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.

Duration: 3 days
Fee: Request
Start Date: Request

Design and Solve Data-Related Problem

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;

Comprehend the principles, techniques and practices relevant to R programming
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.

Pre-requisite:
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

Introduction –
- Basic concepts of R programming
Data Types –
- Variables, Assignments, Vectors, Lists, Matrices, Data Frames, Factors, sub-setting and indexing
Conditions and Loops –
- If/else, Relational Operators, Nested If/else, Logical operators, for loops, repeat loops, continue & break
Functions –
- Global & local variables, define functions, repeat functions
Input/Output –
- Read & Write files, data structures (e.g. csv, json), reading from web

Day 2

Statistics with R –
- Distribution functions, guessing distributions

Day 3

R Modules –
- Random module, time module, sys module
Data Visualization in R –
- Graphing Principles, Visualization packages
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.

Hear from Our Alumni

The training has benefited me a lot with the skills required to be a data analyst and has upskilled me in data analysis. What I like about the program is where real life problems in the office is used as the materials during the trainings which provides more understanding of the topics. Eventually, it has broaden up my market in the career development.

The program started by setting an expectation on Day 1, that it is not a Read-And-Pass syllabus. The beauty of this program is that it requires the trainees to get their hands dirty and do the actual work throughout the course. It is indeed very different from a lot of programs in the market which just produces a certificate in the end without the trainee actually able to do some real stuff. The course is definitely benefiting anyone who really want to do Machine Learning and Data Science, by creating talents who are able to connect human thought with the machine algorithm, being the bridge within the corporate, into the digital worlds.

The training was beneficial as it trains myself to think from another perspective - one that may not be new, but wasn't practiced as often as I'd like. It was fun and interactive thanks to the facilitator, Jay, too. He was very helpful to answer any questions the attendees have. Believe that this training gives the attendees an extra edge by giving them the knowledge and perspective on how to better utilize data to what they want/need to do - for the organization, the business, and even for personal use, of which I believe will bring myself further in my career.

The training has been an eye opener for me. As someone who is not of programming background, it introduces me to the possibilities of what data can do. It has also helped me bridge the difference in thinking between the Business Team and the Data Science Team. Had a great time learning!

This training enhance on my skill of analyzing of using another tools of programming instead of excel and how to model and visualized our data in a better way instead of our traditional way. Especially when involve with a large scale of data. If using excel, there will a lot of time consuming there instead of R. For me, in market nowadays of big data, this course will brings more value and create another point of view on how to visualize our data especially in making any decision. And of course will enhance our career development.

Register Interest

R Programming I

R Programming I

Design and Solve Data-Related Problem

Learning outcome: