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Professional Buisness Analytics

Course Overview

1, Why Business Analytics ?

Business Analytics or Data Science is the scientific process of deriving insights from raw data to support decision making. Through this business analytics course you will learn how to use analytical techniques and the R & SAS language to solve business problems. This is a comprehensive data science course which will take you from the basics of statistical techniques, R & SAS language right up to building predictive models. By the end of this course you will be able to demonstrate your business analytics skills to employers.

2, What are the requirements??

This is a self-learning course through recorded videos where you can learn at your own time and pace. Get faculty support through forums, email or calls for your doubts..

3, What am I going to get from this course???

  • Learn fundamentals of predictive analytics techniques and how & where to use them.
  • Learn the Business Analytics software hands-on to manage, manipulate, cleanse and analyze data.
  • You will not just learn the techniques and tools in isolation but will combine and apply them to derive business insights from raw data.
  • Analytics talent demand is much more than the available skilled supply. Become employable in this fast growing new age field by demonstrating the skills learnt through this course.
  • Use these new-age skills in your existing role to become more efficient and effective.

Modules Overview

  • What is Data Science
  • Explain the need of Data Science
  • Roles and responsibilities of a Data Scientist
  • Types of Analytics
  • Overview of Big Data
  • List of different analytical Tools
  • Introduce R
  • CRAN – How to install base R
  • RStudio
  • Basic building blocks of R
  • Data types in R
  • R operators
  • Conditional Statements
  • Loops
  • Some important functions in R (only list)
  • Different data structures in R
  • Access the elements of different data structures
  • Importing and exporting files of different formats
  • Explain the various types of apply functions
  • dplyr Packages
  • Treating missing values and outliers
  • Data and its types
  • Variables
  • 5 – point summary
  • Skewness and kurtosis
  • Distribution of data
  • Need of Hypothesis testing
  • Four steps of hypothesis testing
  • Types of errors
  • Understand confidence level and significance level
  • Types of test statistics
  • Discuss chi-square test
  • Discuss ANOVA test
  • Correlation and covariance
  • Assumptions of Linearity
  • ANOVA in regression
  • Interpreting the output of Linear regression in R
  • Reason for using logistic regression
  • Logistic transformation
  • Logistic regression modeling
  • ROC
  • Interpreting the output of Logistic regression in R
  • Describe classification system and process
  • Decision Trees and regression trees
  • Understanding the output in R
  • Describe clustering system and process
  • K-means algorithm
  • Hierarchical clustering method