RAdvancedAnalytics

ADVANCED ANALYST :: Foundations for Advanced Data Analytics in R

Module 1: Bootcamp for R Programming

• Week 01
  • Installations and technical prerequisites
  • Introduction to R and RStudio
  • Basics of R markdown
  • Navigating the environment
  • Basic types and data (vectors, matrices, lists, data.frames)
  • Working with variables and basic operations
  • Flow control (if-else decisions, loops)
• Week 02
  • Functions in R
  • Introduction to concepts of functional programming
  • "Apply" function family (apply, lapply, sapply, mapply)
  • Tidyverse approach: data manipulation and exploration
  • Reading and writing data into files (CSV, Excel, etc.)
  • Deeper into R markdown for reporting

Module 2: Data Visualization and Exploratory Analytics

• Week 03
  • Basics of ggplot2: grammar of graphics
  • Basic visualizations: line and bar charts, scatter plots, histograms, bubble charts, box-plot charts, trend lines and panels in ggplot2
  • Descriptive statistics and its interpretation
  • Exploratory analytics in ggplot2
  • Detecting outliers in data
  • Advanced ggplot2 visualizations
  • Exporting and formatting ggplot2 visualizations
• Week 04
  • Basics of Plotly: simple interactive visualization
  • Basic visualizations: line and bar charts, scatter plots, histograms, bubble charts, box-plot charts, trend lines and panels in Plotly
  • From ggplot2 to Plotly
  • Exploratory analytics in Plotly
  • Basics of A/B testing
  • ggplot2 and Plotly in R markdown: fundamental reporting

Module 3: Basic Predictive Analytics and Forecasting

• Week 05
  • One predictor: Simple linear regression
  • Multiple predictors: Multiple linear regression
  • Case Study 1: Report on predictive analytics with linear regression: predicting real estate prices
• Week 06
  • Basics of time series analysis
  • Components of time series
  • Forecasting time series using ARIMA model
  • Case Study 2: Elementary forecasting report

Module 4: Data Collection from APIs and PDFs, Automated Production in MS Office from R, and OpenAI ChatGPT from R

• Week 07
  • R data ecosystem: packages for accessing various data sources
  • Importing data from APIs: simple API calls
  • Understanding JSON and XML formats
  • Advanced API calls and data processing
  • Extracting data from PDF files
  • Case Study 3: World Bank data report
• Week 08
  • Interacting with MS Excel from R
  • Automating PowerPoint production from R
  • Reporting in MS Word from R
  • OpenAI API: automated interaction with ChatGPT from R
  • Case Study 4: Report on text sentiment analysis

Module 5: Classification (Supervised and Unsupervised)

• Week 09
  • Introduction to binomial logistic regression in R
  • Introduction to multinomial regression in R
  • Case Study 5: Solving churn problem report with binomial logistic regression
• Week 10
  • Principal Component Analysis (PCA) in R
  • Case Study 6: Market segmentation report through PCA

Module 6: Decision Tree Model and Clustering Algorithms in R

• Week 11
  • Introduction to information theory for classification trees
  • Concepts of information and entropy, Information Gain and Gini Gain
  • Decision tree for classification problem in rpart package
  • Prediction with decision tree
  • Regression decision trees
  • Case Study 7: Real estate market price prediction report with regression decision tree
• Week 12
  • k-Means Clustering algorithm in R
  • Determining the number of clusters in the solution
  • Case Study 8: User segmentation report through k-Means Clustering