MC4008 Business Data Analytics Syllabus:
MC4008 Business Data Analytics Syllabus – Anna University PG Syllabus 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 MAPREDUCE FRAMEWORK
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.
TOTAL: 45 PERIODS
SUGGESTED ACTIVITIES:
1. Study on some application of Business analytics in organizations of any domain
2. Study the statistics and data visualization charts of sales data like Amazon using R
3. Study on new strategies derived using data analytic tools on some business data set available and its impact on company progress
4. Prepare a report on the use of Hadoop framework in any two companies
5. Compare and contrast the various Data Analytical Frameworks
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 problems 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. U. Dinesh Kumar, “Business Analytics: The Science of Data-Driven Decision Making”, Wiley, First Edition, 2017.
2. Umesh R Hodeghatta, Umesha Nayak, “Business Analytics Using R – A Practical Approach”, Apress, First Edition2017.
3. Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann, David R. Anderson, “Essentials of Business Analytics”, Cengage Learning, second Edition, 2016.
4. Rui Miguel Forte, “Mastering Predictive Analytics with R”, Packt Publication, First Edition2015.
5. VigneshPrajapati, “Big Data Analytics with R and Hadoop”, Packt Publishing, First Edition2013.
6. Anand Rajaraman, Jeffrey David Ullman, “Mining of Massive Datasets”, Cambridge University Press, First Edition 2012.
7. A. Ohri, “R for Business Analytics”, Springer, First Edition, 2012