Data Visualization with Python from Coursera

In today's data-driven world, the ability to create compelling visualizations and tell impactful stories with data is a crucial skill. This comprehensive course will guide you through the process of visualization using coding tools with Python, spreadsheets, and BI (Business Intelligence) tooling. Whether you are a data analyst, a business professional, or an aspiring data storyteller, this course will provide you with the knowledge and best practices to excel in the art of visual storytelling.

Throughout the course, a consistent dataset will be used for exercises, enabling you to focus on mastering the visualization tools rather than getting caught up in the intricacies of the data. The emphasis is on practical application, allowing you to learn and practice the tools in a real-world context. To fully leverage the Python sections of this course, prior experience programming in Python is recommended. Additionally, a solid understanding of high-school level math is expected. Familiarity with the Pandas library will also be beneficial.

By the end of this course, you will possess the necessary skills to become a proficient data storyteller and visual communicator. With the ability to create compelling visualizations and leverage the appropriate tools, you will be well-equipped to navigate the world of data and make informed decisions that drive meaningful impact.

What's inside

Syllabus

Data Visualization Fundamentals

This week, we will introduce you to the fundamentals of data visualization and provide step-by-step guidance on how to get started with creating basic plots in Excel and Google Sheets. Whether you are new to data visualization or looking to enhance your skills, this week will lay the groundwork for effective visual storytelling using these widely accessible tools.

Pandas, Seaborn and Matplotlib

This week, you will embark on an exciting journey of learning as you explore data visualization using Pandas, Seaborn and Matplotlib. You will use Pandas and Seaborn to construct histograms, which will allow you to gain insights into the distribution of your numerical data. You will also delve into scatterplots, enabling you to visualize the relationships between different variables in your datasets and identify patterns and correlations. Lastly, you will learn how to utilize line plots to capture temporal trends and changes over time, enhancing your ability to communicate data-driven narratives effectively.

Plotly, Dash and Streamlit

This week, you will explore the powerful combination of Plotly, Dash, and Streamlit for creating interactive and dynamic visualizations within a dashboard. You will use Plotly to create visually appealing histograms, scatterplots, and line plots that can be embedded within a dashboard. You will also dive into Dash and Streamlit, two popular Python frameworks for building interactive web-based dashboards. By the end of the week, you will be able to apply your knowledge and create a comprehensive dashboard that incorporates these visualizations, enabling users to explore and interact with your data.

Visualization with Cloud-Based Tools: Tableau and Amazon Quicksight

This week, you will dive into two cloud-based tools, Tableau and Amazon QuickSight, to create compelling visualizations, and gain insights from your data in a user-friendly and interactive manner. You will first use Tableau to design and create histograms, scatterplots, and line plots that effectively showcase your data. You will then use Amazon QuickSight, an intuitive and cloud-based business intelligence tool, to create visually appealing and interactive visualizations. By the end of the week, you will be able to apply your knowledge and create captivating visualizations using these platforms.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops skills in creating compelling visualizations, which are in demand in data-driven industries

Covers best practices for visual storytelling, which are essential for effective data communication

Taught by instructors with expertise in data visualization, providing learners access to industry knowledge

Emphasizes practical application, allowing learners to apply their skills in real-world scenarios

Uses a consistent dataset throughout the course, enabling learners to focus on visualization techniques

Assumes a solid understanding of high-school level math, which may be a barrier for some learners

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Visualization with Python with these activities:

Review Python Basics

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Strengthen your understanding of Python's core concepts for effective data visualization.

Browse courses on Python

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Review online resources or tutorials on Python basics
Practice writing simple Python programs
Complete coding exercises to test your comprehension

Find a Data Visualization Mentor

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Accelerate your learning progress by seeking guidance from an experienced mentor.

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Identify individuals in your network or reach out to experts in the field
Ask for guidance and support on your data visualization journey
Schedule regular meetings to discuss progress and get feedback

Attend Data Visualization Meetup

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Connect with professionals in the field of data visualization to expand your knowledge and network.

Browse courses on Data Visualization

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Find and attend a local data visualization meetup
Participate in discussions and ask questions
Connect with speakers and other attendees

Six other activities

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Read Effective Data Visualization by Stephanie Evergreen

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Gain valuable insights and best practices for creating compelling data visualizations.

View Effective Data Visualization on Amazon

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Read the book and take notes on key concepts
Apply the techniques to your own data visualization projects
Discuss the book's concepts with peers or mentors

Matplotlib Practice Drills

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Reinforce your understanding of Matplotlib's functionality through hands-on coding exercises.

Browse courses on Matplotlib

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Explore Matplotlib's interactive plotting capabilities
Create different types of plots and customize their appearance
Practice manipulating and transforming data for visualization

Data Visualization Critique Workshop

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Enhance your visualization skills by receiving and providing feedback on data visualizations.

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Prepare a data visualization and bring it to the workshop
Present your visualization to the group and receive feedback
Provide feedback on the visualizations presented by others

Create Data Visualization Portfolio

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Enhance your understanding of data visualization techniques by applying them to real-world datasets.

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Identify an interesting dataset
Design and create visualizations
Write a brief analysis of your findings

Interactive Dash Tutorial

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Advance your proficiency in creating dynamic and user-friendly visualizations using Dash.

Browse courses on Dash

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Follow an online tutorial on Dash fundamentals
Build an interactive dashboard with data filtering and drill-down capabilities
Deploy your dashboard for real-time data exploration

Comparative Analysis of Visualization Tools

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Develop a deeper understanding of the strengths and limitations of various data visualization tools.

Browse courses on Data Visualization Tools

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Research and select a set of visualization tools
Create visualizations using each tool
Compare and contrast the tools based on factors such as ease of use, functionality, and aesthetics
Write a blog post or article summarizing your findings

Career center

Learners who complete Data Visualization with Python will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts use their skills in identifying trends and patterns in raw data to uncover insights that can help their organizations improve efficiency and gain an edge. They are responsible for organizing data, identifying sources of bias, and making sure that data is reliable. This course can help someone in this role refine their skills in data visualization by using coding tools such as Python. By doing so, they will be able to better communicate data and its insights to other stakeholders in a clear and concise way.

See salaries and explore the career path for Data Analyst

Business Intelligence Analyst

Business Intelligence Analysts use data to understand an organization's past performance and make recommendations for future actions. They are responsible for analyzing data to identify trends and patterns, and then developing reports and presentations that can be used by decision-makers. This course can help Business Intelligence Analysts learn how to use Python, Pandas, Seaborn, Matplotlib, Plotly, Dash, Streamlit, Tableau, and Amazon QuickSight to create visually appealing and interactive visualizations. By doing so, they will be able to better communicate their findings to stakeholders in a way that is both informative and persuasive.

See salaries and explore the career path for Business Intelligence Analyst

Data Scientist

Data Scientists are responsible for handling the full lifecycle of data, from its collection and storage to its analysis and interpretation. They use their skills in statistics and machine learning to build models that can identify complex relationships within data. This course may be useful for Data Scientists who wish to gain a better understanding of how to use Python for data visualization. By learning how to create compelling visualizations, Data Scientists will be able to better communicate their findings to stakeholders in a way that is both informative and persuasive.

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Market Researcher

Market Researchers conduct surveys, focus groups, and other research to gather data on consumer behavior. They use this data to understand consumer needs and preferences, and then develop marketing campaigns that are tailored to specific audiences. This course may be useful for Market Researchers who wish to gain a better understanding of how to use Python for data visualization. By learning how to create compelling visualizations, Market Researchers will be able to better communicate their findings to stakeholders in a way that is both informative and persuasive.

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Statistician

Statisticians use data to understand the world around them. They are responsible for designing experiments, collecting data, and analyzing data to identify trends and patterns. This course may be useful for Statisticians who wish to gain a better understanding of how to use Python for data visualization. By learning how to create compelling visualizations, Statisticians will be able to better communicate their findings to stakeholders in a way that is both informative and persuasive.

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Financial Analyst

Financial Analysts use data to analyze financial performance and make recommendations for investment decisions. They are responsible for evaluating financial data, identifying trends and patterns, and developing reports and presentations that can be used by investors. This course may be useful for Financial Analysts who wish to gain a better understanding of how to use Python for data visualization. By learning how to create compelling visualizations, Financial Analysts will be able to better communicate their findings to stakeholders in a way that is both informative and persuasive.

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Epidemiologist

Epidemiologists use data to investigate the causes of disease and develop strategies to prevent and control disease outbreaks. They are responsible for collecting data on disease outbreaks, identifying risk factors, and developing public health interventions. This course may be useful for Epidemiologists who wish to gain a better understanding of how to use Python for data visualization. By learning how to create compelling visualizations, Epidemiologists will be able to better communicate their findings to stakeholders in a way that is both informative and persuasive.

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Data Journalist

Data Journalists use data to tell stories and inform the public about important issues. They are responsible for gathering data, analyzing data, and writing articles that are both informative and engaging. This course may be useful for Data Journalists who wish to gain a better understanding of how to use Python for data visualization. By learning how to create compelling visualizations, Data Journalists will be able to better communicate their findings to stakeholders in a way that is both informative and persuasive.

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Data Engineer

Data Engineers design and build the systems and infrastructure that are used to store and process data. They are responsible for ensuring that data is reliable, secure, and accessible. This course may be useful for Data Engineers who wish to gain a better understanding of how to use Python for data visualization. By learning how to create compelling visualizations, Data Engineers will be able to better communicate their findings to stakeholders in a way that is both informative and persuasive.

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UX Designer

UX Designers design and evaluate user interfaces to ensure that they are both user-friendly and efficient. They are responsible for gathering feedback from users, conducting usability studies, and developing design prototypes. This course may be useful for UX Designers who wish to gain a better understanding of how to use Python for data visualization. By learning how to create compelling visualizations, UX Designers will be able to better communicate their findings to stakeholders in a way that is both informative and persuasive.

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Marketing Manager

Marketing Managers are responsible for developing and executing marketing campaigns. They are responsible for defining the target audience, setting the marketing budget, and developing the marketing message. This course may be useful for Marketing Managers who wish to gain a better understanding of how to use Python for data visualization. By learning how to create compelling visualizations, Marketing Managers will be able to better communicate their findings to stakeholders in a way that is both informative and persuasive.

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Web Developer

Web Developers design, develop, and maintain websites. They are responsible for turning user requirements into working websites that meet the needs of the business. This course may be useful for Web Developers who wish to gain a better understanding of how to use Python for data visualization. By learning how to create compelling visualizations, Web Developers will be able to better communicate their findings to stakeholders in a way that is both informative and persuasive.

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Software Engineer

Software Engineers design, develop, and maintain software applications. They are responsible for turning user requirements into working software that meets the needs of the business. This course may be useful for Software Engineers who wish to gain a better understanding of how to use Python for data visualization. By learning how to create compelling visualizations, Software Engineers will be able to better communicate their findings to stakeholders in a way that is both informative and persuasive.

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Sales Manager

Sales Managers are responsible for leading and motivating a sales team. They are responsible for setting sales goals, developing sales strategies, and closing deals. This course may be useful for Sales Managers who wish to gain a better understanding of how to use Python for data visualization. By learning how to create compelling visualizations, Sales Managers will be able to better communicate their findings to stakeholders in a way that is both informative and persuasive.

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Product Manager

Product Managers are responsible for the development and launch of new products. They are responsible for defining the product vision, setting the product roadmap, and working with engineers and designers to bring the product to market. This course may be useful for Product Managers who wish to gain a better understanding of how to use Python for data visualization. By learning how to create compelling visualizations, Product Managers will be able to better communicate their findings to stakeholders in a way that is both informative and persuasive.

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