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R Programming for Data Science
By Janets
(160)- 30 Day Money Back Guarantee
- Completion Certificate
- 24/7 Technical Support
Highlights
On-Demand course
6 hours 32 minutes
All levels
Description
Register on the R Programming for Data Science today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career.
The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials.
Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get a digital certificate as a proof of your course completion.
The R Programming for Data Science is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablet, and smartphones.
The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly!
What You Get With The R Programming for Data Science
- Receive a e-certificate upon successful completion of the course
- Get taught by experienced, professional instructors
- Study at a time and pace that suits your learning style
- Get instant feedback on assessments
- 24/7 help and advice via email or live chat
- Get full tutor support on weekdays (Monday to Friday)
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
Certification
After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for £9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for £15.99, which will reach your doorsteps by post.
Who Is This Course For:
The course is ideal for those who already work in this sector or are an aspiring professional. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge.
Requirements:
The online training is open to all students and has no formal entry requirements. To study the R Programming for Data Science, all your need is a passion for learning, a good understanding of English, numeracy, and IT skills. You must also be over the age of 16.
Course Content
| 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 | ||
| IFELSE | 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 | ||
About The Provider
Janets
London
(160)Janets is an online platform where learners come to learn, and evolve. From the very beginning, the aim of this platform was to create an ever-growing community of avid learners instead of just delivering formulaic education. Emphasising on making the learners equipped for the fu...
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