OVERVIEW
The R Programming for Data Science is the best way for you to gain deep insight and knowledge of this topic. You will learn from industry experts and obtain an endorsed certificate after completing the course. Enrol now for a limited-time discounted price.
In this course students will be able to start their career in Data Science by learning and practicing the basics of R Language. Students will learn how to install and configure R and RStudio and how to create various data structures. They will solve simple data problems using various R methods, functions, and packages. Students will also understand and use different data gathering, manipulation, and plotting methods to best extract information from raw data. At the end of the course, students will deploy what they have learned in a COVID-19 analysis task.
Like all the courses of Study Booth, this R Programming for Data Science is designed with the utmost attention and thorough research. All the topics are broken down into easy to understand bite-sized modules that help our learners to understand each lesson very easily.
We don’t just provide courses at Study Booth; we provide a rich learning experience. After purchasing a course from Study Booth, you get complete 1-year access with tutor support.
Our expert instructors are always available to answer all your questions and make your learning experience exquisite.
After completing the R Programming for Data Science, you will instantly get an e-certificate that will help you get jobs in the relevant field and will enrich your CV.
If you want to learn about this topic and achieve a certificate, you should consider this R Programming for Data Science from Study Booth.
We are very upfront and clear about all the costs of the course.
COURSE DESIGN
The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace.
You are taught through a combination of
- Video lessons
- Online study materials
Certificate of Achievement
Endorsed Certificate of Achievement from the Quality Licence Scheme
After successfully completing the course, learners will be able to order an endorsed certificate as proof of their new achievement. Endorsed certificates can be ordered and get delivered to your home by post for only £109. There is an additional £10 postage charge for international students.
CPD Certification from Study Booth
After successfully completing the assessment of this course, you will qualify for the CPD Certificate from Study Booth as proof of your continued expert development. Certification is available in PDF format, at the cost of £9, or a hard copy can be sent to you via post, at the cost of £15.
Endorsement
This course has been endorsed by the Quality Licence Scheme for its high-quality, non-regulated provision and training programmes. This course is not regulated by Ofqual and is not an endorsed lesson. Study Booth will be able to advise you on any further recognition, for example, progression routes into further and/or higher education. For further information, please visit the Learner FAQs on the Quality Licence Scheme website.
Method of Assessment
To assess your learning, you have to complete the assignment questions provided at the end of the course. You have to score at least 60% to pass the exam and to qualify for Quality Licence Scheme endorsed, and CPD endorsed certificates.
After submitting the assignment, our expert tutor will assess your assignment and will give you feedback on your performance.
After passing the assignment exam, you will be able to apply for a certificate.
WHY STUDY THIS COURSE
Whether you’re an existing practitioner or aspiring expert, this course will enhance your expertise and boost your CV with key skills and an endorsed lesson attesting to your knowledge.
The R Programming for Data Science is open to all, with no formal entry requirements. All you need is a passion for learning, a good understanding of the English language, numeracy and IT, and to be over the age of 16.
Course Curriculum
| Unit 01: Data Science Overview | |||
| Introduction to Data Science | 00:01:00 | ||
| Data Science: Career of the Future | 00:04:00 | ||
| What is Data Science? | 00:02:00 | ||
| Data Science as a Process | 00:02:00 | ||
| Data Science Toolbox | 00:03:00 | ||
| Data Science Process Explained | 00:05:00 | ||
| What’s Next? | 00:01:00 | ||
| Unit 02: R and RStudio | |||
| Engine and coding environment | 00:03:00 | ||
| Installing R and RStudio | 00:04:00 | ||
| RStudio: A quick tour | 00:04:00 | ||
| Unit 03: Introduction to Basics | |||
| Arithmetic with R | 00:03:00 | ||
| Variable assignment | 00:04:00 | ||
| Basic data types in R | 00:03:00 | ||
| Unit 04: Vectors | |||
| Creating a vector | 00:05:00 | ||
| Naming a vector | 00:04:00 | ||
| Arithmetic calculations on vectors | 00:07:00 | ||
| Vector selection | 00:06:00 | ||
| Selection by comparison | 00:04:00 | ||
| Unit 05: Matrices | |||
| What’s a Matrix? | 00:02:00 | ||
| Analyzing Matrices | 00:03:00 | ||
| Naming a Matrix | 00:05:00 | ||
| Adding columns and rows to a matrix | 00:06:00 | ||
| Selection of matrix elements | 00:03:00 | ||
| Arithmetic with matrices | 00:07:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 06: Factors | |||
| What’s a Factor? | 00:02:00 | ||
| Categorical Variables and Factor Levels | 00:04:00 | ||
| Summarizing a Factor | 00:01:00 | ||
| Ordered Factors | 00:05:00 | ||
| Unit 07: Data Frames | |||
| What’s a Data Frame? | 00:03:00 | ||
| Creating Data Frames | 00:20:00 | ||
| Selection of Data Frame elements | 00:03:00 | ||
| Conditional selection | 00:03:00 | ||
| Sorting a Data Frame | 00:03:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 08: Lists | |||
| Why would you need lists? | 00:01:00 | ||
| Creating a List | 00:06:00 | ||
| Selecting elements from a list | 00:03:00 | ||
| Adding more data to the list | 00:02:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 09: Relational Operators | |||
| Equality | 00:03:00 | ||
| Greater and Less Than | 00:03:00 | ||
| Compare Vectors | 00:03:00 | ||
| Compare Matrices | 00:02:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 10: Logical Operators | |||
| AND, OR, NOT Operators | 00:04:00 | ||
| Logical operators with vectors and matrices | 00:04:00 | ||
| Reverse the result: (!) | 00:01:00 | ||
| Relational and Logical Operators together | 00:06:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 11: Conditional Statements | |||
| The IF statement | 00:04:00 | ||
| IF…ELSE | 00:03:00 | ||
| The ELSEIF statement | 00:05:00 | ||
| Full Exercise | 00:03:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 12: Loops | |||
| Write a While loop | 00:04:00 | ||
| Looping with more conditions | 00:04:00 | ||
| Break: stop the While Loop | 00:04:00 | ||
| What’s a For loop? | 00:02:00 | ||
| Loop over a vector | 00:02:00 | ||
| Loop over a list | 00:03:00 | ||
| Loop over a matrix | 00:04:00 | ||
| For loop with conditionals | 00:01:00 | ||
| Using Next and Break with For loop | 00:03:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 13: Functions | |||
| What is a Function? | 00:02:00 | ||
| Arguments matching | 00:03:00 | ||
| Required and Optional Arguments | 00:03:00 | ||
| Nested functions | 00:02:00 | ||
| Writing own functions | 00:03:00 | ||
| Functions with no arguments | 00:02:00 | ||
| Defining default arguments in functions | 00:04:00 | ||
| Function scoping | 00:02:00 | ||
| Control flow in functions | 00:03:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 14: R Packages | |||
| Installing R Packages | 00:01:00 | ||
| Loading R Packages | 00:04:00 | ||
| Different ways to load a package | 00:02:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 15: The Apply Family - lapply | |||
| What is lapply and when is used? | 00:04:00 | ||
| Use lapply with user-defined functions | 00:03:00 | ||
| lapply and anonymous functions | 00:01:00 | ||
| Use lapply with additional arguments | 00:04:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 16: The apply Family – sapply & vapply | |||
| What is sapply? | 00:02:00 | ||
| How to use sapply | 00:02:00 | ||
| sapply with your own function | 00:02:00 | ||
| sapply with a function returning a vector | 00:02:00 | ||
| When can’t sapply simplify? | 00:02:00 | ||
| What is vapply and why is it used? | 00:04:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 17: Useful Functions | |||
| Mathematical functions | 00:05:00 | ||
| Data Utilities | 00:08:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 18: Regular Expressions | |||
| grepl & grep | 00:04:00 | ||
| Metacharacters | 00:05:00 | ||
| sub & gsub | 00:02:00 | ||
| More metacharacters | 00:04:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 19: Dates and Times | |||
| Today and Now | 00:02:00 | ||
| Create and format dates | 00:06:00 | ||
| Create and format times | 00:03:00 | ||
| Calculations with Dates | 00:03:00 | ||
| Calculations with Times | 00:07:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 20: Getting and Cleaning Data | |||
| Get and set current directory | 00:04:00 | ||
| Get data from the web | 00:04:00 | ||
| Loading flat files | 00:03:00 | ||
| Loading Excel files | 00:05:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 21: Plotting Data in R | |||
| Base plotting system | 00:03:00 | ||
| Base plots: Histograms | 00:03:00 | ||
| Base plots: Scatterplots | 00:05:00 | ||
| Base plots: Regression Line | 00:03:00 | ||
| Base plots: Boxplot | 00:03:00 | ||
| Unit 22: Data Manipulation with dplyr | |||
| Introduction to dplyr package | 00:04:00 | ||
| Using the pipe operator (%>%) | 00:02:00 | ||
| Columns component: select() | 00:05:00 | ||
| Columns component: rename() and rename_with() | 00:02:00 | ||
| Columns component: mutate() | 00:02:00 | ||
| Columns component: relocate() | 00:02:00 | ||
| Rows component: filter() | 00:01:00 | ||
| Rows component: slice() | 00:04:00 | ||
| Rows component: arrange() | 00:01:00 | ||
| Rows component: rowwise() | 00:02:00 | ||
| Grouping of rows: summarise() | 00:03:00 | ||
| Grouping of rows: across() | 00:02:00 | ||
| COVID-19 Analysis Task | 00:08:00 | ||
| Additional Materials | 00:00:00 | ||
| Assignment | |||
| Assignment – R Programming for Data Science | 2 weeks, 1 day | ||
| Order Your Certificate | |||
| Order Your Certificate QLS | 00:00:00 | ||
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Access01 Year
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LevelLevel 3
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Students3
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Duration2 weeks, 1 day