Login

OTP sent to

Data Science With R Programming

Home > Courses > Data Science with R Programming

Data Science With R Programming

Data Science & Business Analytics

Duration
45 Hours

Course Description

             R is a popular programming language that allows people to adeptly handle mass amounts of data, generate publication-quality visualizations, and perform a range of statistical and analytic computing tasks. Used in fields including data science, finance, academia, and more, R is powerful, flexible, and extensible.

Course Outline For Data Science with R Programming

1. Introduction to R and RStudio

  • R Programming Fundamentals: Understanding basic syntax, variables, data types (vectors, matrices, lists, and data frames), operators, and control structures (loops, conditional statements).
  • RStudio IDE: Familiarization with the RStudio interface for efficient code writing, debugging, and visualization. 

2. Data manipulation and wrangling

  • Tidyverse Ecosystem: Introduction to the tidyverse, a collection of R packages designed for data science, 
  • Data Import/Export: Reading and writing data from various formats (CSV, Excel, databases) using packages like readr and readxl.
  • Data Cleaning and Preparation: Handling missing values, removing duplicates, and reshaping data using packages like tidyr.
  • Data Transformation: Manipulating dataframes, filtering rows, selecting columns, creating new variables, and summarizing data with dplyr functions like mutate(), select(), filter(), summarize(), and arrange(). 

3. Data Visualization

  • Fundamentals of Visualization: Understanding how to effectively visualize data and choose appropriate plot types.
  • ggplot2: Using the ggplot2 package to create high-quality, customizable visualizations like scatter plots, bar charts, histograms, box plots, and more.
  • Interactive Visualizations: Exploring packages like plotly and leaflet to create interactive plots and maps. 

4. Statistical analysis

  • Descriptive Statistics: Calculating measures of central tendency (mean, median), measures of variability (variance, standard deviation), and exploring data distributions.
  • Inferential Statistics: Performing hypothesis tests (t-tests, ANOVA, Chi-square tests), calculating confidence intervals, and understanding probability distributions.
  • Correlation and Regression Analysis: Measuring relationships between variables and building linear regression models with functions like cor() and lm(). 

5. Machine learning with R

  • Introduction to Machine Learning Concepts: Understanding supervised, unsupervised, and reinforcement learning principles.
  • Implementing Machine Learning Algorithms: Applying various algorithms like linear regression, logistic regression, decision trees, random forests, SVM, and clustering algorithms (k-means, hierarchical clustering) using packages like caret, randomForest, and e1071.
  • Model Evaluation: Assessing model performance using techniques like cross-validation and evaluating metrics like ROC curves.
  • Advanced Topics (Optional): Some courses might delve into more advanced topics like deep learning with R using Keras or specialized techniques like time series forecasting with the forecast package. 

6. Project-based learning and real-world applications

  • Hands-on Projects: Working on practical projects involving real-world datasets to solidify your understanding and build a portfolio.
  • Case Studies: Analyzing real-world scenarios and applying R to solve problems in areas like finance, healthcare, or marketing. 
Enquire Now