R Programming for Statistics and Data Science course walks through the essential concepts of R programming required for the Statistics and Data Science field. The course is an all-in-one package where students can learn coding in R from scratch.
The course is suitable for students who want to learn R, statistics, and data science to brush up their skill set as well as for aspiring data scientists who want to implement R in data analysis. The course is usually available for INR 3,499 on Udemy but you can click now to get 87% off and get the R Programming for Statistics and Data Science 2023 Course for INR 449.
Who all can opt for this course?
- Aspiring Data Scientists
- Programmers
- Those with an Interest in Data Analysis and Statistics
- Everyone who wants to get Coding Knowledge and Practise using it
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 06 hours |
| Rating | 4.6/5 |
| Student Enrollment | 24,928 students |
| Instructor | 365 Careers https://www.linkedin.com/in/365careers |
| Topics Covered | Introduction to Statistics, Essentials of R Programming, Data Manipulation & Analysis, etc. |
| Course Level | Beginner |
| Total Student Reviews | 4,308 |
Learning Outcomes
- Learn the fundamentals of R programming
- Work with the loops, functions, and conditional statements provided by R
- Create custom functions in R
- Bring your data into R and out again
- Utilize the ecosystem of packages in the Tidyverse to manipulate data
- R data exploration
- Learn about the ggplot2 package and visual syntax
- Visualize data to plot various sorts of data and derive conclusions
- Best practices regarding when and how to transform data
- Data by index, slice, and subset
- Learn the foundations of statistics and use them in real-world situations
- R hypothesis testing
- Recognize regression analysis in R and perform it
- Make use of dummy variables
- Develop the ability to make informed decisions
- Enjoy dissecting data from Star Wars and Pokemon as well as more important data sets
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Introduction (03 minutes) | Ten Things You Will Learn in This Course |
| 2. | Getting started (18 minutes) | Intro |
| Downloading and installing R & RStudio | ||
| Quick guide to the RStudio user interface | ||
| RStudio’s GUI | ||
| Changing the appearance in RStudio | ||
| Installing packages in R and using the library | ||
| 3. | The building blocks of R (35 minutes) | Creating an object in R |
| Exercise 1 Creating an object in R | ||
| Data types in R – Integers and doubles | ||
| Data types in R – Characters and logicals | ||
| Objects and Data Types | ||
| Exercise 2 Data types in R | ||
| Coercion rules in R | ||
| Exercise 3 Coercion rules in R | ||
| Functions in R | ||
| Exercise 4 Using functions in R | ||
| Functions and arguments | ||
| Exercise 5 The arguments of a function | ||
| Building a function in R (basics) | ||
| Objects and Functions | ||
| Exercise 6 Building a function in R | ||
| Using the script vs. using the console | ||
| 4. | Vectors and vector operations (29 minutes) | Intro |
| Introduction to vectors | ||
| Vector recycling | ||
| Exercise 7 Vector recycling | ||
| Naming a vector in R | ||
| Exercise 8 Vector attributes – names | ||
| Introduction to vectors | ||
| Getting help with R | ||
| Getting Help with R | ||
| Slicing and indexing a vector in R | ||
| Extracting elements from a vector | ||
| Exercise 9 Indexing and slicing a vector | ||
| Changing the dimensions of an object in R | ||
| Exercise 10 Vector attributes – dimensions | ||
| 5. | Matrices (49 minutes) | Creating a matrix in R |
| Faster code: creating a matrix in a single line of code | ||
| Creating a matrix | ||
| Exercise 11 Creating a matrix in R | ||
| Do matrices recycle? | ||
| Indexing an element from a matrix | ||
| Slicing a matrix in R | ||
| Exercise 12 Indexing and slicing a matrix | ||
| Matrix arithmetic | ||
| Exercise 13 Matrix arithmetic | ||
| Matrix operations in R | ||
| Matrix operations | ||
| Exercise 14 Matrix operations | ||
| Categorical data | ||
| Creating a factor in R | ||
| Factors in R | ||
| Exercise 15 Creating a factor in R | ||
| Lists in R | ||
| Exercise: Lists in R | ||
| Completed 33% of the course | ||
| 6. | Fundamentals of programming with R (45 minutes) | Relational operators in R |
| Logical operators in R | ||
| Vectors and logicals operators | ||
| Relational and Logical operators in R | ||
| Exercise Logical operators | ||
| If, else, else if statements in R | ||
| Exercise If, else, else if statements in R | ||
| If, else, else if statements – Keep-In-Mind’s | ||
| For loops in R | ||
| Exercise: For Loops in R | ||
| While loops in R | ||
| Exercise: While loops in R | ||
| Repeat loops in R | ||
| Loops in R | ||
| Building a function in R 2.0 | ||
| Building a function in R 2.0 – Scoping | ||
| Exercise Scoping | ||
| Completed 50% of the course | ||
| 7. | Data frames (37 minutes) | Intro |
| Creating a data frame in R | ||
| Exercise 16 Creating a data frame in R | ||
| The Tidyverse package | ||
| Data import in R | ||
| Importing a CSV in R | ||
| Data export in R | ||
| Exercise 17 Importing and exporting data in R | ||
| Creating data frames | ||
| Getting a sense of your data frame | ||
| Indexing and slicing a data frame in R | ||
| Data frame operations | ||
| Extending a data frame in R | ||
| Exercise 18 Data frame operations | ||
| Dealing with missing data in R | ||
| 8. | Manipulating data (26 minutes) | Intro |
| Data transformation with R – the Dplyr package – Part I | ||
| Data transformation with R – the Dplyr package – Part II | ||
| Sampling data with the Dplyr package | ||
| Using the pipe operator in R | ||
| Manipulating data | ||
| Exercise 19 Data transformation with Dplyr | ||
| Tidying data in R – gather() and separate() | ||
| Tidying data in R – unite() and spread() | ||
| Tidying data | ||
| Exercise 20 Data tidying with Tidyr | ||
| 9. | Visualizing data (44 minutes) | Intro |
| Intro to data visualization | ||
| Intro to ggplot2 | ||
| Variables: revisited | ||
| Building a histogram with ggplot2 | ||
| Exercise 21 Building a histogram with ggplot2 | ||
| Building a bar chart with ggplot2 | ||
| Exercise 22 Building a bar chart with ggplot2 | ||
| Building a box and whiskers plot with ggplot2 | ||
| Exercise 23 Building a box plot with ggplot2 | ||
| Building a scatterplot with ggplot2 | ||
| Exercise 24 Building a scatterplot with ggplot2 | ||
| 10. | Exploratory data analysis (26 minutes) | Population vs. sample |
| Mean, median, mode | ||
| Skewness | ||
| Exercise 25 Determining Skewness | ||
| Variance, standard deviation, and coefficient of variability | ||
| Covariance and correlation | ||
| Exercise 26 Practical example with real estate data | ||
| 11. | Hypothesis Testing (56 minutes) | Distributions |
| Standard Error and Confidence Intervals | ||
| Hypothesis testing | ||
| Type I and Type II errors | ||
| Test for the mean-population variance known | ||
| Exercise: Test for the mean-population variance known | ||
| The P-value | ||
| Test for the mean – Population variance unknown | ||
| Exercise: Test for the mean-population variance unknown | ||
| Comparing two means – Dependent samples | ||
| Exercise: Comparing two means – Dependent samples | ||
| Comparing two means – Independent samples | ||
| 12. | Linear Regression Analysis (26 minutes) | The linear regression model |
| Correlation vs regression | ||
| Geometrical representation | ||
| First regression in R | ||
| How to interpret the regression table | ||
| Exercise: Doing a regression in R | ||
| Decomposition of variability: SST, SSR, SSE | ||
| R-squared | ||
| Completed 100% of the course |
Resources Required
- R Studio – Instructors will walk you through the installation process
Featured Review
R K (5/5): Simona is an excellent instructor. Course is short but a good introduction to R . You never get bored & simona made fun as well as informative. The best part is she responds to every question you have. Great course! Thanks.
Pros
- Sadanand Shirke (5/5): This is the best course before we get in to R.
- Abril Izquierdo (5/5): In my case I am self taught in R, but never took any course or lesson, so I just wanted to perfect the basics, and it did!
- Amitav Gupta (5/5): Best pick to start off the journey of data Analysis 3.Good Explanation of the syntaxes used
- Rachel Reed (5/5): Since I have no programming experience, this course has been excellent.
Cons
- Rachel Liu (2/5): This is at least the 11th courses I am taking in Udemy.
- Andre Munoz (2/5): The problem here is that the code examples do the bare minimum to illustrate a technique but are meaningless (random number sets) or worse, impractical (such as the role playing card game) for any real world business situation.
- Esmail Afsah (1/5): this is an awful course – I have done a 2 other courses on R on Udemy, either better – especially the one by Kirill Eremenko
About the Author
The course is instructed by 365 Careers who are creating opportunities for Data Science and Finance students. With a 4.6 instructor rating and 6,18,572 reviews on Udemy, they offer 91 courses and have taught 2,143,657 students so far.
- On Udemy, 365 Careers is the top-selling provider of courses in business, finance, and data science.
- In 210 different countries, more than 2,000,000 students have taken the company’s courses.
- People who have finished 365 Careers training now work at renowned companies like Apple, PayPal, and Citibank.
- On Udemy right now, 365 focuses on the following subjects: Finance, Data Science, Entrepreneurship, Office Productivity, Business, and Blockchain.
- The courses offered by 365 Careers are the ideal place to start whether you want to work as a financial analyst, data scientist, business analyst, data analyst, business intelligence analyst, business executive, finance manager, FP&A analyst, investment banker, or entrepreneur.
Comparison Table
| Parameters | R Programming for Statistics and Data Science 2023 | Logistic Regression in Python | Support Vector Machines in Python: SVM Concepts & Code |
|---|---|---|---|
| Offers | INR 455 ( | INR 455 ( | INR 455 ( |
| Duration | 6.5 hours | 7.5 hours | 6.5 hours |
| Rating | 4.6 /5 | 4.6 /5 | 4.5 /5 |
| Student Enrollments | 24,926 | 98,702 | 84,824 |
| Instructors | 365 Careers | Start-Tech Academy | Start-Tech Academy |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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