R Programming for Data Science: Introduction



Overview

R is a popular and free programming language. It is easy to install and works on common platforms such as Windows, Linux and Mac. Even if you have limited or no computer programming experience, R is easy to learn.

Its applications span various areas such as statistical significance testing, data analysis and visualisations, data processing, manipulation and summarisation. An example is the fact that it is fairly easy to perform MS Excel, or SQL-like operations with R. In addition, R is a good choice for machine/deep learning, image analysis and processing and much more.

During the course you will learn the basics of the popular R programming language and how to use it to manipulate data and perform excel-like operations. 

This course begins with the very basics of R and its syntax and control statements, and gradually builds up to cover lots of useful functionalities and data manipulation. For example, the course covers control statements and instructions related to decision making and iterations as well as various types of data structures and functions. After this, you will learn how to use R to apply several common data processing and manipulation operations.

The course is designed in such a way that people with minimal or no computer programming experience can use it as a foundation to learn more advanced R topics or transfer their skills to other programming languages.

Programme details

First live webinar: 25 April 2025, 5.00-6.00pm (UK time)

Week 1: R Introduction (includes R installation): R Reserved Words, Variables & Constants, R Operators and Operator Precedence.

Week 2: Decision and Loop: if…else, for loop, while loop, break & next

Week 3: Functions: What are they? How to write your own Function, Function Return Value, Environment & Scope.

Week 4: Data Structures - Part 1: Vectors, Matrices and Lists.

Week 5: Data Structures - Part 2: Data Frames and Factors. Slicing, Selection and Filtering.

Week 6: Basic Graphs & Charts: Bar Plot, Histogram, Pie Chart, Box Plot (includes when to use them and how to interpret them).

Week 7: File Reading and Writing: How to read from and save to text files, CSV files and Excel sheets.

Week 8: Data Manipulation - Part 1: Dealing with Missing and Duplicate Values, Sorting and Data Type Conversion.

Week 9: Data Manipulation - Part 2: Merging and Joining Data Frames

Week 10: Data Manipulation - Part 3: GroupBy and Pivot Tables. Working with Date/Time Data.

Certification

Credit Application Transfer Scheme (CATS) points 

To earn credit (CATS points) for this course you will need to register for credit and pay an additional £30 fee. You can do this by ticking the relevant box at the bottom of the enrolment form or when enrolling online. If you do not register for credit when you enrol you have up until the course closes to enrolments to pay the £30 fee. Students who do not register for CATS points prior to the start of the course can apply retrospectively from the January 1st after the current full academic year has been completed.

See more information on CATS point

Coursework is an integral part of all online courses and everyone enrolled will be expected to do coursework, but only those who have registered for credit will be awarded CATS points for completing work at the required standard. If you are enrolled on the Certificate of Higher Education, you need to indicate this on the enrolment form but there is no additional registration fee. Students who register for CATS points will be posted a Record of CATS points on successful completion of their course assessment.

Digital credentials

All students who pass their final assignment, whether registered for credit or not, will be eligible for a digital Certificate of Completion. Upon successful completion, you will receive a link to download a University of Oxford digital certificate. Information on how to access this digital certificate will be emailed to you after the end of the course. The certificate will show your name, the course title and the dates of the course you attended. You will be able to download your certificate or share it on social media if you choose to do so. 

Please note that summative assignments are not graded but are marked either pass or fail. 

Fees

Description Costs
Course Fee £310.00
Take this course for CATS points £30.00

Funding

If you are in receipt of a UK state benefit, you are a full-time student in the UK or a student on a low income, you may be eligible for a reduction of 50% of tuition fees. Please see the below link for full details:

Concessionary fees for short courses

Tutor

Dr Noureddin Sadawi

Dr. Noureddin Sadawi is a consultant in machine/deep learning and data science. He has several years’ experience in various areas involving data manipulation and analysis. He received his PhD from the University of Birmingham, United Kingdom. He is the winner of two international scientific software development contests - at TREC2011 and CLEF2012.

Noureddin is an avid scientific software researcher and developer with a passion for learning and teaching new technologies. A list of his publications can be found here: https://scholar.google.com/citations?user=KA4HdnkAAAAJ&hl=en. He is an experienced scientific software developer and data analyst; over the last few years he has been using R and Python as his preferred programming languages. Also, he has been involved in several projects spanning a variety of fields such as bioinformatics, textual/image/video data analysis, drug discovery, omics data analysis and computer network security. He has taught at multiple universities in the UK and has worked as a software engineer in different roles.

Course aims

An introductory overview of the R programming language is covered in this course. Students will learn the basics of R and move on gradually to data processing and performing excel-like operations on tabular data.

Course objectives:

  • To introduce programming with R.
  • To introduce how to read data from files.
  • To introduce how to perform excel-like operations with R.

Teaching methods

Learning takes place on a weekly schedule. At the start of each weekly unit, students are provided with learning materials on our online platform, including one hour of pre-recorded video, often supplemented by guided readings and educational resources. These learning materials prepare students for a one-hour live webinar with an expert tutor at the end of each weekly unit which they attend in small groups. Webinars are held on Microsoft Teams, and provide the opportunity for students to respond to discussion prompts and ask questions. The blend of weekly learning materials that can be worked through flexibly, together with a live meeting with a tutor and their peers, maximise learning and engagement through interaction in a friendly, supportive environment.

Learning outcomes

By the end of the course students will be expected to:

  • understand R syntax, basic control flow and program design;
  • understand R data structures and how/when to use them;
  • understand how to perform Excel-like operations with R.

After attending this course, students will know:

  • foundational programming concepts such as variables, iterative statements, conditionals, functions and data structures;
  • how to read and write files, how to generate basic plots and visualisations and how/when to use data structures;
  • how to perform several excel-like operations such as sorting and filtering data, joining tables and much more.

Assessment methods

The assessment will be a set of ten questions that will enable students to demonstrate an understanding of the material discussed during each week of the course.

Please note that summative assignments are not graded but are marked either pass or fail. 

Coursework is an integral part of all weekly classes and everyone enrolled will be expected to do coursework in order to benefit fully from the course. Only those who have registered for credit will be awarded CATS points for completing work the required standard.

Students must submit a completed Declaration of Authorship form at the end of term when submitting your final piece of work. CATS points cannot be awarded without the aforementioned form - Declaration of Authorship form

Application

Please use the 'Book now' or 'Apply' button on this page. Alternatively, please complete an Enrolment Form (Word) or Enrolment Form (Pdf)

We will close to enrolments 7 days prior to the first live webinar to allow us to complete the course set up. We will email your joining instructions at that time (7 days before you first live webinar) so you can access the Canvas virtual learning environment (VLE) and watch your first pre-recorded video. Please check spam and junk folders during this period to ensure that these emails are received. 

 

Level and demands

The Department's short online courses are taught at FHEQ Level 4, i.e. first year undergraduate level, and you will be expected to engage in private study in preparation for the classes. This may take the form, for instance, of reading and analysing set texts, responding to questions or tasks, or preparing work to present in class. FHEQ level 4 courses require approximately 10 hours study per week, therefore a total of about 100 study hours.

Terms & conditions for applicants and students

Information on financial support