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R for Data Science

Gain expertise in R for data cleaning, visualization, and statistical analysis.

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Certificate :

After Completion

Start Date :

10-Jan-2025

Duration :

30 Days

Course fee :

$150

COURSE DESCRIPTION:

  1. Discover the capabilities of R in data science and statistical analysis.

  2. This course covers R’s extensive tools for data manipulation, visualization, and modeling.

  3. Gain skills in data cleaning, analysis, and visualization using R.

  4. Explore the application of machine learning techniques for insight extraction.

  5. Enhance your ability to make informed, data-driven decisions with R.

CERTIFICATION:

  1. Earn a Certified Data Scientist with R credential to demonstrate your expertise in using R for data science and analytics.

LEARNING OUTCOMES:

By the conclusion of the course, participants will possess the skills to:

  1. Grasp the core principles of R, focusing on syntax, data structures, and functions.

  2. Utilize libraries such as dplyr and tidyr for data manipulation and cleaning.

  3. Create informative and interactive visualizations with ggplot2.

  4. Implement statistical techniques and models for hypothesis testing and predictive analysis.

  5. Develop and assess machine learning models using packages like caret and randomForest, while integrating R with databases and APIs for thorough data analysis.

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Course Curriculum

Introduction to R and Data Science
  1. Overview of R
    • Introduction to R programming language.
    • Setting up R and RStudio for data analysis.
    • Understanding the basic syntax and data types in R.
  2. What is Data Science?
    • Defining data science and its applications.
    • The role of R in data science and analytics.
    • R vs Python: Why R for Data Science?
R Programming Fundamentals
  1. Data Structures in R
    • Vectors, lists, matrices, arrays, and data frames.
    • Operations on R data structures.
    • Subsetting and indexing data.
  2. Control Structures
    • Conditional statements (if, else, switch).
    • Loops (for, while, repeat).
    • Functions and user-defined functions in R.
  3. Basic Data Manipulation
    • Using base R functions to clean and manipulate data.
    • Combining and merging datasets.
Data Visualization with R
  1. Basic Plotting in R
    • Creating basic plots using plot() function.
    • Customizing plots (colors, labels, titles).
  2. ggplot2 for Advanced Visualization
    • Introduction to the ggplot2 package for creating complex visualizations.
    • Plot types: scatter plots, bar charts, histograms, boxplots, etc.
    • Customizing and enhancing visualizations: themes, colors, labels, and legends.
    • Combining multiple plots (faceting and grid layouts).
  3. Interactive Visualizations
    • Using plotly and shiny for creating interactive web-based visualizations.
Data Cleaning and Preprocessing
  1. Handling Missing Data
    • Identifying and handling missing values in datasets.
    • Imputation techniques and removing missing values.
  2. Data Transformation
    • Using dplyr for data wrangling: filtering, selecting, mutating, and summarizing data.
    • Grouping data and performing aggregation operations.
    • Working with dates and times in R.
  3. Text Data Processing
    • Text mining with R: cleaning and transforming text data.
    • Working with tm and stringr packages for text analysis.
Statistical Analysis in R
  1. Descriptive Statistics
    • Calculating measures of central tendency and dispersion (mean, median, variance, standard deviation).
    • Frequency distributions and summarizing data.
  2. Hypothesis Testing
    • Introduction to hypothesis testing concepts.
    • t-tests, chi-square tests, ANOVA, and non-parametric tests.
  3. Correlation and Regression Analysis
    • Correlation analysis: Pearson’s, Spearman’s, and Kendall’s correlation.
    • Simple and multiple linear regression models.
    • Logistic regression and its applications.
  4. Statistical Models in R
    • Building statistical models using R’s lm(), glm(), and other functions.
    • Model diagnostics and interpretation of results.
Machine Learning with R
  1. Introduction to Machine Learning
    • What is machine learning and its importance in data science?
    • Types of machine learning: Supervised, unsupervised, and reinforcement learning.
  2. Supervised Learning Algorithms
    • Linear regression and logistic regression in R.
    • Decision trees, random forests, and gradient boosting.
    • k-Nearest Neighbors (k-NN), Support Vector Machines (SVM).
  3. Unsupervised Learning Algorithms
    • Clustering: K-means, hierarchical clustering, DBSCAN.
    • Dimensionality reduction techniques: Principal Component Analysis (PCA).
  4. Model Evaluation and Tuning
    • Cross-validation and overfitting.
    • Model evaluation metrics: accuracy, precision, recall, F1-score, ROC curve.
    • Hyperparameter tuning using caret package.
Time Series Analysis
  1. Understanding Time Series Data
    • Introduction to time series data and its components: trend, seasonality, and noise.
    • Time series decomposition using R.
  2. Time Series Forecasting
    • ARIMA, Exponential Smoothing, and other forecasting models.
    • Using the forecast package for time series analysis and prediction.
  3. Advanced Time Series Models
    • Handling missing values in time series data.
    • Seasonal adjustments and forecasting accuracy.
Capstone Project
  1. Build a Full Data Science Solution
    • Implement a data science project from start to finish.
    • Demonstrating your ability to clean, visualize, analyze, model, and report findings with R.
    • Example projects: Fraud detection, customer segmentation, sentiment analysis, predictive maintenance, etc.

Training Features

Hands-on Projects

Practical exercises and projects focused on real-world data science challenges, such as sentiment analysis, sales forecasting, and data classification.

Comprehensive R Programming Skills

Learn R programming from the basics to advanced techniques, covering both statistical analysis and machine learning.

Data Visualization with ggplot2

Master data visualization using ggplot2, creating compelling plots, and interactive dashboards with R.

Statistical Analysis

Gain proficiency in performing hypothesis testing, regression analysis, and building statistical models.

Career-Ready Skills

Learn the essential tools and techniques to become proficient in data science, preparing you for job roles like Data Scientist, Data Analyst, and Machine Learning Engineer.

Certification

Receive a certificate upon successful completion, validating your skills in R for data science and analytics.

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