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Data Scientist Upskilling

Course Code



2 Days

It is beneficial to have familiarity with Statistics, general Big Data platform knowledge as well as trends in types of Data collected.
This course provides Data Analysts an outline to upskill into a Data Scientist. Participants will learn the additional skills required to make the transition, and the tools and platforms that are widely used for implementation. There will also be an introduction and hands-on training using R.
This course is designed for individuals who are data analysts and have programming experience.

In this course, participants will:

  • Understand the role of a ‘Citizen Data Scientist’
  • Get an overview of the rapidly changing Data Landscape
  • Describe the skills to transition from Data Analyst to a Data Scientist
  • How to use a tool/platform such as R/RStudio
  • Traverse the journey from Descriptive to Diagnostic to Predictive Analytics
  • Learn how to work with Data
  • Learning from Data – Machine Learning
  • Supervised/Unsupervised Learning
  • Black Box methods like Neural Networks and Deep Learning

Module 1: Introduction
Understand the role of a “Citizen Data Analyst Scientist”
Comprehend an overview of the landscape change due to technology evolution
Understand data generation in the information age
Comprehend data consumption by individual
Understand the web, social media, mobile devices, and data landscape
Know which tools are needed to process data at volume and speed
Understand the skills needed to transform a Data Analyst to process the information presented in this module
Comprehend the skills needed for a Data Scientist such as:

  • Mathematics/Statistics
  • Computer Programming
  • Domain Knowledge

Module 2: R in a Nutshell
Understand an introduction to R and Python
Set-up and interact with R
Comprehend Data Structures in R
Understand introductory information about RStudio
Interact with Data on RStudio
Become familiar with an overview of R on Cloud Platform

Module 3: Detailed Topics
Understand an overview of the skills required
Comprehend the path from data and tell the story
Perform exploratory analysis
Understand descriptive analytics
Comprehend how to communicate data

Module 4: Working with Data
Know how to handle missing data
Communicate data
Perform advanced plotting

Module 5: Learning from Data
Understand machine learning
Comprehend regression in supervised learning
Comprehend classification in supervised learning

Module 6: Learning from Data II
Understand unsupervised learning
Comprehend black-box methods – neural networks
Understand deep learning as an extension of neural networks

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