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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 R & SAS 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 analytics and why is it so important?
  • Applications of analytics
  • Different kinds of analytics
  • Various analytics tools
  • Analytics project methodology
  • Case study
  • Installation of R & R Studio
  • Getting started with R
  • Basic and Advanced Data types in R
  • Variable operators in R
  • Working with R data frames
  • Reading and writing data files to R
  • R functions and loops
  • Special utility functions
  • Merging and sorting data
  • Practice assignment
  • Summarizing data, measures of central tendency
  • Measures of data variability & distributions
  • Using R language to summarize data
  • Practice assignment
  • Need for data visualization
  • Components of data visualization
  • Utility and limitations
  • Introduction to grammar of graphics
  • Using the ggplot2 package in R to create
  • visualizations
  • Practice assignment
  • Introducing statistical inference
  • Estimators and confidence intervals
  • Central Limit theorem
  • Parametric and non-parametric statistical tests
  • Analysis of variance (ANOVA)
  • Case study
  • Needs & methods of data preparation
  • Handling missing values
  • Outlier treatment
  • Transforming variables
  • Derived variables
  • Binning data
  • Modifying data with Base R
  • Data processing with dplyr package
  • Using SQL in R
  • Practice assignment
  • What is the SAS software?
  • Why is it used?
  • SAS GUI layout
  • Components of a SAS program
  • SAS libraries and library referencing
  • SAS datasets & variables
  • Hands-on practice
  • Creating SAS datasets
  • Referencing SAS files
  • Reading SAS datasets
  • Read/Import raw data files in SAS
  • INPUT statement and its components
  • Infile, Informat statements and Proc Import
  • Reading delimited data
  • Various options while importing files
  • Combine SAS datasets using DATA step
  • Hands-on practice
  • Investigate SAS datasets using basic Procs
  • Sorting observations
  • Conditional execution of SAS statements
  • Assignment statements in DATA step
  • Variable attribute modification
  • Using BY statement to create totals and sub-
  • totals
  • Various SAS functions to manipulate & convert
  • data
  • Merging datasets
  • Hands-on practice
  • Correlation
  • Simple linear regression
  • Multiple linear regression
  • Model diagnostics and validation
  • Case study
  • Moving from linear to logistic regression
  • Model assumptions and Odds ratio
  • Model assessment and gains table
  • ROC curve and KS statistic
  • Case study
  • Need for segmentation
  • Criterion of segmentation
  • Types of distances
  • Clustering algorithms
  • Hierarchical clustering
  • K-means clustering
  • Deciding number of clusters
  • Case study
  • What are time-series?
  • Need for forecasting
  • Trends, seasons, cycles
  • Exponential smoothing-Holt Winters method
  • ARIMA
  • Case Study
  • What are decision trees?
  • Entropy
  • Gini impurity index
  • Decison trees algorithms
  • ID3
  • C4.5
  • CART
  • CHAID
  • Regression trees
  • Linear regression
  • Logistic regression
  • Clustering
  • Hands-on practice