Data Visualization
Course Description:
As big data advances into every industry and organization, finding ways to effectively share and communicate it with diverse audiences remains challenging. Data visualization provides decision-makers with visual representation of this analytics that makes data easier to understand, parse and take action. This course is ideal for passionate professionals or students looking to build practical skills using visualization tools that bring their data to life.
This course takes a holistic approach to creating visualizations, weaving in theory as well as practical applications using tools on various datasets.
This 8-week journey will provide you with a practitioner-based view of SQL, Python, R, D3.js and Power BI. You will be introduced to principles of visual design, explore industry-relevant tools for visualization, and become more proficient in presenting data to a variety of audiences.
In addition to weekly webinars, you will have the opportunity to apply the methods of data visualization in hands-on assignments, with instructor and peer-to-peer feedback.
Instructor: Carmen Taglienti
Carmen has over twenty-five years’ experience as an Analytics and Data Management expert. He is a recognized leader in the Analytics, Data Visualization, Data Science, Data Governance, and ‘Big Data’ space.
Currently Carmen heads up Global Data Management and Governance for State Street bank as the company moves to a data-centric, governed, ontology approach, embracing data as a key asset with the organization. Prior to State Street, Carmen has held global roles in Analytics, Data Architecture and Informatics. Additionally, he has built and led an Information and Analytics Practice for Slalom in Boston, which is focused on working with clients to enable business-focused, high-impact analysis solutions driven by modern data architecture platforms.
Carmen is an adjunct professor at Merrimack College, teaching Data Management and ‘Data Governance, Law and Ethics’ in their online graduate Data Science program. Additionally, he teaches Data Warehousing, Visualization, Advanced Cloud Computing and presents regularly at industry events.
Syllabus
Week 1: Introduction to Data Visualization
Objectives:
Becoming conversant in the types of visualizations and their applications
Assess visualizations while applying principles of quality design
Focus:
o What is Data Visualization and Why is it Important?
o Why Visualization?
o Types of Visualization and Their Principles
o Common Graph Types and When to Use Them
o Examples of Memorable Visualizations
Recommended Reading:
§ “Show me the numbers: Designing Tables and Graphs to Impress”, Stephen Few. Analytics Press. Chapters 6 & 7
§ “Understanding Charts and Graphs”, Stephen Kosslyn. Applied Cognitive Psychology, Vol. 3, pps 185-226
§ “The Visual Display of Quantitative Information”, Edward R. Tufte. Graphics Press
Week 2: Data Management and Visualization
Objectives:
Define and review available classes of data management services
Practice connecting to a database
Focus:
o What is Data Management?
o Available Classes of Data Services?
o Introduction to MySQL and the SQL Language
o Introduce Additional Visualization Types
o Demonstrate Database Connectivity Using Python & Power BI
Additional Subject Matter References (follow-up from Week 1 Office Hours):
§ “The Data Loom”, Stephen Few, Analytics Press, 2019 https://www.amazon.com/Data-Loom-Understanding-Critically-Scientifically/dp/1938377117
§ “Data Visualization – A Practical Introduction”, Kieran Healy, Princeton University Press, 2019 https://kieranhealy.org/publications/dataviz/
§ “Good Charts”, Scott Berniato, Harvard Business School Publishing, 2016 https://store.hbr.org/product/good-charts-the-hbr-guide-to-making-smarter-more-persuasive-data-visualizations/15005?sku=15005-PBK-ENG
Week 3: Data Management & Visualization and Python
Objectives:
Understanding how data management occurs within Python
Understand and execute code for visualizations using the Jupyter Notebook
Focus:
o Python Overview
o Features of Python
o Data Management with Python
o Popular Python Visualization Packages
Week 4: Data Management & Visualization and R
Objectives:
Understanding how data management occurs within R
Understand and execute code in R using the Jupyter Notebook
Focus:
o Understand how to use R for Data Visualization
o Introduce R at a high level (in comparison to Python) for Data Science and specifically with respect to Data Visualization
o Discuss the data management model used by R and connect it to MySQL
o Introduce the visualization model that is used by R
o Demos of visualizations in R
o Comparison: understand the features of each environment to help select which tools to use for your solution
Week 5: Custom (JavaScript) Visualization
Objectives:
Understand how D3 is used for data visualization
Explore visualization techniques using D3
Understand and execute code in R using the Jupyter Notebook and jsfiddle.net
Focus:
o D3 Overview
o D3 Features
o D3 Advantages
o D3 Language Features and Demonstration
o D3 Comparison
Week 6: Visualization Tools
Objectives:
Discuss applications of Power BI and connect to MySQL
Review the visualization model using Power BI
Generate visualizations with Power BI
Focus:
o Overview of Power BI
o Proprietary/Licensed visualization tool characteristics
o Power BI features and benefits
o How to get started with Power BI
o Use cases for visualization discussion
o Power BI architecture
o How to select the right visualization tool
o Power BI comparison
Recommended Reading:
§ Microsoft Learn: Create and use analytics reports with Power BI https://learn.microsoft.com/en-us/training/paths/create-use-analytics-reports-power-bi/
§ What is Power BI Desktop https://learn.microsoft.com/en-us/power-bi/fundamentals/desktop-what-is-desktop
§ Power BI official documentation https://learn.microsoft.com/en-us/power-bi/
§ Guided Learning – Getting Started https://learn.microsoft.com/en-us/training/powerplatform/power-bi?WT.mc_id=powerbi_landingpage-docs-link
Week 7: Streaming Visualizations
Objectives:
Review concepts of streaming visualizations and background on sources of streaming data
Connect to a streaming dataset and generate visualizations using Power BI
Focus:
o Understand what streaming visualization is and why it is important
o Understand how to use Power BI for real-time streaming visualization
o Introduce concepts of streaming visualization
o Introduce the background of IoT and the origins of this type of data
Recommended Reading:
§ “Visualizing Streaming Data”, Aragues, Anthony, https://www.oreilly.com/library/view/visualizing-streaming-data/9781492031840/ch01.html#idm139635124847184
§ “What is streaming data?” https://aws.amazon.com/streaming-data/
§ “Visualization of Streaming Data: Observing Change and Context in Information Visualization Techniques”, Milos Krstaji – Univeristy of Konstanz https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1017.1435&rep=rep1&type=pdf
§ “Streaming Analytics 101: The what, why and how” https://www.dataversity.net/streaming-analytics-101/
Week 8: Bringing it all together
Objectives:
Identify the key components of data visualization projects
Critique visualizations using the principles of design and their ability to tell a story
Focus:
o Discuss and understand real world data visualization solutions and how visualization is used to convey results and conclusions – “telling a story”
o Present an approach to data visualization projects
o Share examples of compelling visualizations from class & across the Industry
o Discuss some additional popular tools
o Class wrap-up
Recommended Reading:
§ “Storytelling with data”, Cole, Nussbaumer, Knaflic. Chapters 1,2,8-10 (Supplemental reading Chapters 3-7
Reference Tool:
§ “The Data Visualization Handbook”, SAP Analytics Cloud https://info.sapdigital.com/The-Data-Visualization-Handbook_WC.html