Course

Unit I

Introduction to Business Analytics – Meaning – Definition-Need for Business Analytics – Benefits- Data Scientist vs. Data Engineer vs. Business Analyst – Career in Business Analytics-Types of Business Analytics-Analytics in various fields.

Unit II

Data Collection-Data Management – Sources of data – Importance of data quality – Dealing with missing or incomplete data – Data Visualization – Data Classification.

Measures of central tendency and dispersion of a data set – Probability- conditional probability- Bayes theorem- Simple linear regression- coefficient of determination-Multiple linear regression – coefficient of multiple coefficients of determination.

Unit III

Data Mining – Meaning- Data Warehouse- Related technologies – Machine Learning – DBMS – OLAP – Statistics- Stages of the Data Mining Process-Data Mining Techniques- Data cleaning- Data transformation.

Introduction to OLTP and OLAP – OLTP – OLAP – Different OLAP Architectures – OLAP operations – OLTP and OLAP – Data models for OLTP and OLAP – Role of OLAP Tools in BI Architecture

Unit IV

Data Integration – Data Warehouse – Goals – Data sources – Extract – Transform, Load – Data Integration – Technologies – Data Quality maintenance – Data profiling – Data Modelling – Basics – Types – Techniques – Fact table – Dimension Table – Typical Dimensional Models – Dimensional modelling life cycle – Designing the Dimensional Model

Unit V

Data Mining Tool: Introduction to WEKA – Loading the data (Simple) – Filtering attributes (Simple)

Selecting attributes (Intermediate) – Training a classifier (Simple) – Building your own classifier (Advanced) – Tree visualization (Intermediate) – Testing and evaluating your models (Simple)Regression models (Simple) – Association rules (Intermediate) – Clustering (Simple) – Reusing models (Intermediate) – Data mining in direct marketing (Simple) – Using Weka for stock value forecasting (Advanced).

Course Objectives:

To enable the students to strengthen their basic knowledge on analytical skills and apply this knowledge to the spectrum of business.

Course Outcomes:

The students will able:

CO1: To understand business analytics and its applications

CO2: To understand sources and collection of data

CO3: To familiarize Data mining concepts and Data warehousing techniques

CO4: To understand data integrations and Dimensional Model

CO5: To understand data visualization using WEKA

References:

1. Pang-Ning Tan – Introduction to Data Mining – Pearson Education
2. Jiawei Han, Micheline Kamber, Jian Pei – Data Mining Concepts and Techniques – Elsevier
3. Jeffrey Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann – Business Analytics – Cengage Learning