Post Graduate diploma in Data Analytics

Section - I

Introduction to Business Intelligence: Introduction to OLTP and OLAP, BI Definitions & Concepts, Business Applications of BI, BI Framework, Role of Data Warehousing in BI, BI Infrastructure Components – BI Process, BI Technology, BI Roles & Responsibilities 

Basics of Data Integration (Extraction Transformation Loading): Concepts of data integration need and advantages of using data integration, introduction to common data integration approaches, introduction to ETL using SSIS, Introduction to data quality, data profiling concepts and applications

Section - II

Introduction to Multi-Dimensional Data Modeling: Introduction to data and dimension modeling, multidimensional data model, ER Modeling vs. multi dimensional modeling, concepts of dimensions, facts, cubes, attribute, hierarchies, star and snowflake schema, introduction to business metrics and KPIs, creating cubes using SSAS

Section – III

Basics of Enterprise Reporting: Introduction to enterprise reporting, concepts of dashboards, balanced scorecards, introduction to SSRS Architecture, enterprise reporting using SSRS

Section-I

Descriptive Statistics, Probability Distributions, Inferential Statistics through hypothesis tests, Regression, ANOVA (Analysis of Variance)

Section II

Differentiating algorithmic and model based frameworks, Regression: Ordinary Least Squares, Ridge Regression, Lasso Regression, K Nearest Neighbours Regression & Classification, Bias-Variance Dichotomy, Model Validation Approaches, Logistic Regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Regression and Classification Trees, Support Vector Machines

Section III

Ensemble Methods: Random Forest, Neural Networks, Deep learning, Clustering, Associative Rule Mining, Challenges for big data anlalytics, Creating data for analytics through designed experiments, Creating data for analytics through Active learning, Creating data for analytics through Reinforcement learning

PGDDA 101: CLOUD COMPUTING

 

Course Objectives:

1)   To learn how to use Cloud Services.

2) To implement Virtualization.

3)  To design a private cloud.

4)To study various applications involving Big Data.

 

                                                      Section-A

Overview of Computing Paradigm- Recent trends in Computing: Grid Computing, Cluster Computing, Distributed Computing, Utility Computing, Cloud computing; Evolution of cloud computing, Business driver for adopting cloud computing.

Cloud Computing Architecture- Standard cloud model, NIST cloud computing reference architecture, Cloud deployment models, Choosing the appropriate deployment model.

Service Management in Cloud Computing: Service Delivery Models, Service Abstraction, The SPI Model, A traditional system vs Cloud system model, SAAS and PAAS: Salesforece.com and Force.com, Other Category of Cloud Services.

 

                                                     Section-B

Cloud Security- The Security Concern in Cloud, Cloud Security Working Groups, Elements of cloud security model, Examining Cloud Security against traditional computing, Security policy.

 

Introduction to Big Data-Big Data and its importance, Characteristics of Big Data, Domain specific examples of Big Data, Analytics Flow for Big Data.

 

                                                  Section-C

Introduction to Hadoop: Hadoop as a Solution, Features of Hadoop, Hadoop Core Components, Hadoop Ecosystem. 

NoSQL- What is it, Where It is Used, Why NoSQL, Advantages of NoSQL, SQL vs NoSQL, NoSQL databases.

 

Recommended Books

1.         Cloud Computing Bible, Barrie Sosinsky, Wiley-India, 2010

2.         Cloud Computing: Principles and Paradigms, Editors: Rajkumar Buyya, James Broberg, Andrzej M. Goscinski, Wiley, 2011

3.         Cloud Computing: Principles, Systems and Applications, Editors: Nikos Antonopoulos, Lee Gillam, Springer, 2012

4.         Cloud Security: A Comprehensive Guide to Secure Cloud Computing, Ronald L. Krutz, Russell Dean Vines, Wiley-India, 2010

5.         Boris lublinsky, Kevin t. Smith, AlexeyYakubovich, “Professional Hadoop Solutions”, Wiley, ISBN: 9788126551071, 2015.

6.           Chris Eaton,Dirk derooset al. , “Understanding Big data ”, McGraw Hill, 2012

7.           Big Data and Analytics , Sima Acharya, Subhashini Chhellappan, Wiley.