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Abstract Blue Light
Certified Data Science R Programming

Target Students​

Data scientists, analysts, and professionals looking to develop their data science skills using the R programming language.

Exam Formats​
 

100 multiple-choice questions

Duration :  40 hours (5 days)
Learning Objectives

-Master the R programming language for data science applications.

-Learn data manipulation, analysis, and visualization techniques using R.

-Implement machine learning algorithms and statistical models in R.

-Understand best practices for writing efficient, maintainable, and scalable R code.

-Prepare for data science certification exams and real-world data science projects.

Exam Options​
 

Online

In-Person

Exam Codes: DSRP-803
Exam Duration: 2 hours
Passing Score​​: 70% 
Course Outline
Introduction to R and Basic Data Handling
​
Module 1: Introduction to R Programming
​

  • Getting Started with R

  • Overview of R: Installation, IDEs (RStudio), and Basic Syntax

  • Understanding R Data Types: Vectors, Lists, Matrices, Data Frames, and Factors

  • Writing and Running Basic R Scripts

  • Control Structures in R

  • Conditional Statements: If-Else, Switch

  • Looping Structures: For, While, Repeat

  • Error Handling: Try-Catch, Debugging Techniques

​Module 2: Data Structures and Basic Operations in R
​

  • Understanding and Using R Data Structures

  • Creating and Manipulating Vectors, Lists, and Data Frames

  • Working with Matrices and Arrays: Applications in Data Science

  • Factors in R: Handling Categorical Data

  • Basic Data Operations

  • Subsetting, Indexing, and Merging Data

  • Applying Functions: Lapply, Sapply, Tapply, and Apply

  • String Manipulation in R: Regular Expressions, Substring, and Stringr Package

Data Manipulation and Management with R
Module 3: Data Manipulation with dplyr and tidyr
​

  • Introduction to dplyr

  • Overview of the dplyr Package: Tidy Data Concepts

  • Key Functions: Select, Filter, Mutate, Summarize, and Arrange

  • Piping with %>%: Streamlining Data Manipulation Workflows

  • Data Transformation with tidyr

  • Reshaping Data: Gather, Spread, Separate, and Unite

  • Managing Missing Data: Imputation Techniques, Dropping NA Values

  • Case Study: Cleaning and Transforming Real-World Datasets

Module 4: Advanced Data Management
​

  • Working with Dates and Times in R

  • Overview of Date and Time Classes in R: Date, POSIXct, POSIXlt

  • Date-Time Manipulation using Lubridate Package

  • Time Series Analysis: Handling and Visualizing Temporal Data

  • Database Interaction with R

  • Introduction to DBI and RSQLite for Database Management

  • Connecting to SQL Databases: Querying and Retrieving Data

  • Case Study: Integrating R with SQL for Data Science Projects

Data Visualization in R
Module 5: Data Visualization with ggplot2
​

  • Introduction to Data Visualization

  • Importance of Data Visualization in Data Science

  • Overview of ggplot2 Package: Grammar of Graphics

  • Creating Basic Plots: Scatter Plots, Line Charts, Bar Graphs

  • Advanced Visualization Techniques

  • Customizing Plots: Themes, Colors, and Annotations

  • Faceting and Layering: Creating Complex Visualizations

  • Case Study: Visualizing Large Datasets with ggplot2

Module 6: Interactive and Dynamic Visualizations
​

  • Interactive Visualizations with plotly

  • Converting Static Plots to Interactive Visuals

  • Adding Hover, Zoom, and Filter Interactivity

  • Building Interactive Dashboards with Shiny

  • Geospatial Data Visualization

  • Mapping Data with ggmap and sf Packages

  • Visualizing Spatial Data: Choropleth Maps, Heatmaps

  • Applications of Geospatial Visualization in Real-World Scenarios

Statistical Analysis and Machine Learning with R
Module 7: Statistical Analysis in R
​

  • Descriptive and Inferential Statistics

  • Overview of Basic Statistical Measures: Mean, Median, Mode, Variance, Standard Deviation

  • Hypothesis Testing: T-tests, Chi-Square Tests, ANOVA

  • Understanding p-values, Confidence Intervals, and Effect Sizes

  • Regression Analysis

  • Implementing Linear Regression: Simple and Multiple Linear Regression

  • Logistic Regression for Binary Outcomes

  • Model Evaluation: R-squared, Adjusted R-squared, AIC, BIC

Module 8: Machine Learning with R
​

  • Supervised Learning Techniques

  • Classification Models: Decision Trees, Random Forests, Support Vector Machines (SVM)

  • Regression Models: Ridge, Lasso, Elastic Net

  • Model Validation: Cross-Validation, Confusion Matrix, ROC Curves

  • Unsupervised Learning Techniques

  • Clustering Algorithms: K-Means, Hierarchical Clustering

  • Dimensionality Reduction: PCA, t-SNE

  • Case Study: Clustering and Dimensionality Reduction for Customer Segmentation

Real-World Applications and Capstone Project
Module 9: Advanced Topics and Real-World Applications
​

  • Big Data and R

  • Working with Big Data in R: Introduction to SparkR and Hadoop Integration

  • Managing Large Datasets with Data.Table and Dplyr

  • Case Study: Big Data Analysis in R

  • Ethics and Data Privacy in R

  • Data Privacy Concerns: GDPR and CCPA Compliance in Data Science

  • Ensuring Data Security in R Projects: Best Practices

  • Ethical Considerations in Data Science

Module 10: Capstone Project and Exam Preparation
​

  • Capstone Project

  • Participants work on a comprehensive data science project using R

  • Application of skills learned: Data Collection, Cleaning, Analysis, Visualization, and Machine Learning

  • Peer Review and Feedback on Project Work

  • Exam Preparation and Review

  • Review of Key Concepts Covered During the Course

  • Sample Exam Questions and Discussion

  • Final Q&A Session and Wrap-Up

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