Programme Coverage for Business Analytics
MODULE 1 - Introduction to Big Data
- 1.1 Introduction to Big Data
- 1.2 Business Applications to Big Data
- 1.3 Technologies for handling Big Data
- 1.4 Understanding the Hadoop Ecosystem
- 1.5 Map Reduce Fundamentals
MODULE 2 – Introduction to Analytics and R Programming
- 2.1 Understanding Analytics
- 2.2 Analytical Approaches and Tools
- 2.3 Exporting R
- 2.4 Reading Datasets into R and exporting data from R
- 2.5 Manipulating and Processing data in R
MODULE 3 – Data Analytics using R
- 3.1 Using Functions and Packages in R
- 3.2 Descriptive Statistics in R
- 3.3 Analyzing Big Data by Using Functions, Loops and Data Frames
- 3.4 Graphical Analysis in R
- 3.5 Hypothesis Testing in R
MODULE 4 - Big Data Analytics Methods
- 4.1 Implementing a Big data solution
- 4.2 Data Cleaning and Pre-processing
- 4.3 Social media analytics and text mining
- 4.4 Mobile Analytics
- 4.5 Big Data visualizations
MODULE 5 – Advanced Analytics Using SAS
- 5.1 Linear Regression in R
- 5.2 Nonlinear Regression
- 5.3 Cluster Analysis
- 5.4 Decision Trees
- 5.5 Integrating R and Hadoop and Understanding Hive
Additional Modules
1. Data Visualization with Tableau
- Learn EDA and Basics of Data Visualization
- Descriptive Analytics and various graphs in Tableau
- Advanced graphs and features in Tableau
- Creating dashboards and interactive visualizations in Tableau
2. Machine Learning Concepts
- Understanding Machine Learning
- Graphical Models and Bayesian Networks on R
- Artificial Neural Networks
- Dimensionality Reduction Using PCA and Factor Analysis in R
- Support Vector Machines
3. Social Media, Mobile Analytics
- Big Data Solution engineering
- Social Media Analytics and Testing Mining
- Performing Mobile Analytics
- Big Data Visualization
- Recruitment readiness