Mastering Data Visualization with Python from Udemy

This course will help you draw meaningful knowledge from the data you have.

Three systems of data visualization in R are covered in this course:

A. Pandas B. Matplotlib C. Seaborn

A. Types of graphs covered in the course using the pandas package:

Time-series: Line Plot

Single Discrete Variable: Bar Plot, Pie Plot

Single Continuous Variable: Histogram, Density or KDE Plot, Box-Whisker Plot

Two Continuous Variable: Scatter Plot

Two Variable: One Continuous, One Discrete: Box-Whisker Plot

B. Types of graphs using Matplotlib library:

Time-series: Line Plot

Single Discrete Variable: Bar Plot, Pie Plot

Single Continuous Variable: Histogram, Density or KDE Plot, Box-Whisker Plot

Two Continuous Variable: Scatter Plot

In addition, we will cover subplots as well, where multiple axes can be plotted on a single figure.

C. Types of graphs using Seaborn library:

In this we will cover three broad categories of plots:

relplot (Relational Plots): Scatter Plot and Line Plot

displot (Distribution Plots): Histogram

What's inside

Syllabus

Introduction

Study Plan - Please do NOT skip this

Download Section 1 Resources

Python Refresher - Part 1

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Covers Pandas, Matplotlib, and Seaborn, which are essential Python libraries for creating effective visualizations and communicating insights from data

Includes a Python refresher, which ensures learners with varying levels of experience can grasp the fundamental concepts and techniques

Explores a wide range of plot types, including time series, bar plots, histograms, and scatter plots, which allows learners to choose the most appropriate visualization for their data

Teaches how to refine plots with annotations, themes, and color palettes, which enables learners to create visually appealing and informative graphics

Features a bonus section on Python for absolute beginners, which makes the course accessible to individuals with no prior programming experience

Focuses on older versions of plotting libraries, which may not reflect the most up-to-date features and functionalities available in the latest releases

Reviews summary

Practical python data visualization with libraries

According to learners, this course offers a largely positive sentiment and is a practical guide to data visualization using Python's key libraries: Pandas, Matplotlib, and Seaborn. Students frequently praise the instructor's clarity and the hands-on labs and practical examples, finding them crucial for mastering the material and applying skills to their work. While the course is seen as providing a solid foundation covering all three libraries, some reviewers found the Matplotlib section less clear and occasionally difficult to follow compared to other parts, particularly Seaborn which is often highlighted positively. Opinions are mixed on whether the included Python refreshers are sufficient for absolute beginners. Overall, it's considered highly relevant for those in data roles.

Skills are applicable in data analysis roles.

"Highly recommend for anyone in data roles."

"Useful for practitioners."

"I feel much more confident in creating professional-looking plots for my work now."

"I'm now using these skills daily in my data analysis tasks."

Teaches Pandas, Matplotlib, and Seaborn.

"Good course covering the main libraries."

"The sections on Matplotlib and Seaborn were very useful."

"Covers the essentials of Pandas, Matplotlib, and Seaborn."

"Provides a comprehensive guide to data visualization using Python's key libraries."

"Covers all the major libraries with great examples."

Instructor is clear and labs are hands-on.

"The instructor explains complex concepts with such clarity."

"The hands-on labs using Jupyter notebooks were particularly helpful."

"The examples are relevant and easy to follow. The instructor is knowledgeable and engaging."

"The step-by-step approach and hands-on labs were perfect."

"Clear, concise, and practical... Instructor makes it easy to follow."

Most students rate this course highly overall.

"Absolutely fantastic course!"

"Excellent course!"

"Loved this course! It's incredibly practical."

"Highly recommended!"

"Fantastic course! Really demystified Python data visualization for me."

Refreshers may not be enough for beginners.

"Requires a bit of prior Python knowledge despite the refreshers."

"The Python refreshers are way too basic if you actually need them."

"The refreshers were okay but didn't fully prepare me for the plotting challenges."

"Assumes more prior knowledge than stated."

Some learners found Matplotlib difficult.

"the Matplotlib section was a bit dry and hard to follow at times."

"Matplotlib felt less intuitive as taught here compared to other resources."

"The actual plotting sections are confusing, especially Matplotlib."

"Matplotlib section was difficult to grasp."

"Matplotlib section could be improved for clarity."

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 Mastering Data Visualization with Python with these activities:

Review Pandas Fundamentals

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Reinforce your understanding of Pandas DataFrames and Series, which are essential for data manipulation and plotting in this course.

Browse courses on Pandas

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Review Pandas documentation on DataFrames and Series.
Practice creating and manipulating DataFrames using sample datasets.
Complete online Pandas tutorials and exercises.

Review 'Storytelling with Data'

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Learn how to effectively communicate insights from your data visualizations by studying the principles of storytelling with data.

View Storytelling with You: Plan, Create, and... on Amazon

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Read the chapters on data storytelling and visualization design.
Analyze examples of good and bad data visualizations.
Apply the principles to your own plots and projects.

Review 'Python Data Science Handbook'

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Deepen your understanding of Pandas, Matplotlib, and Seaborn by studying a comprehensive data science handbook.

View Python Data Science Handbook: Essential Tools... on Amazon

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Read the chapters on Pandas, Matplotlib, and Seaborn.
Work through the examples and exercises in the book.
Compare the book's approach to the course's approach.

Four other activities

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Follow Advanced Seaborn Tutorials

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Expand your Seaborn knowledge by following advanced tutorials on topics like custom color palettes, complex plot combinations, and interactive visualizations.

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Search for advanced Seaborn tutorials on websites like Medium or Towards Data Science.
Choose a tutorial that covers a topic you're interested in.
Follow the tutorial step-by-step, adapting the code to your own datasets.
Experiment with different parameters and options to customize the plots.

Replicate Plots from Online Sources

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Improve your plotting skills by attempting to recreate complex and visually appealing plots found on websites like FlowingData or in academic papers.

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Find a complex plot online or in a publication.
Identify the data and libraries used to create the plot.
Recreate the plot using Pandas, Matplotlib, or Seaborn.
Compare your plot to the original and refine your code.

Visualize Your Personal Data

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Apply your data visualization skills to a personal dataset, such as fitness tracking data, financial transactions, or social media activity.

Show steps

Choose a personal dataset to visualize.
Clean and prepare the data using Pandas.
Create a variety of plots using Matplotlib and Seaborn to explore the data.
Document your findings and insights.

Create a Data Visualization Portfolio

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Showcase your data visualization skills by creating a portfolio of your best plots and projects.

Show steps

Select your best data visualizations from the course and personal projects.
Write a brief description of each visualization, explaining the data and insights.
Create a website or online platform to host your portfolio.
Share your portfolio with potential employers or collaborators.

Career center

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

Data Analyst

As a data analyst, you transform raw data into actionable insights. This course helps build a foundation in creating compelling visualizations using Python libraries like Pandas, Matplotlib, and Seaborn. Data analysts often need to present findings to both technical and nontechnical audiences, and creating effective histograms, scatter plots, and box-whisker plots becomes crucial. Knowing how to refine plots and use subplots, as covered in this course, ensures that the data analyst can communicate complex information clearly. This is a must-have course for mastering the creation of insightful visualizations.

See salaries and explore the career path for Data Analyst

Business Intelligence Analyst

The key role of a Business Intelligence Analyst revolves around identifying trends and patterns in data to improve business decisions. This course would provide a solid foundation in Python-based data visualization. Business intelligence analysts can leverage skills learned in mastering various plot types using Pandas, Matplotlib, and Seaborn. For example, a business intelligence analyst can use time-series line plots to show trends, or bar plots to compare categories. This course's coverage of Seaborn's relational and distribution plots may be particularly useful, helping the business intelligence analyst to present data insights in a palatable way.

See salaries and explore the career path for Business Intelligence Analyst

Data Scientist

The field of data science increasingly relies on effective data storytelling through visuals. This course helps data scientists communicate complex findings. Data scientists must explore and present data using a variety of techniques, and this course offers a practical introduction to Pandas, Matplotlib, and Seaborn. The course's focus on plot refinement, subplots, and customization options ensures that the data scientist can adapt visualizations to specific project needs. The data scientist can use this course to build a strong foundation in data visualization.

See salaries and explore the career path for Data Scientist

Marketing Analyst

Marketing analysts need to understand customer behavior, campaign performance, and market trends. This course can help marketing analysts extract meaningful insights from data and present them effectively. The focus on various plot types using Pandas, Matplotlib, and Seaborn means that a marketing analyst can visualize marketing data to identify trends, measure campaign effectiveness, and understand customer segmentation. For example, a marketing analyst can use the skills learned in this course to present customer segmentation data visually using pie plots or bar plots. Learning the material presented in this course helps the marketing analyst tell a story with their data.

See salaries and explore the career path for Marketing Analyst

Machine Learning Engineer

While machine learning engineers focus on building and deploying models, effectively visualizing data is essential for understanding model performance and communicating results. With this course on data visualization, Machine Learning Engineers can create informative plots for exploratory data analysis and model evaluation. The course will come in handy for generating histograms, scatter plots, and other visualizations. Understanding how to use Seaborn's advanced plotting capabilities, as taught in this course, can help make the machine learning engineer more effective in their work and in communicating it to others.

See salaries and explore the career path for Machine Learning Engineer

Research Scientist

A research scientist needs to analyze and present data from experiments and studies, and this course can prove invaluable. By mastering the plotting techniques taught in this course, research scientists can create compelling visualizations for publications and presentations. The course covers a variety of plot types using Pandas, Matplotlib, and Seaborn, allowing the research scientist to choose the most appropriate visualization for their data. In particular, the course's attention to detail on plot refinement and customization means that the research scientist can tailor visualizations to meet the specific requirements of their field.

See salaries and explore the career path for Research Scientist

Financial Analyst

Financial analysts evaluate investments, manage financial risk, and provide financial advice. Visualizing financial data effectively is crucial for spotting trends, communicating insights, and making informed decisions. Financial analysts can leverage this course to create compelling presentations that showcase financial performance, project future earnings, and assess risk. The course's coverage of time series plots, scatter plots, and other visualization techniques using Pandas, Matplotlib, and Seaborn enables a financial analyst to communicate complex data in a way that the audience can understand. Financial analysts can use this course to improve visual communication.

See salaries and explore the career path for Financial Analyst

Data Engineer

While data engineers primarily focus on building and maintaining data pipelines, understanding how data is used and visualized is essential for ensuring data quality and accessibility. Data engineers can use this course to gain insight into the types of visualizations that data scientists and analysts create, allowing them to better design data infrastructure. The course reviews the use of Pandas, Matplotlib, and Seaborn. Understanding the different plot types and their applications enables the data engineer to optimize data structures and pipelines for visualization purposes. The data engineer can use this course to better understand downstream data needs.

See salaries and explore the career path for Data Engineer

Statistician

Statisticians use statistical methods to analyze data and draw conclusions. This course may help Statisticians present their findings using various visualization techniques. The course covers a variety of plot types using Pandas, Matplotlib, and Seaborn, which allows the statistician to effectively communicate complex statistical results to both technical and nontechnical audiences. A statistician could utilize this course to improve their visual communication skills.

See salaries and explore the career path for Statistician

Healthcare Analyst

Healthcare analysts work with healthcare data to improve patient outcomes, reduce costs, and improve efficiency. The analyst may find this course useful for creating compelling data visualizations to communicate healthcare insights. The course includes a variety of plot types using Pandas, Matplotlib, and Seaborn. Healthcare analysts can use these tools to visualize patient demographics, treatment outcomes, and healthcare costs. Furthermore, the course touches on refining plots and using subplots, which may enable a healthcare analyst to present complex data in a clear and concise manner.

See salaries and explore the career path for Healthcare Analyst

Supply Chain Analyst

Supply chain analysts are responsible for optimizing the flow of goods and services from suppliers to customers. This course may be useful for visualizing supply chain data to identify bottlenecks, improve efficiency, and reduce costs. The course provides an overview of various plot types using Pandas, Matplotlib, and Seaborn, that allow supply chain analysts to communicate complex data in a clear and concise way. A supply chain analyst can use this course to create visualizations of inventory levels, transportation costs, and delivery times.

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Technical Writer

Technical writers create documentation for technical products and services. Although technical writing primarily involves written communication, including visual elements can significantly enhance understanding. This course may be useful for technical writers who need to create visualizations for their documentation. Including visualizations of data, workflows, or system architectures can improve the clarity and engagement of technical documents. The course could enable technical writers to create effective visuals to complement their written explanations.

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Project Manager

Project managers oversee projects from initiation to completion. While project managers may not directly analyze data, understanding how data is visualized can help them monitor project progress and communicate project status to stakeholders. This course may be useful for project managers to understand different visualization techniques. By knowing how data can be effectively presented, project managers can better interpret project reports, identify potential issues, and make informed decisions. Gaining a basic understanding of data visualization can improve project managers' communication and decision-making skills.

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Human Resources Analyst

A Human Resources analyst gathers and analyzes data related to a company's workforce. This course may benefit human resources analysts by letting them visually represent trends in employee performance, demographics, or compensation. The course offers a number of plot types using Pandas, Matplotlib, and Seaborn. The human resources analyst may draw on that information to present insights to management, potentially supporting strategic decision-making. A human resources analyst may find this course a good introduction to data driven decision making.

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Software Developer

While software developers primarily focus on coding, understanding data visualization can be useful for debugging, monitoring application performance, and presenting data to users. Although a software developer might not directly benefit from this course, there may be some use, as the course does cover a variety of plot types using Pandas, Matplotlib, and Seaborn. This could help a software developer create custom dashboards or visualizations for internal tools. This course may be best suited for the software developer, if they aim to create dashboards that make it easier to interpret data.

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Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Mastering Data Visualization with Python.

Storytelling with You

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Focuses on the principles of effective data communication and storytelling through visualizations. It teaches you how to choose the right chart type, eliminate clutter, focus attention, and tell a compelling story with your data. While the course focuses on the technical aspects of creating plots, this book complements it by providing guidance on how to present your visualizations in a clear and impactful way. This book is widely recommended for anyone who wants to improve their data presentation skills.

Storytelling with You: Plan, Create, and Deliver a...

Paperback

Storytelling with You: Plan, Create, and Deliver a...

Kindle Edition

Python Data Science Handbook

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Provides a comprehensive overview of essential Python data science tools, including Pandas, Matplotlib, and Seaborn. It offers in-depth explanations and practical examples that complement the course material. It is particularly useful for understanding the underlying principles and advanced techniques of data visualization. This book is commonly used as a reference by data scientists and valuable resource for expanding your knowledge beyond the course.

Python Data Science Handbook: Essential Tools for...

Paperback

$$$

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What's inside

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Reviews summary

Practical python data visualization with libraries

Activities

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