Module 1: Introduction to Business Analytics
This section provides an overview into the world of analytics. You will learn about various applications of analytics and analytics cycle.

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
R is the most popular software/language for data management & statistical analysis of data. It is free and open source. This section covers on the first step of analytics on how to manage and manipulate data and datasets. Also learn how to start understanding the story your data is narrating by summarizing the data, checking its variability and shape.

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
Data visualization is extremely important to understand what the data is saying and gain insights in a snap. Visualization of data is a strong point of the R software.

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: R Data preparation
Real world data is rarely Clean, It will always be dirty with missing data points, incorrect data, variables needing to be changed or created in order to analyze etc. A typical analytics project will have 60% of its time spent on preparing data for analysis. This is a crucial process as properly cleaned data will result in more accurate and stable analysis. This section teaches you all the data preparation techniques.

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

Module 5: Hypothesis testing and ANOVA in R
With 93% confidence we can say that there is a 70% chance, people visiting this site thrice will buy this product. In this section, we cover on how to create a hypothesis, statistically test it and validate it through data and present it with clear and formal numbers to support decision making.

Introducing statistical inference
Estimators and confidence intervals
Central Limit theorem
Parametric and non-parametric statistical tests
Analysis of variance (ANOVA)
Case Study

Module 6: R Predictive analytics

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

Module 7: 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 analytics
Text mining process
What is document & 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 scoring
Case study - Analyzing sentiments in tweets for smartphone companies

CASE STUDY: Solving an actual business problem through analytics connecting all the concepts you had studied in this course.

CBAP: Certified Business Analytics Professional

Contact