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EIEIO / DATA SCIENCE

R for Data Science

Course outline

4 days
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Duration

4 days

Prerequisites

All participants should have a basic knowledge of programming in any language (Java, C, C++, Pascal, Fortran, Javascript, PHP, Python, etc.).

Who Should Attend

This workshop is intended for individuals who are interested in learning data science, or who want to begin their career as a data scientist.

Will a Certificate be Provided?

Students will be given a Certificate of Attendance after successfully completing the course.

Course Overview

R is a programming language that is well-known for its power in statistical computing. The use of R in data science enables insights from data to be extracted, and these insights allow companies to get ahead of their competitors. This course provides an introduction to the fundamentals of R language, with a specific focus on how it can be used in Data Science. You’ll gain knowledge on how to gather data, and what you can do to work with it, starting from reading and writing, to manipulation and visualisation. Databases, the means by which data is stored, and Structured Query Language (SQL), the language that is used to interact with the database, will be covered as well. You’ll also be exposed to a wide range of topics including Big Data and its real-world applications, supervised learning, predictive analytics, exploratory data analysis, basic statistics, logistic regression, and data mining.

Learning Outcomes

Upon completion of the Beginner to Intermediate course, you will be able to:

  • Understand R language fundamentals, including basic syntax, variables, and types.
  • Create functions and use control flow.
  • Read and write data in R.
  • Work with data in R.
  • Create and customise visualisations using ggplot2.
  • Perform predictive analytics using R.

Upon completion of the Advanced course, you will be able to:

  • Get exposure on the applications of R.
  • Perform data manipulation using R.
  • Use various techniques to import data.
  • Understand exploratory data analysis.
  • Learn the basics of statistics and logistic regression.
  • Understand clustering techniques, regression, and classification in data mining.

Course Outline

Introduction to Data Science

  • What is Data?
  • Types of Data?
  • What is Data Science?
  • Statistical Thinking
  • Knowledge Check
  • Lab Activity

Data Processes

  • Extract, Transform and Load (ETL)
  • Data Cleansing
  • Aggregation, Filtering, Sorting, Joining
  • Data Workflow
  • Knowledge Check
  • Lab Activity

Data Quality

  • Raw vs Tidy Data
  • Key Features of Data Quality
  • Maintenance of Data Quality
  • Data Profiling
  • Data Completeness and Consistency

Life of a Data Scientist

  • Identify Problem
  • Define Question
  • Define Ideal Dataset
  • Obtain Data
  • Analyse Data
  • Interpret Results
  • Distribute Results
  • Knowledge Check

Beginning Database

  • Types of Databases
  • Relational Databases
  • NoSQL
  • Hybrid Database
  • Knowledge Check
  • Lab Activity

Structured Query Language (SQL)

  • Performing CRUD (Create, Retrieve, Update, Delete)
  • Designing a Real World Database
  • Normalizing a Table
  • Knowledge Check
  • Lab Activity

Lab: Data Cleansing Exercise

Data Gathering

  • Obtain Data from Online Repositories
  • Import Data from Local File Formats (json, xml)
  • Import Data using Web API
  • Scrape Website for Data
  • Knowledge Check
  • Lab Activity

Instructor-led Case Study Exploratory

Data Analysis

  • What is EDA?
  • Goals of EDA
  • The Role of Graphics
  • Handling Outliers
  • Dimension Reduction

Introduction to R

  • Features of R
  • Vectors
  • Matrices and Arrays
  • Data Frame
  • Input/Output

Lab: Exploring Data using R

Introduction to Predictive Analytics

  • Linear Models and Regression
  • Multivariate Regression
  • Logistic Regression
  • Knowledge Check
  • Lab Activity

Supervised Learning

  • What is Prediction?
  • Sampling, Training Set, Testing Set
  • Constructing a Decision Tree
  • Knowledge Check
  • Lab Activity

Presenting Data

  • Choosing the Right Visualisation
  • Plotting and Charting in R
  • Knowledge Check
  • Lab Activity

Data Analysis Presentation

  • Using Markdown Language
  • Convert your Data into Slides
  • Data Presentation Techniques
  • The Pitfall of Data Analysis
  • Knowledge Check
  • Lab Activity
  • Group Presentation

Lab: Mini Project

Big Data Landscape

  • What is Small Data?
  • What is Big Data?
  • Big Data Analytics vs Data Science
  • Key Elements in Big Data (3Vs)
  • Extracting Values from Big data
  • Challenges in Big Data

Big Data Tools and Applications

  • Introducing Hadoop Ecosystem
  • Cloudera vs Hortonworks
  • Real World Big Data Applications
  • R/Hadoop
  • Knowledge Check
  • Group Discussion

Programme scope, delivery arrangements and suitability are confirmed during enquiry.