Data Visualization: Tools and Techniques

Data Visualization: Tools and Techniques

Date/Time: Date(s) - 15/06/2022 - 17/06/2022( 8:00 am - 5:00 pm )
Location: Singapore


Overview

As big data advances into every industry and organization, finding ways to effectively share and communicate data with diverse audiences remains challenging. Data visualization provides decision makers with a visual representation of their analytics that makes data easier to understand, parse, and act upon. Taught via live online sessions, this course is ideal for professionals or students who want to learn how to characterize the data required to create effective visualizations—and build practical skills using visualization tools that bring their data to life.

Who Should Attend?

The Data Visualization course is ideal for professionals or students seeking to gain an understanding of data visualization, especially in the context of presenting data to make a compelling proposition. The course also teaches hands-on skills using Python, R, Power BI and D3.js.

Outlines

The course is comprised of eight modules that help participants learn to generate visualizations to better understand real-world problems, discover insights, argue persuasively, and tell compelling stories.

Module 1: Introduction to Visualization

  • Defining data visualization and why it’s important
  • The value and goals of visualization
  • Types of visualizations
  • Pragmatic and artistic visualization
  • Visual principles

Module 2: Data Management and Visualization

  • What is data management? Why is it important to visualization?
  • Classes of data services in the industry
  • Introduction to MySQL and SQL
  • Demonstration of connectivity from Python and Power BI

Module 3: Data Management and Visualization With Python

  • How to use Python for data visualization
  • Introduction to and comparison of Python for data visualization
  • Using Python libraries and techniques
  • Data management in Python and connecting to MySQL
  • Demos of visualizations in Python

Module 4: Data Management and Visualization With R

  • Using R for data visualization
  • Introduction to R for data visualization
  • Using R libraries and techniques
  • Data frames in R and connecting to MySQL
  • Demos of visualizations in R
  • Practical selection of which environment to use

Module 5: Custom (JavaScript) Visualizations

  • Overview and reasons to do custom visualizations
  • What it takes to create an application in these environments
  • Introduction to D3.js
  • Examples of D3.js

Module 6: Visualization Tools

  • Power BI
  • Power BI examples
  • Out-of-the-box software tools for data visualization

Module 7: Streaming Visualizations

  • Real-time data visualizations connected to devices/IoT
  • Introduction to streaming data
  • Using PowerBI to connect to streaming data
  • Solving problems with visualizations tools: IoT applications

Module 8: Bringing It All Together

  • Understanding the business problem
  • Gathering the data
  • Processing the data
  • Visualizing the data
  • Presenting the visuals
  • Additional solutions using favorite tools chosen by participants

Practical

Introduction to Data Visualization

  • Participants find visualizations and critique based on sound design principles

Introduction to Data Management

  • Insights to data storage and data querying
  • Use data management principles to access data in MySQL
  • Leverage your knowledge to understand the data model of the data you’ll be accessing for visualization and write a query in SQL to extract data in preparation for visualization

Data Visualization Using Python

  • Use Python to prepare and shape data using Pandas
  • Connect to popular relational databases (MySQL) with Python
  • Use that data to create visualizations
  • Pull data from the web into Python for visualization

Data Visualization Using R

  • Use R to prepare and shape data
  • Connect to popular relational databases (MySQL) with R
  • Use that data to create visualizations
  • Pull data from the web into R for visualization

Data Visualization Using D3.js

  • Use D3 to prepare and shape data (a bit more limited than in Python/R)
  • Use data from popular relational databases in D3
  • Build a DOM from D3/SQL data and graph it using the platform

Training Methodology

  • Case Studies.
  • Individual and group discussions and exercises.
  • Intensive training by using templates, diagrams, and charts.
  • Planning activities and presentations.
  • Self-assessments.
  • Action plan.

Request Booking

Bookings are closed for this course.

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