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The Data Visualization Lifecycle (Video Training)

The Data Visualization Lifecycle (Video Training)

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  • Copyright 2023
  • Edition: 1st
  • Online Video
  • ISBN-10: 0-13-806845-3
  • ISBN-13: 978-0-13-806845-5

4+ Hours of Video Instruction

The Perfect Course for Learning the Data Visualization Life Cycle

The Data Visualization Life Cycle not only teaches you fundamentally useful techniques for making your data visualizations better, but it will also open your eyes to the different data cultures that exist in our modern, data-driven world. You learn how to make better data visualization for your stakeholders, work more effectively with your peers, and enhance your ability to explore your own data.

Skill Level:

  • Beginner
  • Intermediate

Learn How To:

  • Understand your audience and the approach you should take to your data visualization
  • Optimize data visualizations for different data cultures and points in the life cycle
  • Collaborate with others to produce data visualizations
  • Manage pain points when working with different data professionals
  • Improve data explanation
  • Design for different kinds of data products and delivery methods
  • Build a data visualization culture and increase data literacy
  • Investigate automated and machine-based analysis
  • View data visualizations as business assets

Who Should Take This Course:

  • Audience: This video series is designed to provide key context and skills for data visualization creators whether they are analysts, data scientists, data engineers or even downstream data consumers and leadership. You'll find the most value in the course if you've learned some fundamentals of data visualization and need more practical advice for how to work with peers and stakeholders to create effective data communication.

Course Requirements:

  • Working understanding of basic data visualization (knowing when and being able to create bar charts, line charts and scatterplots)
  • Experience building data products like dashboards, reports or presentations using data visualization
  • Familiarity with the data lifecycle

Topics Include:

Lesson 1. Understanding Stakeholders and Approaches to Data Visualization

Data visualization is often taught as if there was only one kind of approach to answer one kind of question for one kind of audience. But data visualization out in the real world is used in many different ways with different goals and different ways of measuring impact. In this section, I introduce the data visualization life cycle, which will provide you with a broader understanding of different approaches to data visualization.

Lesson 2. Optimizing for Different Data Cultures

It's not enough to understand that there are different ways of doing data visualization from a theoretical standpoint. This section focuses on specific and practical aspects of how you might visualize the same information differently depending on where you and your audience are in the data visualization life cycle. We focus in on how to visualize data for data wrangling, data engineering, and data science.

Lesson 3. Collaboration Foundations

Collaboration isn't well-described in a data visualization setting, even though it happens all the time. In this section, we explore how different roles collaborate with each other as well as how principles of collaboration help you to design charts even if the audience is only yourself. To understand collaboration, we look at it from several different angles including collaboration between data roles to make a product, collaboration with stakeholders, and how to think about collaborating with yourself.

Lesson 4. Collaboration in Mature Data Organizations

Once you've mastered the general principles of collaboration you need to understand how we collaborate in more mature data settings. This section highlights typical modes of collaboration in organizations with different data roles by looking at each data roleanalyst, data engineer, and data scientistto understand typical areas of friction between the roles to develop a data visualization product.

Lesson 5. Improving Data Explanation

When your data visualization is used for explanatory purposes you need to leverage all the features available to you to make the most actionable charts. This section provides you with an overview and specific examples of how to make your data visualization more informative and accessible but also more likely to be used and acted upon.

Lesson 6. Productization Productionalization Productischemizationismeizen

Sometimes we view charts in a dashboard, sometimes in an email report, and sometimes using interactive scrollytelling. This section details the affordances and tradeoffs of each of these modes with tips and techniques to ensure that you're using the right mode and taking advantage of its strengths.

Lesson 7. Closing the Loop

We often think that charts only exist for a single decision, but they live on and affect more than just the moment they were made for. In this section, we explore how charts live on in an organization and what it means for you, your team, and your organization. Understanding this not only lets you make more effective charts but also makes for a more effective organization by growing data literacy and developing long-term processes that lead to better data visualization and better results from that data visualization.

About Pearson Video Training:

Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Sams, and Que Topics include: IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more.  Learn more about Pearson Video training athttp://www.informit.com/video.

Sample Content

Table of Contents


Lesson 1: Understanding Stakeholders and Approaches to Data Visualization

1.1 The life cycle relies on it depends

1.2 How your role aligns with the life cycle

1.3 Exploring versus explaining in the life cycle

Lesson 2: Optimizing for Different Data Cultures

2.1 Where are you in the life cycle?

2.2 Visualizing the data by cleaning it (data wrangler culture)

2.3 Visualizing the data by understanding its health and performance (data engineer culture)

2.4 Visualizing data with hypothesis generation and exploration (data scientist culture)

Lesson 3: Collaboration Foundations

3.1 Typical pain points in presenting to data consumers

3.2 General principles of collaboration with data visualization

Lesson 4: Collaboration in Mature Data Organizations

4.1 Typical pain points of engineer to analyst/data scientist handoffs

4.2 Typical pain points of scientist to analyst handoffs

4.3 How to dumb it down (you're not dumbing it down)

Lesson 5: Improving Data Explanation

5.1 Storytelling

5.2 Growing your audience

5.3 Interactivity

5.4 Building and spending trust with your stakeholders

5.5 Driving engagement through annotation and novel chart features

Lesson 6:   Productization Productionalization Productischemizationismeizen

6.1 Chart design for self-service versus curated data products

6.2 Optimization for using different delivery methods (dashboards, reports, memos, scrollytelling)

6.3 Data visualization as metric design (data visualization as part of the virtuous cycle of data)

Lesson 7: Closing the Loop

7.1 Building your data visualization culture

7.2 Increasing data (visualization) literacy for your organization

7.3 Explanatory techniques useful for automatic exploratory analysis (anomaly detection, intelligence)

7.4 Human in the loop ML

7.5 Data visualization as business assets



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