LECTURE 2 (05 MAY) Definition, Types of Data, Importance of Data

LECTURE 3 (6 MAY) Categories of Data, Sources of Data

LECTURE 4 (07 MAY) The Importance of Data Collection, Ethical Consideration in Data Collection, MCQ

LECTURE 5 (8 MAY) Introduction to Data Analytics, Data Analytics Cycle

LECTURE 6 (9 MAY) Data Analytics Stages, Applications of Data Analytics, Implications of Data Analytics, MCQs

LECTURE 7 (11 MAY) Revision

LECTURE 8 (12 MAY) Characteristics of Big Data

LECTURE 9 (13 MAY) Sources of Big Data, Application of Big Data, Challanges of Big Data

LECTURE 10 (14 MAY) MCQ, Chapter 2 Data Goverance and management System

LECTURE 11 (15 MAY) Key Objectives of Data Governance, Key Components of Data Governance (Data Life-Cycle Management)

LECTURE 12 (16 MAY) Key Components of Data Governance (topic# 1,2,3,6) Unit 6&7 (Data stewardship and Metadata Management)

LECTURE 13 (18 MAY) Key Components of Data Governance, Data security, Data security practices Emerging threats and solutions.

LECTURE 14 (19 MAY) Levels of Data Governance, Data Classification

LECTURE 15 (20 MAY) Data Storage, Storage Option, Emerging Trend in Data storage, Data Integrity, Types Of Data Integrity

LECTURE 16 (21 MAY) PART A Intro to DBMS, Component of DBMS, DATABASE Characteristic, Atomicity

LECTURE 16 (21 MAY) PART B Consistency, Isolation, Durability, Additional Characteristics

Lecture 17 (22 May): Three Level Architecture

Lecture 18 (23 May): Data Independence

LECTURE 19 (1 Jun) PART A: Data Warehousing

LECTURE 19 (1 Jun) PART B: OLAP & OLTP

LECTURE 20 (02 JUN): Data Normalization

LECTURE 21 (03 JUN)

LECTURE 22 (04 JUN)

LECTURE 23 (05 JUN) PART A(CHAPTER 5)

LECTURE 23 (05 JUN) PART B(CHAPTER 5)

LECTURE 24 (06 JUN) (CHAPTER 6)