Certified in Business Analytics Course overview

This specialized course covers the concept of Business Analytics and its key importance to any organization. This course provides an overview into the world of analytics and will learn the various applications of analytics and analytics cycle. Participants will also learn how to create hypothesis, statistically test and validate through data and present with clear and formal numbers to support decision making.

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Instructor Led Learning

Duration: 4 Days
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Video Learning

Duration: 4 Days
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What you will learn

      • Lesson 1:  Introduction to Business Analytics
      • Lesson 2: R Fundamentals
      • Lesson 3: R Data Visualization
      • Lesson 4: Hypothesis testing & ANOVA in R
      • Lesson 5: R Data Preparation
      • Lesson 6: Text Analytics, Document and Word Classification & Sentiment Analysis
      • Lesson 7: R Predictive Analytics

Basic computer skills

Computers made easy course

Module 1: Introduction to Business Analytics

  • What is analytics and why is it so important?
  • Applications of analytics
  • Different kinds of analytics
  • Various analytics tools
  • Analytics project methodology
  • Business Analytics vs. Business Analysis

Module 2: R Fundamentals

  • Installation of R & R Studio
  • 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
  • Merging and sorting data
  • Summarizing data, measures of central tendency
  • Measures of data variability & distributions
  • Using R language to summarize data

Module 3: R Data Visualization

  • 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

Module 4: Hypothesis testing & ANOVA in R

  • Introducing statistical inference
  • Estimators and confidence intervals
  • Central Limit theorem Certified in Business Analytics (CBA)
  • Parametric and non-parametric statistical tests
  • Analysis of variance (ANOVA)
  • Case Study

Module 5: R Data Preparation

  • Needs & methods of data preparation
  • Handling missing values
  • Outlier treatment
  •  Transforming variables
  • Derived variables
  • Binning data
  • Modifying data with Base R

Module 6: Text Analytics, Document and Word Classification & Sentiment Analysis

  • What is text mining?
  • Tools for text mining
  • Text mining packages in R
  • Use cases of text analysis
  • Text mining process
  •  What is document and word classification
  •  Steps for document & word classification
  • Techniques for classification
  • Case study on classifying news articles
  • What is sentiment analysis
  • Why is sentiment analysis done
  • Real world applications of sentiment analysis
  • Steps for sentiment analysis
  • Sentiment scoring
  • Dictionary creation
  • Algorithms for sentiment crossing
  •  Case Study – Analyzing sentiments in tweets for smartphone companies

Module 7: R Predictive Analytics

  • Correlation and Linear regression
  • Logistic regression
  • Segmentation for marketing analytics
  • Time series forecasting
  • Decision Trees

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