R Programming II

Track: Enterprise Data Analyst (EDA)
This subject will provide the necessary knowledge for advanced analytics with R programming. R programming is one of the most used programming languages/environments used in data processing, data modelling and data analytics.

Design and Solve a Data Related Problem

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Topics covered in this subject are data cleansing, connecting and work with databases and interactive 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 as well as 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.

Pre-requisite:
R Programming I, Database Management Systems
Minimum Qualification:
Undergraduate Degree

Training Track

Enterprise Data Analyst (EDA)

R Programming II is one of the modules under our Enterprise Data Analyst (EDA) programme. EDA is a 28-day training program that provides analysts with the tools to be immediate contributors to a data science team. They will assist to frame business requirements as analytics models.

Details of Subject

Day 1 & 2
  1. Data Cleansing –
    • Basic concepts data cleansing, outliers, handling missing values and imputation
  2. Regression, Associative Rules, Decision Tree, Clustering –
    • Building regression models and associative rules and running tests
  3. SQL fundamentals –
    • Connecting to relational databases and running sql statements and queries via suitable packages
Day 2 & 3
  1. Interactive Visualization in R –
    • Using Shiny to build interactive visualizations
  2. 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

EDA CADS Certified Enterprise Data Analyst

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

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R Programming II