Visualizing & Communicating Results in Python with Jupyter from Coursera

Code and run your first Python program in minutes without installing anything!

This course is designed for learners with limited coding experience, providing a foundation for presenting data using visualization tools in Jupyter Notebook. This course helps learners describe and make inferences from data, and better communicate and present data.

The modules in this course will cover a wide range of visualizations which allow you to illustrate and compare the composition of the dataset, determine the distribution of the dataset, and visualize complex data such as geographically-based data. Completion of Data Analysis in Python with pandas & matplotlib in Spyder before taking this course is recommended.

To allow for a truly hands-on, self-paced learning experience, this course is video-free.

Assignments contain short explanations with images and runnable code examples with suggested edits to explore code examples further, building a deeper understanding by doing. You’ll benefit from instant feedback from a variety of assessment items along the way, gently progressing from quick understanding checks (multiple choice, fill in the blank, and un-scrambling code blocks) to small, approachable coding exercises that take minutes instead of hours. Finally, an accumulative lab at the end of the course will provide you an opportunity to apply all learned concepts within a real-world context.

What's inside

Syllabus

Creating Comparison and Composition Charts

Creating Distribution Charts

Creating Specialized Visualizations

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Emphasizes data presentation and visualization, a foundational skill in exploratory data analysis

Suitable for learners with limited coding experience, providing a solid starting point for data visualization

Employs an interactive, hands-on approach, encouraging active learning and reinforcement of concepts

Covers a comprehensive range of visualization tools, including comparison charts, distribution charts, and specialized visualizations for complex data

Leverages Jupyter Notebook, a popular platform for data exploration and visualization, providing learners with practical experience in a widely used tool

Recommends completing 'Data Analysis in Python with pandas & matplotlib in Spyder' before taking this course, which may limit accessibility for complete beginners

Reviews summary

Practical python data visualizations

According to learners, 'Visualizing & Communicating Results in Python with Jupyter' is a highly practical and hands-on course that excels in teaching data visualization. Many appreciate the clear explanations and the structure that facilitates a self-paced learning experience. A notable feature is the instant feedback provided on coding exercises, which students find highly beneficial. While the video-free format is a plus for some who prefer reading and exploration, it is a potential challenge for others who miss video lectures for complex concepts. Overall, it's a strong resource for bridging the gap to professional data communication in Python.

Course relies solely on text and images, with no video lectures.

"The video-free format allowed me to go at my own pace without feeling rushed. Highly recommend!"

"I felt a bit lost without video lectures. While the text explanations were thorough, some topics felt like they needed a different teaching approach."

"A solid course if you're comfortable learning without videos."

Concepts are well-explained, suitable for learners new to the tools.

"Good introduction to visualizing data in Jupyter. The explanations were clear, especially for someone new to data science tools."

"The examples are clear, the exercises are challenging yet achievable, and the accumulative lab at the end really brings everything together."

"The text and images provided were generally sufficient for understanding the concepts."

Assessments provide quick validation of learning progress.

"I really appreciated the instant feedback on my code."

"The immediate feedback is a game-changer. Loved it!"

"You’ll benefit from instant feedback from a variety of assessment items along the way, gently progressing from quick understanding checks..."

Emphasizes active learning through hands-on coding assignments.

"The hands-on labs using Jupyter were incredibly helpful, and I really appreciated the instant feedback on my code."

"I found the practical exercises very useful. The explanations were clear, especially for someone new to data science tools."

"The structure of 'explain, example, practice' worked perfectly. It truly helped me communicate my analytical findings more effectively."

Some learners found certain Python aspects challenging, despite prerequisites.

"I did struggle a bit with some of the more advanced Python aspects initially, even though I'd taken the recommended prerequisite."

"Completion of Data Analysis in Python with pandas & matplotlib in Spyder before taking this course is recommended."

"I would recommend reviewing core Python concepts if you're not actively coding, even after taking the suggested prerequisite."

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 Visualizing & Communicating Results in Python with Jupyter with these activities:

Review 'Python for Data Analysis'

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This book provides a comprehensive overview of Python for data analysis and will enhance your understanding of the subject.

View Python for Data Analysis on Amazon

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Read through the book
Take notes and highlight important concepts

Review Concepts of Pandas in Python

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Grasping the fundamental principles of Pandas will set you up for success before the course starts.

Browse courses on Pandas

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Review documentation for Pandas basics
Work through a simple Pandas tutorial
Practice creating and manipulating dataframes

Coding Exercises on Python Data Structures

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Reinforcing your understanding of data structures will aid in effectively manipulating data.

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Find a collection of Python data structures exercises
Work through the exercises and implement solutions

Two other activities

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Follow a Matplotlib Tutorial

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Deepening your understanding of Matplotlib will enhance your ability to present data effectively.

Browse courses on Matplotlib

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Select a comprehensive Matplotlib tutorial
Follow the tutorial step-by-step
Practice creating different types of visualizations

Analyze and Visualize Real-World Dataset

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Applying your skills in a practical setting will reinforce your learning and provide valuable experience.

Browse courses on Data Science Project

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Choose a dataset of interest
Clean and prepare the data
Perform data analysis and visualization
Present your findings in a clear and concise manner

Career center

Learners who complete Visualizing & Communicating Results in Python with Jupyter will develop knowledge and skills that may be useful to these careers:

Data Journalist

Data Journalists use data to tell stories and communicate insights. This course can help Data Journalists build a foundation in Python programming and data visualization for creating compelling data-driven narratives.

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Business Analyst

Business Analysts use data analysis and visualization techniques to support decision-making. This course helps build a foundation for Business Analysts in Python programming and data visualization.

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Data Analyst

Data Analysts analyze data to identify meaningful insights for making informed decisions. This course provides foundational knowledge for Data Analysts in Python programming and data visualization.

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Market Researcher

Market Researchers gather and analyze data to understand consumer behavior. This course can provide Market Researchers with foundational knowledge in Python programming and data visualization techniques.

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Actuary

Actuaries use mathematical and statistical techniques to assess risk and uncertainty. This course can provide Actuaries with a foundation in Python programming and data visualization techniques.

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Statistician

Statisticians apply statistical methods to collect, analyze, interpret, and present data. This course may be useful for Statisticians who want to enhance their data visualization skills using Python.

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Machine Learning Engineer

Machine Learning Engineers build and maintain machine learning models. This course provides a foundation in Python and data visualization for Machine Learning Engineers who need to communicate their results.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze data and make investment decisions. This course may be useful for Quantitative Analysts who wish to enhance their data visualization skills in Python.

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Data Scientist

Data Scientists leverage coding, statistics, and machine learning to analyze data to solve business. This course may also be helpful for Data Scientists who wish to visualize their results using Python.

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Financial Analyst

Financial Analysts evaluate financial data to make investment recommendations. This course may be useful for Financial Analysts who wish to improve their data visualization skills in Python.

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Data Engineer

Data Engineers are responsible for building, maintaining, and optimizing data pipelines. This course can help Data Engineers create visualizations for communicating data insights.

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Product Manager

Product Managers lead the development and launch of products. This course may be helpful for Product Managers who need to visualize data to track progress and make decisions.

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UX Designer

UX Designers create user interfaces and experiences for websites and software. This course may be helpful for UX Designers who want to learn how to visualize data to improve user experience.

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Software Engineer

Software Engineers design, develop, and maintain software systems. This course may be helpful for Software Engineers who need to visualize data as part of their projects.

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Web Developer

Web Developers create and maintain websites. This course may be useful for Web Developers who need to visualize data on their websites.

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