CIE358 Business Data Analytics Syllabus:

CIE358 Business Data Analytics Syllabus – Anna University Regulation 2021

COURSE OBJECTIVES:

• To understand the basics of business analytics and its life cycle
• To gain knowledge about fundamental business analytics.
• To learn modeling for uncertainty and statistical inference.
• To understand analytics using Hadoop and Map Reduce frameworks.
• To acquire insight on other analytical frameworks.

UNIT I OVERVIEW OF BUSINESS ANALYTICS

Introduction – Drivers for Business Analytics – Applications of Business Analytics: Marketing and Sales, Human Resource, Healthcare, Product Design, Service Design, Customer Service and Support – Skills Required for a Business Analyst – Framework for Business Analytics Life Cycle for Business Analytics Process.

UNIT II ESSENTIALS OF BUSINESS ANALYTICS

Descriptive Statistics – Using Data – Types of Data – Data Distribution Metrics: Frequency, Mean, Median, Mode, Range, Variance, Standard Deviation, Percentile, Quartile, z-Score, Covariance, Correlation – Data Visualization: Tables, Charts, Line Charts, Bar and Column Chart, Bubble Chart, Heat Map – Data Dashboards.

UNIT III MODELING UNCERTAINTY AND STATISTICAL INFERENCE

Modeling Uncertainty: Events and Probabilities – Conditional Probability – Random Variables – Discrete Probability Distributions – Continuous Probability Distribution – Statistical Inference: Data Sampling – Selecting a Sample – Point Estimation – Sampling Distributions – Interval Estimation – Hypothesis Testing.

UNIT IV ANALYTICS USING HADOOP AND MAPREDUCEFRAMEWORK

Introducing Hadoop – RDBMS versus Hadoop – Hadoop Overview – HDFS (Hadoop Distributed File System) – Processing Data with Hadoop – Introduction to MapReduce – Features of MapReduce – Algorithms Using Map-Reduce: Matrix-Vector Multiplication, Relational Algebra Operations, Grouping, and Aggregation – Extensions to MapReduce

UNIT V OTHER DATA ANALYTICAL FRAMEWORKS

Overview of Application Development Languages for Hadoop – PigLatin – Hive – Hive Query Language (HQL) – Introduction to Pentaho, JAQL – Introduction to Apache: Sqoop, Drill, and Spark, Cloudera Impala – Introduction to NoSQL Databases – Hbase and MongoDB.

COURSE OUTCOMES:

On completion of the course, the student will be able to:
CO1: Identify the real world business problems and model with analytical solutions.
CO2: Solve analytical problem with relevant mathematics background knowledge.
CO3: Convert any real world decision making problem to hypothesis and apply suitable statistical testing.
CO4: Write and Demonstrate simple applications involving analytics using Hadoop and MapReduce
CO5: Use open source frameworks for modeling and storing data.

REFERENCES:

1. Jiawei Han, MichelineKamber, ―Data Mining: Concepts and Techniques‖, Morgan Kaufmann, Third edition, 2011.