R Programming for Statistics and Data Science
R Programming for Statistics and Data Science, available at $89.99, has an average rating of 4.55, with 128 lectures, 16 quizzes, based on 5207 reviews, and has 29260 subscribers.
You will learn about Learn the fundamentals of programming in R Work with R’s conditional statements, functions, and loops Build your own functions in R Get your data in and out of R Learn the core tools for data science with R Manipulate data with the Tidyverse ecosystem of packages Systematically explore data in R The grammar of graphics and the ggplot2 package Visualise data: plot different types of data & draw insights Transform data: best practices of when and how Index, slice, and subset data Learn the fundamentals of statistics and apply them in practice Hypothesis testing in R Understand and carry out regression analysis in R Work with dummy variables Learn to make decisions that are supported by the data! Have fun by taking apart Star Wars and Pokemon data, as well some more serious data sets This course is ideal for individuals who are Aspiring data scientists or Beginners to programming or People interested in statistics and data analysis or Anyone who wants to learn how to code and apply their skills in practice It is particularly useful for Aspiring data scientists or Beginners to programming or People interested in statistics and data analysis or Anyone who wants to learn how to code and apply their skills in practice.
Enroll now: R Programming for Statistics and Data Science
Summary
Title: R Programming for Statistics and Data Science
Price: $89.99
Average Rating: 4.55
Number of Lectures: 128
Number of Quizzes: 16
Number of Published Lectures: 126
Number of Published Quizzes: 15
Number of Curriculum Items: 144
Number of Published Curriculum Objects: 141
Original Price: $189.99
Quality Status: approved
Status: Live
What You Will Learn
- Learn the fundamentals of programming in R
- Work with R’s conditional statements, functions, and loops
- Build your own functions in R
- Get your data in and out of R
- Learn the core tools for data science with R
- Manipulate data with the Tidyverse ecosystem of packages
- Systematically explore data in R
- The grammar of graphics and the ggplot2 package
- Visualise data: plot different types of data & draw insights
- Transform data: best practices of when and how
- Index, slice, and subset data
- Learn the fundamentals of statistics and apply them in practice
- Hypothesis testing in R
- Understand and carry out regression analysis in R
- Work with dummy variables
- Learn to make decisions that are supported by the data!
- Have fun by taking apart Star Wars and Pokemon data, as well some more serious data sets
Who Should Attend
- Aspiring data scientists
- Beginners to programming
- People interested in statistics and data analysis
- Anyone who wants to learn how to code and apply their skills in practice
Target Audiences
- Aspiring data scientists
- Beginners to programming
- People interested in statistics and data analysis
- Anyone who wants to learn how to code and apply their skills in practice
R Programming for Statistics and Data Science 2023
R Programming is a skill you need if you want to work as a data analyst or a data scientist in your industry of choice. And why wouldn’t you? Data scientist is the hottest ranked profession in the US.
But to do that, you need the tools and the skill set to handle data. R is one of the top languages to get you where you want to be. Combine that with statistical know-how, and you will be well on your way to your dream title.
This course is packing all of this, and more, in one easy-to-handle bundle, and it’s the perfect start to your journey.
So, welcome to R for Statistics and Data Science!
R for Statistics and Data Science is the course that will take you from a complete beginner in programming with R to a professional who can complete data manipulation on demand. It gives you the complete skill set to tackle a new data science project with confidence and be able to critically assess your work and others’.
Laying strong foundations
This course wastes no time and jumps right into hands-on coding in R. But don’t worry if you have never coded before, we start off light and teach you all the basics as we go along! We wanted this to be an equally satisfying experience for both complete beginners and those of you who would just like a refresher on R.
What makes this course different from other courses?
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Well-paced learning.
Receive top class training with content which we’ve built – and rigorously edited – to deliver powerful and efficient results.
Even though preferred learning paces differ from student to student, we believe that being challenged just the right amount underpins the learning that sticks.
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Introductory guide to statistics.
We will take you through descriptive statistics and the fundamentals of inferential statistics.
We will do it in a step-by-step manner, incrementally building up your theoretical knowledge and practical skills.
You’ll master confidence intervals and hypothesis testing, as well as regression and cluster analysis.
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The essentials of programming – R-based.
Put yourself in the shoes of a programmer, rise above the average data scientist and boost the productivity of your operations.
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Data manipulation and analysis techniques in detail.
Learn to work with vectors, matrices, data frames, and lists.
Become adept in ‘the Tidyverse package’ – R’s most comprehensive collection of tools for data manipulation – enabling you to index and subset data, as well as spread(), gather(), order(), subset(), filter(), arrange(), and mutate() it.
Create meaning-heavy data visualizations and plots.
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Practice makes perfect.
Reinforce your learning through numerous practical exercises, made with love, for you, by us.
What about homework, projects, & exercises?
There is a ton of homework that will challenge you in all sorts of ways. You will have the chance to tackle the projects by yourself or reach out to a video tutorial if you get stuck.
You: Is there something to show for the skills I will acquire?
Us:Indeed, there is – a verifiable certificate.
You will receive a verifiable certificate of completion with your name on it. You can download the certificate and attach it to your CV and even post it on your LinkedIn profile to show potential employers you have experience in carrying out data manipulations & analysis in R.
If that sounds good to you, then welcome to the classroom 🙂
Course Curriculum
Chapter 1: Introduction
Lecture 1: Ten Things You Will Learn in This Course
Chapter 2: Getting started
Lecture 1: Intro
Lecture 2: Downloading and installing R & RStudio
Lecture 3: Quick guide to the RStudio user interface
Lecture 4: Changing the appearance in RStudio
Lecture 5: Installing packages in R and using the library
Chapter 3: The building blocks of R
Lecture 1: Creating an object in R
Lecture 2: Exercise 1 Creating an object in R
Lecture 3: Data types in R – Integers and doubles
Lecture 4: Data types in R – Characters and logicals
Lecture 5: Exercise 2 Data types in R
Lecture 6: Coercion rules in R
Lecture 7: Exercise 3 Coercion rules in R
Lecture 8: Functions in R
Lecture 9: Exercise 4 Using functions in R
Lecture 10: Functions and arguments
Lecture 11: Exercise 5 The arguments of a function
Lecture 12: Building a function in R (basics)
Lecture 13: Exercise 6 Building a function in R
Lecture 14: Using the script vs. using the console
Chapter 4: Vectors and vector operations
Lecture 1: Intro
Lecture 2: Introduction to vectors
Lecture 3: Vector recycling
Lecture 4: Exercise 7 Vector recycling
Lecture 5: Naming a vector in R
Lecture 6: Exercise 8 Vector attributes – names
Lecture 7: Getting help with R
Lecture 8: Slicing and indexing a vector in R
Lecture 9: Exercise 9 Indexing and slicing a vector
Lecture 10: Changing the dimensions of an object in R
Lecture 11: Exercise 10 Vector attributes – dimensions
Chapter 5: Matrices
Lecture 1: Creating a matrix in R
Lecture 2: Faster code: creating a matrix in a single line of code
Lecture 3: Exercise 11 Creating a matrix in R
Lecture 4: Do matrices recycle?
Lecture 5: Indexing an element from a matrix
Lecture 6: Slicing a matrix in R
Lecture 7: Exercise 12 Indexing and slicing a matrix
Lecture 8: Matrix arithmetic
Lecture 9: Exercise 13 Matrix arithmetic
Lecture 10: Matrix operations in R
Lecture 11: Exercise 14 Matrix operations
Lecture 12: Categorical data
Lecture 13: Creating a factor in R
Lecture 14: Exercise 15 Creating a factor in R
Lecture 15: Lists in R
Lecture 16: Exercise: Lists in R
Lecture 17: Completed 33% of the course
Chapter 6: Fundamentals of programming with R
Lecture 1: Relational operators in R
Lecture 2: Logical operators in R
Lecture 3: Vectors and logicals operators
Lecture 4: Exercise Logical operators
Lecture 5: If, else, else if statements in R
Lecture 6: Exercise If, else, else if statements in R
Lecture 7: If, else, else if statements – Keep-In-Mind's
Lecture 8: For loops in R
Lecture 9: Exercise: For Loops in R
Lecture 10: While loops in R
Lecture 11: Exercise: While loops in R
Lecture 12: Repeat loops in R
Lecture 13: Building a function in R 2.0
Lecture 14: Building a function in R 2.0 – Scoping
Lecture 15: Exercise Scoping
Lecture 16: Completed 50% of the course
Chapter 7: Data frames
Lecture 1: Intro
Lecture 2: Creating a data frame in R
Lecture 3: Exercise 16 Creating a data frame in R
Lecture 4: The Tidyverse package
Lecture 5: Data import in R
Lecture 6: Importing a CSV in R
Lecture 7: Data export in R
Lecture 8: Exercise 17 Importing and exporting data in R
Lecture 9: Getting a sense of your data frame
Lecture 10: Indexing and slicing a data frame in R
Lecture 11: Extending a data frame in R
Lecture 12: Exercise 18 Data frame operations
Lecture 13: Dealing with missing data in R
Chapter 8: Manipulating data
Lecture 1: Intro
Lecture 2: Data transformation with R – the Dplyr package – Part I
Instructors
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365 Careers
Creating opportunities for Data Science and Finance students -
365 Simona (The 365 Team)
Data Science Instructor
Rating Distribution
- 1 stars: 31 votes
- 2 stars: 56 votes
- 3 stars: 431 votes
- 4 stars: 1809 votes
- 5 stars: 2880 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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