Do Serial Number, Recode Into Same Variable, Recode Into Different Variable, Compute Command, Visual Binning, Generation Of Shell File.

Descriptive Statistics:-
Tables And Graphs for One Variable, Tables And Graphs for Two Variables, One Variable Descriptive Statistics, Two Variables Descriptive Statistics, Measures Of Central Tendency And Variability, Shape Of Distribution, Stem and Leaf Charts, Box And Whisker Plot.

One - Sample Hypothesis Tests, Two- Sample Hypothesis Tests (theory):-
The Logic of Hypothesis testing, A More Realistic Case: We Don’t know µand Sigma, OneSample T-Test, The Logic of Hypothesis Testing, Paired vs. Independent Samples, Testing

Assumptions of Independent Samples, Normal Populations, Randomness of Data and Equal population Variance, Comparing Three or More Means, Testing Assumptions of Independent Samples, Normal Populations and Homogeneity Population Variance, One- Factor Independent Measures ANOVA, Post Hoc Multiple Comparisons

Univariate, bivariate, Multivariate analysis techniques (SPSS Practical session)

Parametric and Nonparametric Methods:-
Introduction to Parametric and Nonparametric Methods, Mann-Whitney U test, Wilcoxon Signed Ranks Test, Kruskal- Wallis H Test, Spearman’s Rank Order Correlation, Sign Test, Runs Test, One Sample Chi Square Test, Fridman One-Way Anova, Kolmogorov- Smirnov One Sample Test.

Factor analysis, cluster and discriminate analysis :-
What is FA, Hypothetical example of FA, Assumptions of FA, Deriving factors and assessing overall fit, interpreting the factors, Validation of factors analysis. What is cluster analysis, how does it work, Cluster analysis decision process, what is MDS, Hypothetical example of it, Assumption of it interpretation & Validation

Correlation and regression analysis:-
What is Multiple Correlation Analysis, assumption of it, Hypothetical example of it, interpretation & validation. Introduction, Regression with a binary dependent variable, estimating Logistic Regression Model, Hypothetical example of it, interpretation & Validation Introduction R- Software and Qualitative Analysis Introductory session for each one mentioned above