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
