R Programming I

Track: Enterprise Data Scientist (EDS)
This subject will provide the necessary knowledge that is required to get started with R programming. R programming is one of the most prevalent programming languages/environments in data processing, data modelling and data analytics.

Design and Solve Data-Related Problem

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The objective of this subject is to provide students with a basic introduction to R programming. Topics covered in this subject are data types, control flow, and graphical user interface-driven applications. The examples and problems used in this course are drawn from diverse areas such as text processing, simple graphics creation and image manipulation, and genomics.

Learning outcome:

Upon completion, participants should be able to demonstrate each of the following;
  1. Comprehend the principles, techniques and practices relevant to R programming
  2. Design and solve data-related problems by writing a workable program

Who should attend:

Professionals that work with data

3 days of in depth learning

Face to face with experienced Data Scientist.

Course Methodology

This course will utilize a combination of Presentations and Workshops.

CADS Certification​

Earn certification upon completion.

Introduction to Programming
Minimum Qualification:
Undergraduate Degree

Training Track

Enterprise Data Scientist (EDS)

R Programming I is one of the modules under our Enterprise Data Scientist (EDS) programme. EDS is a 42-day training program that provides participants with the tools to be key leaders and contributors of a data science team and be able to analyze data to drive informed business decisions.

Details of Subject

Day 1 & 2
  1. Introduction –
    • Basic concepts of R programming
  2. Data Types –
    • Variables, Assignments, Vectors, Lists, Matrices, Data Frames, Factors, sub-setting and indexing
  3. Conditions and Loops –
    • If/else, Relational Operators, Nested If/else, Logical operators, for loops, repeat loops, continue & break
  4. Functions –
    • Global & local variables, define functions, repeat functions
  5. Input/Output –
    • Read & Write files, data structures (e.g. csv, json), reading from web
Day 2
  1. Statistics with R –
    • Distribution functions, guessing distributions
Day 3
  1. R Modules –
    • Random module, time module, sys module
  2. Data Visualization in R –
    • Graphing Principles, Visualization packages
  3. Assignment –
    • To define a data science project related to trainees’ occupation to be delivered in the last session

Lead Instructor

Narjes Khatoon Naseri
Narjes is a data scientist and a software engineer with more than 5 years of experience in data analysis and building predictive models. Her expertise covers exploratory data analysis, statistical modeling, machine learning and heuristic search algorithms. Narjes is also a professional trainer and content developer in Data Science domain. She specializes in conducting a complete lifecycle of Natural Processing Language (NLP) on social media and customer service systems for predicting society behavior, feedback classification and summarization. Narjes is also an expert in applying mathematical and artificial intelligence approaches to optimize planning and timetabling systems.

CADS Certification

EDS CADS Certified Enterprise Data Scientist

Certification information for this module & track will be made available soon.

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R Programming I