• Overview of R
  • History and evolution of R.
  • Key features and applications in data analysis.
• Setting Up R Environment
  • Installing R and RStudio on different operating systems.
  • Navigating the RStudio: Console, Source, Environment, and History panels.

• Introduction to R Syntax
  • Basic syntax, variables, and data types.
  • Arithmetic operations and basic functions.
• Control Flow
  • Conditional statements: if, else, if-else.
  • Loops: for, while, and repeat.
  • Break and next statements.
• Functions
  • Writing and using functions.
  • Arguments and return values.

• Vectors
  • Creating, accessing, and modifying vectors.
  • Vectorized operations.
• Matrices
  • Creating and manipulating matrices.
  • Matrix operations.
• Lists
  • Creating and using lists.
  • Accessing list elements.
• Data Frames
  • Creating, accessing, and modifying data frames.
  • Data frame operations.
• Factors
  • Creating and using factors.
  • Factor levels and reordering.

• Reading Data into R
  • Reading CSV, Excel, and JSON files.
  • Using readr and readxl packages.
• Exporting Data
  • Writing data to CSV, Excel, and JSON files.
  • Using write.csv, write.xlsx, and jsonlite.

• Introduction to dplyr
  • Using select, filter, mutate, arrange, and summarize functions.
  • Grouping data with group_by.
• Data Tidying with tidyr
  • Using gather, spread, separate, and unite functions.
  • Reshaping and tidying data.

• Introduction to ggplot2
  • Understanding the grammar of graphics.
  • Creating basic plots: scatter plots, line charts, bar charts.
• Customizing Plots
  • Adding titles, labels, and themes.
  • Customizing colors, shapes, and sizes.
• Advanced Visualizations
  • Creating faceted plots.
  • Using ggplot2 extensions for specialized plots.

• Introduction to googleVis
  • Understanding the grammar of graphics.
  • Creating basic and advanced plots: scatter plots, line charts, bar charts.

• Calculating Descriptive Statistics
  • Mean, median, mode, variance, and standard deviation.
• Data Summarization
  • Using summary, aggregate, and other summary functions.

• Hypothesis Testing
  • Introduction to null and alternative hypotheses.
  • Performing t-tests and chi-square tests.
• Correlation and Regression Analysis
  • Pearson and Spearman correlation.
  • Simple and multiple linear regression.
  • Logistic regression.

• Introduction to Time Series Data
  • Understanding time series components.
• Time Series Decomposition
  • Decomposing time series data into trend, seasonal, and residual components.
• Time Series Forecasting
  • Using ARIMA and exponential smoothing models for forecasting.

• Introduction to Machine Learning
  • Key concepts and terminology.
  • Overview of supervised and unsupervised learning.
• Supervised Learning Algorithms
  • Linear and logistic regression.
  • Decision trees and random forests.
• Unsupervised Learning Algorithms
  • K-means clustering and hierarchical clustering.
• Model Evaluation
  • Cross-validation and performance metrics.

• Dimensionality Reduction
  • Principal Component Analysis (PCA).
  • Factor Analysis.
• Natural Language Processing
  • Text mining and sentiment analysis.
• Network Analysis
  • Creating and analysing network graphs.

Comprehensive Analytics Project

Data Science using R Programming involves the application of R, a powerful statistical programming language, to analyze, visualize, and interpret complex datasets. R is widely used in data science for its extensive libraries and packages that support statistical analysis, machine learning, and data visualization, making it a preferred tool for data scientists and analysts.

R Programming is essential for anyone looking to build a career in data science because of its strong statistical capabilities, comprehensive packages for data analysis, and the ability to handle large datasets efficiently. Learning R enhances your ability to perform sophisticated data analysis, making you a valuable asset in industries that rely on data-driven decision-making.

The course is suitable for aspiring data scientists, statisticians, researchers, analysts, and professionals in fields such as economics, finance, healthcare, and marketing. It is also ideal for career changers looking to enter the field of data science. Basic knowledge of programming and statistics is recommended but not mandatory, as foundational concepts will be covered.

The course covers a range of topics, including R programming basics, data manipulation, data visualization using ggplot2, statistical modeling, machine learning algorithms, data cleaning, exploratory data analysis (EDA), and advanced topics such as time series analysis and text mining.

R Programming is specifically designed for statistical analysis and data visualization, making it a preferred choice for data scientists focused on statistical modeling. While Python is more versatile and widely used in various domains, R’s specialized packages like ggplot2, dplyr, and caret provide powerful tools for data analysis, especially in academic and research settings.

Basic understanding of computer operations and a keen interest in data science are the primary prerequisites. Familiarity with programming concepts and basic statistics will be advantageous, but the course is designed to start with fundamental concepts, making it accessible to beginners.

Completing this course opens up numerous career paths, including roles such as Data Scientist, Data Analyst, Statistician, Research Analyst, and Business Intelligence Analyst. Industries such as finance, healthcare, marketing, and academia are constantly seeking professionals skilled in R for data-driven roles.

The course includes hands-on projects, case studies, and practical assignments that simulate real-world data science problems. You will work with actual datasets, applying R Programming techniques to solve business challenges, which prepares you to tackle data-driven issues in your career effectively.

Yes, upon successful completion of the course, you will receive a certification from Ampersand Academy that validates your expertise in Data Science using R Programming. This certificate is widely recognized and can enhance your professional credibility in the job market.

Students receive support through one-on-one mentorship, course materials, and interactive Q&A sessions with instructors. Additionally, we offer career support services, including resume building, mock interviews, and job placement assistance.

Yes, the course is available online with flexible scheduling options to accommodate working professionals. You can choose from live instructor-led sessions or self-paced learning modules, ensuring you can balance your studies with your professional commitments.

You will learn to work with various R packages such as dplyr, ggplot2, tidyr, caret, and shiny. The course also covers RStudio, a popular IDE for R, and introduces tools for data visualization, statistical modeling, machine learning, and data manipulation.

While all our data science courses are designed to provide comprehensive training, the Data Science using R Programming course focuses more on statistical analysis and visualization using R. It is ideal for those who wish to specialize in data analysis and statistical modeling, particularly in academic and research environments.

Throughout the course, you will engage in projects such as predictive modeling, customer segmentation, sentiment analysis, and time series forecasting. These projects are designed to apply the concepts learned in class to real-world data scenarios, helping you build a strong portfolio.

Our job placement assistance program includes resume enhancement, interview preparation, and access to job openings through our HR network and alumni connections. We also provide project portfolio assistance to showcase your practical experience, significantly improving your job prospects in the field of data science.