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Introduction to R for Data Science Online Course

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106 STUDENTS
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Overview:

If you are interested in data science, here’s an opportunity to gain an understanding of R from scratch. The Introduction to R course is a step by step approach for beginners to learn R. The course will teach you about installation, how to use R help tools and R exercise database, basic R functions like first coding steps and a look at graphical tools.

The Introduction to R course will cover topics starting with data and object types, importing into R, operations, loops and conditions, the use of R in statistics, and graphics.

Introduction to R will help learners use R to evaluate descriptive statistics, linear modelling, hypothesis testing and probability distributions. The course is internationally recognised and accredited to a training organisation and you will be issued an internationally recognised qualification following full completion of Introduction to R course.

Why consider 1Training?

As improvements and advancements are made in technology, online courses are no longer just conventional means of studying at affordable costs. In many aspects online training offers superiority to traditional learning. There is an effectiveness and convenience that traditional learning cannot provide. The overall convenience and flexibility makes it a superior learning method.

1Training offers the most convenient path to gain an internationally recognised qualification that will give you the opportunity to put into practice your skill and expertise in an enterprise or corporate environment. You can study at your own pace at 1Training and you will be provided with all the necessary material, tutorials, qualified course instructor and multiple free resources which include Free CV writing pack, Nus Discounted Card, Free career support and course demo to make your learning experience enriching and more rewarding.

Learning Outcomes

  • Gain an understanding of the fundamentals of R.
  • Become functional with R.
  • Learn how to download and install R.
  • Understand the use of R in analytics.
  • Learn how to use R in data mining and analytical operations.

Course Titles

  • Module 01: Introduction to R
  • Module 02: Variables
  • Module 03: Data Structures and Operators
  • Module 04: Data Frames and Tables

Access Duration

The course will be directly delivered to you, and you have 12 months access to the online learning platform from the date you joined the course.  The course is self-paced and you can complete it in stages, revisiting the lectures at anytime.

Who is this Course aimed at?

  • The course is aimed at enterprise data analysts.
  • Students with interest in data mining.
  • The course is designed for individuals interested in statistics and data visualisation.
  • Anyone interested in data science.
  • Web developers.
  • Professionals working in data analytics.

Entry Requirements

  • You must be over the age of 16 and have a basic understanding of Maths, English and ICT
  • Course is for beginners with basic proficiency in statistics and probability distributions

Method of Assessment

At the end of the Introduction to R course you will be required to take a multiple choice question assessment test. The multiple choice question assessment will be automatically marked with learners receiving an instant grade.

Certification

Those who successfully complete the exam will be awarded the certificate in Introduction to R.

Awarding Body

The certificate will be awarded by CPD and iAP. This internationally recognised qualification will make your CV standout and encourage employers to see your motivation at expanding your skills and knowledge in the IT enterprise.

Progression and Career Path

Once you successfully complete Introduction to R you will be qualified to work in the following positions. The Introduction to R qualification will also put you in line to demand a higher salary or job promotion. The average UK salary per annum according to https://www.payscale.com is given below.

  • Software Developer – £30,651 per annum
  • Programmer – £44,873 per annum
  • Computer Programmer – £30,400 per annum
  • Data Analysts – £25,511 per annum

Other Benefits

  • Written and designed by the industry’s finest expert instructors with over 15 years of experience
  • Repeat and rewind all your lectures and enjoy a personalised learning experience
  • Gain access to quality video tutorials
  • Unlimited 12 months access from anywhere, anytime
  • Excellent Tutor Support Service (Monday to Friday)
  • Save time and money on travel
  • Learn at your convenience and leisure
  • Quizzes, tests mock exams, practice exams to ensure you are 100% ready
  • Eligible for a NUS discount card
  • Free Career Support Service
  • 25% Discount on personal Statement and covering letter writing service
  • Free Access to Over 150 courses for 2 days (48 hours)
  • Free access to course before you purchase (For selected courses only)

Key Features

Gain an accredited UK qualification

Access to excellent quality study materials

Learners will be eligible for TOTUM Discount Card

Personalized learning experience

One year’s access to the course

Support by phone, live chat, and email

Course Curriculum Total Units : 38
➤ Module 01 - Introduction To R
1.0 Topic A: About R - Part 1
1.1 About R – Part 2
1.2 About R – Part 3
2.0 Topic B: Rstudio - Part 1
2.1 RStudio – Part 2
2.2 RStudio – Part 3
3.0 Topic C: Workspaces - Part 1
3.1 Workspaces – Part 2
3.2 Workspaces – Part 3
4.0 Topic D: Basic Types - Part 1
4.1 Basic Types – Part 2
4.2 Basic Types – Part 3
➤ Module 02 – Variables
1.0 Topic A: Basic Types Demo - Part 1
1.1 Basic Types Demo – Part 2
1.2 Basic Types Demo – Part 3
2.0 Topic B: Dates Demo - Part 1
2.1 Dates Demo – Part 2
2.2 Dates Demo – Part 3
3.0 Topic C: Variables - Part 1
3.1 Variables – Part 2
3.2 Variables – Part 3
4.0 Topic D: Missing Values - Part 1
4.1 Missing Values – Part 2
4.2 Missing Values – Part 3
➤ Module 03 - Data Structures And Operators
1.0 Topic A: Vectors - Part 1
1.1 Vectors – Part 2
1.2 Vectors – Part 3
2.0 Topic B: Matrices - Part 1
2.1 Matrices – Part 2
2.2 Matrices – Part 3
3.0 Topic C: Arrays - Part 1
3.1 Arrays – Part 2
3.2 Arrays – Part 3
4.0 Topic D: Lists And Factors - Part 1
4.1 Lists and Factors – Part 2
4.2 Lists and Factors – Part 3
5.0 Topic E: Arithmetic And Relational Operators - Part 1
5.1 Arithmetic and Relational Operators – Part 2
5.2 Arithmetic and Relational Operators – Part 3
6.0 Topic F: Logical And Assignment Operators - Part 1
6.1 Logical and Assignment Operators – Part 2
6.2 Logical and Assignment Operators – Part 3
➤ Module 04 - Data Frames And Tables
1.0 Topic A: Data Frames - Part 1
1.1 Data Frames – Part 2
1.2 Data Frames – Part 3
2.0 Topic B: Working With Data Frames - Part 1
2.1 Working with Data Frames – Part 2
2.2 Working with Data Frames – Part 3
3.0 Topic C: Data Tables - Part 1
3.1 Data Tables – Part 2
3.2 Data Tables – Part 3
4.0 Topic D: Working With Data Tables - Part 1
4.1 Working with Data Tables – Part 2
4.2 Working with Data Tables – Part 3
5.0 Topic E: Shortcuts - Part 1
5.1 Shortcuts – Part 2
5.2 Shortcuts – Part 3
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