Course: The 60 Minute Masterclass on Writing Effective Resumes & CVs
The Data Analysis with R course welcomes you to a structured learning journey. This course is designed for individuals who want to strengthen their data analysis skills using R, a powerful language widely used in statistics and data science. It is organized into modular topics that build from setup to advanced techniques. In Introduction to R and RStudio, you will set up the environment and learn basic syntax and data types. Data Manipulation with dplyr introduces the tidyverse philosophy and practical wrangling techniques using filter, select, and mutate. A practical example demonstrates filtering the mtcars dataset for six-cylinder cars and selecting mpg, hp, and wt. Data Visualization with ggplot2 covers the principles of effective visualization and the creation of a variety of plots. The Statistical Modeling module introduces linear regression and guides you through building and evaluating models. Advanced Topics touch on time series analysis and machine learning basics with caret. By the end, you will be able to manipulate data in R, create informative visualizations, and build and interpret statistical models to support data-driven decisions. Getting Started recommends installing R and RStudio, engaging with exercises, and participating in discussions to deepen understanding. The course welcomes beginners and experienced learners alike, providing guidance through essential concepts and techniques. Through hands-on practice, you will gain confidence in analyzing data and communicating findings clearly. Let’s embark on this journey together and unlock the potential of data with R.Lesson
Data Analysis with R: Course Overview
Learning Outcomes
- Effectively use R for data manipulation and analysis.
- Create informative visualizations to communicate findings.
- Build and interpret statistical models to make data-driven decisions.
- Understand core concepts across modules including R setup, dplyr, ggplot2, linear regression, and introductory machine learning.
Conceptual Explanation
R and RStudio provide a versatile environment for data analysis, combining data wrangling, visualization, and modeling in a coherent workflow. The course structure guides learners through setup, manipulation, plotting, and modeling, with practical examples and hands-on exercises to reinforce understanding.
Bilingual (EN → বাংলা)
EN: R and RStudio provide a flexible platform for data analysis, supporting clean data manipulation, visualization, and modeling workflows. The course is organized into modules that build progressively from setup to advanced topics, with practical examples to reinforce learning.
বাংলা: R ও RStudio ডেটা বিশ্লেষণের জন্য একটি নমনীয় প্ল্যাটফর্ম প্রদান করে, যা তথ্য-সংশোধন, ভিজ্যুয়ালাইজেশন এবং মডেল নির্মাণের ওয়ার্কফ্লো সমর্থন করে। কোর্সটি মডিউলগুলিতে সংগঠিত যা সেটআপ থেকে উন্নত বিষয় পর্যন্ত ধারাবাহিকভাবে আরো বাড়ায়, এবং বাস্তব উদাহরণ দিয়ে শেখাকে শক্তিশালী করে।
Important Notes
- Prerequisites: familiarity with basic programming concepts is helpful but not required.
- Each module includes practical exercises and projects to apply what you learn.
- Engagement with discussions and continuous practice will deepen understanding.
- Code examples shown are representative and can be executed in RStudio.
Course Structure
| Module | Key Focus |
|---|---|
| Introduction to R and RStudio | Setting up environment; Basic R syntax and data types |
| Data Manipulation with dplyr | Introduction to the tidyverse; Data wrangling with filter(), select(), mutate() |
| Data Visualization with ggplot2 | Principles of effective visualization; Creating plots (e.g., scatterplots, bars) |
| Statistical Modeling | Introduction to linear regression; Building and evaluating models |
| Advanced Topics | Time series analysis; Machine learning basics with caret |
Learning Objectives
- Effectively use R for data manipulation and analysis.
- Create informative visualizations to communicate your findings.
- Build and interpret statistical models to make data-driven decisions.
Getting Started
Getting Started
- Install R and RStudio before starting the modules.
- Engage with the exercises and projects to apply what you learn.
- Participate in discussions and ask questions to deepen your understanding.
Conclusion
Conclusion
This course aims to provide you with the skills needed to analyze data effectively using R. Whether you are a beginner or someone with prior experience looking to enhance your skills, this course will guide you through the essential concepts and techniques in data analysis. Let’s embark on this journey together and unlock the potential of data!
Exercises → True / False / Fill in the Blanks / MCQ / Matching / Descriptive / Creative
True / False
The statement "R is primarily a visual tool" is true. (This is false; R is a programming language for statistics and data analysis.)
Answer
False. R is a general-purpose programming language for statistical computing and graphics, not solely a visual tool.
Fill in the Blanks
In the dplyr function to select variables, the typical syntax is select( ... ).
Answer
select(...): selects variables from a data frame by name.
MCQ
Which R package is primarily used for data visualization?
- a) dplyr
- b) ggplot2
- c) caret
- d) shiny
Answer
b) ggplot2
Matching
| Term | Definition |
|---|---|
| dplyr | Data wrangling verbs in the tidyverse |
| ggplot2 | Data visualization package |
| lm | Linear regression model function |
Answer
dplyr → Data wrangling verbs; ggplot2 → Data visualization; lm → Linear regression model
Descriptive
Describe how you would approach a data analysis task in this course.
Answer
Approach: define the question, import and clean data, manipulate using dplyr, visualize with ggplot2, and build a simple model using lm to draw data-driven conclusions.
Creative
Propose a mini-project that demonstrates end-to-end data analysis using R.
Answer
Mini-project: import a CSV dataset, clean it, compute summary statistics with dplyr, create visuals with ggplot2, and fit a linear model to predict a target variable. Present a brief write-up of insights.
Activities, Vocabulary, Homework, Practice Task
Activities
- Install R and RStudio and run the initial setup tasks.
- Experiment with filter, select, and mutate on a sample dataset.
- Create a ggplot2 visualization and interpret the results.
- Build and compare a simple linear model with real data.
Vocabulary
- Data wrangling, tidyverse, dplyr, ggplot2, lm, and related terms.
Homework
Complete a short data analysis project using a dataset of your choice, including code and a brief write-up of findings.
Practice Task
Practice Task: Import a dataset, apply dplyr filtering, compute a summary, visualize results, and fit a linear model to predict a numeric outcome.