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Snehan Kekre
Producing visualizations is an important first step in exploring and analyzing real-world data sets. As such, visualization is an indispensable method in any data scientist's toolbox. It is also a powerful tool to identify problems in analyses and for illustrating results.In this project-based course, we will employ the statistical data visualization library, Seaborn, to discover and explore the relationships in the Breast Cancer Wisconsin (Diagnostic) Data Set. We will cover key concepts in exploratory data analysis (EDA) using visualizations to identify and interpret inherent relationships in the data set, produce various chart types including histograms, violin plots, box plots, joint plots, pair grids, and heatmaps, customize plot aesthetics and apply faceting methods to visualize higher dimensional data.
Read more
Producing visualizations is an important first step in exploring and analyzing real-world data sets. As such, visualization is an indispensable method in any data scientist's toolbox. It is also a powerful tool to identify problems in analyses and for illustrating results.In this project-based course, we will employ the statistical data visualization library, Seaborn, to discover and explore the relationships in the Breast Cancer Wisconsin (Diagnostic) Data Set. We will cover key concepts in exploratory data analysis (EDA) using visualizations to identify and interpret inherent relationships in the data set, produce various chart types including histograms, violin plots, box plots, joint plots, pair grids, and heatmaps, customize plot aesthetics and apply faceting methods to visualize higher dimensional data.
Read more
Producing visualizations is an important first step in exploring and analyzing real-world data sets. As such, visualization is an indispensable method in any data scientist's toolbox. It is also a powerful tool to identify problems in analyses and for illustrating results.In this project-based course, we will employ the statistical data visualization library, Seaborn, to discover and explore the relationships in the Breast Cancer Wisconsin (Diagnostic) Data Set. We will cover key concepts in exploratory data analysis (EDA) using visualizations to identify and interpret inherent relationships in the data set, produce various chart types including histograms, violin plots, box plots, joint plots, pair grids, and heatmaps, customize plot aesthetics and apply faceting methods to visualize higher dimensional data.
This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and scikit-learn pre-installed.
Notes:
- You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want.
- This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/5rwny4/exploratory
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What's inside
Syllabus
Project: Exploratory Data Analysis with Seaborn
Producing visualizations is an important first step in exploring and analyzing real-world data sets. As such, visualization is an indispensable method in any data scientist's toolbox. It is also a powerful tool to identify problems in analyses and for illustrating results.In this project-based course, we will employ the statistical data visualization library, Seaborn, to discover and explore the relationships in the Breast Cancer Wisconsin (Diagnostic) Data Set. We will cover key concepts in exploratory data analysis (EDA) using visualizations to identify and interpret inherent relationships in the data set, produce various chart types including histograms, violin plots, box plots, joint plots, pair grids, and heatmaps, customize plot aesthetics and apply faceting methods to visualize higher dimensional data.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for aspiring data scientists interested in learning Seaborn, a statistical data visualization library
Uses Breast Cancer Wisconsin (Diagnostic) Data Set, which is relevant to healthcare and medical professionals
Introduces key concepts in exploratory data analysis through hands-on project
Leverages cloud desktops with preconfigured software, allowing for convenient project execution
Learners have limited access to the cloud desktop for project completion
Course availability is currently limited to learners in the North America region
Save this course
Save Exploratory Data Analysis with Seaborn to your list so you can find it easily later:
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Reviews summary
Seaborn exploratory data analysis
Learners say this well-received course is a great guided project to practice EDA. It is especially good for beginners, and covers basic EDA principles and useful Python code for building visualizations in Seaborn. The instructor's explanations are clear, and the hands-on exercises are helpful and practical.
Easy-to-follow explanations and well-paced delivery
"The instructor did an amazing job in this project!"
"Awesome guided project part 1 of 2. Great instructor, going step by step with clear explanation for a short course."
"He is a great teacher and knows how to teach. He explains each code."
Engaging assignments that reinforce concepts
"The hands on session is really helpful as it's not just a read and go thing"
"I needed to practice an Exploratory Data Analysis EDA and I find it so helpful project to practice."
"This project is excellent and one can blindly go for it, especially if u want to learn seaborn library, everything is explained very well"
A great option for new learners
"As a beginner, this was a very good insight into EDA for me."
"It is a great course, very well explained and helped me to learn new things in Python about data analysis."
"For a beginner stepping into the Data analysis domain, this guided project gives you the best shot at having a good hands on experience."
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 Exploratory Data Analysis with Seaborn with these
activities:
Review the fundamentals of descriptive statistics
Show steps
Refreshing your knowledge of descriptive statistics will help you understand the concepts of exploratory data analysis and the different types of visualizations used to represent data.
Browse courses on
Descriptive Statistics
Show steps
-
Review your notes or textbook on descriptive statistics.
-
Complete practice problems or online quizzes.
Review basic statistical concepts
Show steps
Reviewing essential statistical ideas will guarantee a solid foundation for learning statistical data visualization techniques.
Browse courses on
Data Visualization
Show steps
-
Reread lecture notes or textbook chapters on basic statistics.
-
Work through practice problems or online quizzes to test your understanding.
Follow a tutorial on using Seaborn
Show steps
Enhance your understanding of Seaborn by following a well-structured tutorial. This will provide you with a guided approach to learning the library's features and capabilities.
Browse courses on
Seaborn
Show steps
-
Find a reputable tutorial on Seaborn
-
Follow the tutorial step-by-step, experimenting with the code and examples
-
Apply what you learn to your own data analysis projects
14 other activities
Expand to see all activities and additional details
Show all 17 activities
Practice creating visualizations using Seaborn
Show steps
Practicing creating visualizations using Seaborn will help you become more proficient in using the library and creating effective data visualizations.
Browse courses on
Seaborn
Show steps
-
Follow along with the tutorials provided in the course.
-
Create visualizations for practice datasets.
Discuss data visualization techniques with classmates
Show steps
Discussing data visualization techniques with classmates will help you learn from others' perspectives and approaches to data visualization.
Show steps
-
Join or form a study group with classmates.
-
Discuss different data visualization techniques.
-
Share examples of effective data visualizations.
Practice using Seaborn to create visualizations
Show steps
Immerse yourself in the world of data visualization by practicing the creation of your own visualizations using Seaborn. This will solidify your understanding of the library and enhance your data analysis skills.
Browse courses on
Seaborn
Show steps
-
Set up your Python environment and install Seaborn
-
Load the Breast Cancer Wisconsin (Diagnostic) Dataset into your environment
-
Create a histogram to visualize the distribution of a numerical feature
-
Create a scatter plot to visualize the relationship between two numerical features
-
Create a box plot to visualize the distribution of a numerical feature across different categories
Explore seaborn documentation and tutorials
Show steps
Going through seaborn documentation and tutorials will familiarize you with its capabilities and best practices for data visualization.
Browse courses on
Seaborn
Show steps
-
Visit the seaborn website and explore the documentation.
-
Follow online tutorials or workshops on using seaborn.
-
Experiment with different seaborn functions and options in a Jupyter notebook.
Organize a study group with classmates
Show steps
Discussing the course material with peers will enhance your understanding and provide diverse perspectives.
Show steps
-
Reach out to classmates and set up a regular meeting time.
-
Choose topics to cover during each session.
-
Take turns presenting concepts, leading discussions, and solving problems.
Attend a workshop on using Seaborn
Show steps
Attending a workshop provides an immersive learning experience where you can interact with experts and delve deeper into the practical applications of Seaborn.
Browse courses on
Seaborn
Show steps
-
Find a workshop on Seaborn that fits your schedule and skill level
-
Register for the workshop and prepare any necessary materials
-
Attend the workshop, actively participate in discussions, and ask questions
-
Implement what you learn in your own data analysis projects
Answer questions in course discussion forums
Show steps
Helping others understand concepts will reinforce your own understanding and identify areas where you need further clarification.
Show steps
-
Regularly visit the course discussion forums.
-
Identify questions that you can answer confidently.
-
Provide clear and helpful responses, explaining the concepts and providing examples.
Follow online tutorials on advanced Seaborn techniques
Show steps
Following online tutorials on advanced Seaborn techniques will help you expand your knowledge and skills in using the library.
Browse courses on
Seaborn
Show steps
-
Search for online tutorials on advanced Seaborn techniques.
-
Follow the tutorials and complete the exercises.
Create visualizations for a small dataset
Show steps
Hands-on practice with data visualization will solidify your understanding of seaborn and its applications.
Browse courses on
Data Visualization
Show steps
-
Obtain a small dataset (e.g., from Kaggle or UCI Machine Learning Repository).
-
Import the dataset into a Jupyter notebook.
-
Use seaborn to create various visualizations (e.g., histograms, scatter plots, box plots).
-
Interpret the visualizations and draw insights from the data.
Create a data visualization dashboard
Show steps
Creating a data visualization dashboard will allow you to apply your knowledge of Seaborn to create a real-world data visualization product.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset and define the scope of your dashboard.
-
Create visualizations for the dashboard using Seaborn.
-
Organize and arrange the visualizations into a coherent dashboard.
Create a blog post or video tutorial on using Seaborn
Show steps
By creating a blog post or video tutorial, you will not only reinforce your understanding of Seaborn but also contribute to the learning community by sharing your knowledge.
Browse courses on
Seaborn
Show steps
-
Choose a specific topic or feature of Seaborn to focus on
-
Research and gather information on the topic
-
Create your blog post or video tutorial, ensuring clarity and engaging content
-
Share your creation with others and encourage feedback
Develop a data visualization dashboard
Show steps
Creating a data visualization dashboard will challenge you to apply your skills in a practical, real-world scenario.
Browse courses on
Interactive Data Visualization
Show steps
-
Choose a dataset and define the visualizations to be included in the dashboard.
-
Design the layout and user interface of the dashboard.
-
Implement the visualizations using seaborn and other relevant libraries.
-
Host the dashboard online or share it with others.
Write a blog post or article on seaborn
Show steps
Writing about seaborn will force you to organize your knowledge, identify gaps, and develop a deeper understanding.
Browse courses on
Seaborn
Show steps
-
Choose a specific topic related to seaborn.
-
Research the topic and gather relevant information.
-
Write a well-structured and engaging article.
-
Publish the article on a blog or website.
Contribute to the seaborn project
Show steps
Contributing to the seaborn project will expose you to real-world development practices and enhance your understanding of the library.
Browse courses on
Seaborn
Show steps
-
Identify an area of seaborn that you would like to contribute to.
-
Read the seaborn documentation and contribution guidelines.
-
Propose your changes or feature additions through a pull request.
-
Work with the seaborn maintainers to refine and merge your contributions.
Review the fundamentals of descriptive statistics
Show steps
Refreshing your knowledge of descriptive statistics will help you understand the concepts of exploratory data analysis and the different types of visualizations used to represent data.
Browse courses on
Descriptive Statistics
Show steps
Show steps
Show steps
- Review your notes or textbook on descriptive statistics.
- Complete practice problems or online quizzes.
Review basic statistical concepts
Show steps
Reviewing essential statistical ideas will guarantee a solid foundation for learning statistical data visualization techniques.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Reread lecture notes or textbook chapters on basic statistics.
- Work through practice problems or online quizzes to test your understanding.
Follow a tutorial on using Seaborn
Show steps
Enhance your understanding of Seaborn by following a well-structured tutorial. This will provide you with a guided approach to learning the library's features and capabilities.
Browse courses on
Seaborn
Show steps
Show steps
Show steps
- Find a reputable tutorial on Seaborn
- Follow the tutorial step-by-step, experimenting with the code and examples
- Apply what you learn to your own data analysis projects
14 other activities
Expand to see all activities and additional details
Show all 17 activities
Practice creating visualizations using Seaborn
Show steps
Practicing creating visualizations using Seaborn will help you become more proficient in using the library and creating effective data visualizations.
Browse courses on
Seaborn
Show steps
Show steps
Show steps
- Follow along with the tutorials provided in the course.
- Create visualizations for practice datasets.
Discuss data visualization techniques with classmates
Show steps
Discussing data visualization techniques with classmates will help you learn from others' perspectives and approaches to data visualization.
Show steps
Show steps
Show steps
- Join or form a study group with classmates.
- Discuss different data visualization techniques.
- Share examples of effective data visualizations.
Practice using Seaborn to create visualizations
Show steps
Immerse yourself in the world of data visualization by practicing the creation of your own visualizations using Seaborn. This will solidify your understanding of the library and enhance your data analysis skills.
Browse courses on
Seaborn
Show steps
Show steps
Show steps
- Set up your Python environment and install Seaborn
- Load the Breast Cancer Wisconsin (Diagnostic) Dataset into your environment
- Create a histogram to visualize the distribution of a numerical feature
- Create a scatter plot to visualize the relationship between two numerical features
- Create a box plot to visualize the distribution of a numerical feature across different categories
Explore seaborn documentation and tutorials
Show steps
Going through seaborn documentation and tutorials will familiarize you with its capabilities and best practices for data visualization.
Browse courses on
Seaborn
Show steps
Show steps
Show steps
- Visit the seaborn website and explore the documentation.
- Follow online tutorials or workshops on using seaborn.
- Experiment with different seaborn functions and options in a Jupyter notebook.
Organize a study group with classmates
Show steps
Discussing the course material with peers will enhance your understanding and provide diverse perspectives.
Show steps
Show steps
Show steps
- Reach out to classmates and set up a regular meeting time.
- Choose topics to cover during each session.
- Take turns presenting concepts, leading discussions, and solving problems.
Attend a workshop on using Seaborn
Show steps
Attending a workshop provides an immersive learning experience where you can interact with experts and delve deeper into the practical applications of Seaborn.
Browse courses on
Seaborn
Show steps
Show steps
Show steps
- Find a workshop on Seaborn that fits your schedule and skill level
- Register for the workshop and prepare any necessary materials
- Attend the workshop, actively participate in discussions, and ask questions
- Implement what you learn in your own data analysis projects
Answer questions in course discussion forums
Show steps
Helping others understand concepts will reinforce your own understanding and identify areas where you need further clarification.
Show steps
Show steps
Show steps
- Regularly visit the course discussion forums.
- Identify questions that you can answer confidently.
- Provide clear and helpful responses, explaining the concepts and providing examples.
Follow online tutorials on advanced Seaborn techniques
Show steps
Following online tutorials on advanced Seaborn techniques will help you expand your knowledge and skills in using the library.
Browse courses on
Seaborn
Show steps
Show steps
Show steps
- Search for online tutorials on advanced Seaborn techniques.
- Follow the tutorials and complete the exercises.
Create visualizations for a small dataset
Show steps
Hands-on practice with data visualization will solidify your understanding of seaborn and its applications.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Obtain a small dataset (e.g., from Kaggle or UCI Machine Learning Repository).
- Import the dataset into a Jupyter notebook.
- Use seaborn to create various visualizations (e.g., histograms, scatter plots, box plots).
- Interpret the visualizations and draw insights from the data.
Create a data visualization dashboard
Show steps
Creating a data visualization dashboard will allow you to apply your knowledge of Seaborn to create a real-world data visualization product.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset and define the scope of your dashboard.
- Create visualizations for the dashboard using Seaborn.
- Organize and arrange the visualizations into a coherent dashboard.
Create a blog post or video tutorial on using Seaborn
Show steps
By creating a blog post or video tutorial, you will not only reinforce your understanding of Seaborn but also contribute to the learning community by sharing your knowledge.
Browse courses on
Seaborn
Show steps
Show steps
Show steps
- Choose a specific topic or feature of Seaborn to focus on
- Research and gather information on the topic
- Create your blog post or video tutorial, ensuring clarity and engaging content
- Share your creation with others and encourage feedback
Develop a data visualization dashboard
Show steps
Creating a data visualization dashboard will challenge you to apply your skills in a practical, real-world scenario.
Browse courses on
Interactive Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset and define the visualizations to be included in the dashboard.
- Design the layout and user interface of the dashboard.
- Implement the visualizations using seaborn and other relevant libraries.
- Host the dashboard online or share it with others.
Write a blog post or article on seaborn
Show steps
Writing about seaborn will force you to organize your knowledge, identify gaps, and develop a deeper understanding.
Browse courses on
Seaborn
Show steps
Show steps
Show steps
- Choose a specific topic related to seaborn.
- Research the topic and gather relevant information.
- Write a well-structured and engaging article.
- Publish the article on a blog or website.
Contribute to the seaborn project
Show steps
Contributing to the seaborn project will expose you to real-world development practices and enhance your understanding of the library.
Browse courses on
Seaborn
Show steps
Show steps
Show steps
- Identify an area of seaborn that you would like to contribute to.
- Read the seaborn documentation and contribution guidelines.
- Propose your changes or feature additions through a pull request.
- Work with the seaborn maintainers to refine and merge your contributions.
Career center
Learners who complete Exploratory Data Analysis with Seaborn will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts use their knowledge of data visualization techniques to help businesses make better decisions. This course in Exploratory Data Analysis with Seaborn can help you build a foundation in data visualization, which is an essential skill for any Data Analyst. You'll learn how to use Seaborn to create a variety of charts and graphs that can help you understand your data and communicate your findings to others.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. They use a variety of techniques, including data visualization, to understand data and communicate their findings to others. This course in Exploratory Data Analysis with Seaborn can help you build a foundation in data visualization, which is an essential skill for any Data Scientist. You'll learn how to use Seaborn to create a variety of charts and graphs that can help you understand your data and communicate your findings to others.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make better decisions. They use a variety of techniques, including data visualization, to understand data and communicate their findings to others. This course in Exploratory Data Analysis with Seaborn can help you build a foundation in data visualization, which is an essential skill for any Business Analyst. You'll learn how to use Seaborn to create a variety of charts and graphs that can help you understand your data and communicate your findings to others.
Statistician
Statistician
Statisticians use data to solve problems and make informed decisions. They use a variety of techniques, including data visualization, to understand data and communicate their findings to others. This course in Exploratory Data Analysis with Seaborn can help you build a foundation in data visualization, which is an essential skill for any Statistician. You'll learn how to use Seaborn to create a variety of charts and graphs that can help you understand your data and communicate your findings to others.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers use data to build and train machine learning models. They use a variety of techniques, including data visualization, to understand data and communicate their findings to others. This course in Exploratory Data Analysis with Seaborn can help you build a foundation in data visualization, which is an essential skill for any Machine Learning Engineer. You'll learn how to use Seaborn to create a variety of charts and graphs that can help you understand your data and communicate your findings to others.
Data Visualization Analyst
Data Visualization Analyst
Data Visualization Analysts use data visualization techniques to help businesses make better decisions. They use a variety of tools, including Seaborn, to create charts and graphs that can help businesses understand their data. This course in Exploratory Data Analysis with Seaborn can help you build a foundation in data visualization, which is an essential skill for any Data Visualization Analyst. You'll learn how to use Seaborn to create a variety of charts and graphs that can help you understand your data and communicate your findings to others.
Data Engineer
Data Engineer
Data Engineers use data to build and maintain data pipelines. They use a variety of techniques, including data visualization, to understand data and communicate their findings to others. This course in Exploratory Data Analysis with Seaborn can help you build a foundation in data visualization, which is an essential skill for any Data Engineer. You'll learn how to use Seaborn to create a variety of charts and graphs that can help you understand your data and communicate your findings to others.
Product Manager
Product Manager
Product Managers use data to make decisions about products. They use a variety of techniques, including data visualization, to understand data and communicate their findings to others. This course in Exploratory Data Analysis with Seaborn can help you build a foundation in data visualization, which is an essential skill for any Product Manager. You'll learn how to use Seaborn to create a variety of charts and graphs that can help you understand your data and communicate your findings to others.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment decisions. They use a variety of techniques, including data visualization, to understand data and communicate their findings to others. This course in Exploratory Data Analysis with Seaborn can help you build a foundation in data visualization, which is an essential skill for any Financial Analyst. You'll learn how to use Seaborn to create a variety of charts and graphs that can help you understand your data and communicate your findings to others.
Marketing Analyst
Marketing Analyst
Marketing Analysts use data to make marketing decisions. They use a variety of techniques, including data visualization, to understand data and communicate their findings to others. This course in Exploratory Data Analysis with Seaborn can help you build a foundation in data visualization, which is an essential skill for any Marketing Analyst. You'll learn how to use Seaborn to create a variety of charts and graphs that can help you understand your data and communicate your findings to others.
Software Engineer
Software Engineer
Software Engineers use data to build and maintain software. They use a variety of techniques, including data visualization, to understand data and communicate their findings to others. This course in Exploratory Data Analysis with Seaborn can help you build a foundation in data visualization, which is an essential skill for any Software Engineer. You'll learn how to use Seaborn to create a variety of charts and graphs that can help you understand your data and communicate your findings to others.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data to solve business problems. They use a variety of techniques, including data visualization, to understand data and communicate their findings to others. This course in Exploratory Data Analysis with Seaborn can help you build a foundation in data visualization, which is an essential skill for any Operations Research Analyst. You'll learn how to use Seaborn to create a variety of charts and graphs that can help you understand your data and communicate your findings to others.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use data to make investment decisions. They use a variety of techniques, including data visualization, to understand data and communicate their findings to others. This course in Exploratory Data Analysis with Seaborn can help you build a foundation in data visualization, which is an essential skill for any Quantitative Analyst. You'll learn how to use Seaborn to create a variety of charts and graphs that can help you understand your data and communicate your findings to others.
Market Researcher
Market Researcher
Market Researchers use data to understand consumer behavior. They use a variety of techniques, including data visualization, to understand data and communicate their findings to others. This course in Exploratory Data Analysis with Seaborn can help you build a foundation in data visualization, which is an essential skill for any Market Researcher. You'll learn how to use Seaborn to create a variety of charts and graphs that can help you understand your data and communicate your findings to others.
Data Mining Analyst
Data Mining Analyst
Data Mining Analysts use data to find patterns and trends. They use a variety of techniques, including data visualization, to understand data and communicate their findings to others. This course in Exploratory Data Analysis with Seaborn can help you build a foundation in data visualization, which is an essential skill for any Data Mining Analyst. You'll learn how to use Seaborn to create a variety of charts and graphs that can help you understand your data and communicate your findings to others.
For more career information including salaries, visit:
OpenCourser.com/course/5rwny4/exploratory
Reading list
We've selected 13 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
Exploratory Data Analysis with Seaborn.
Save
A guide to using R for data science. Covers topics such as data cleaning, wrangling, and visualizing data using RStudio and other packages.
Save
An introduction to machine learning. Covers topics such as linear regression, logistic regression, decision trees, and support vector machines.
A guide to using Python for data visualization.
An introduction to using Python for data analysis and visualization. Covers topics such as data cleaning, wrangling, and visualizing data using libraries like Pandas, Matplotlib, and Seaborn.
A comprehensive guide to data science. Covers topics such as data cleaning, analysis, modeling, and visualization.
A guide to using statistical methods for the social sciences. Covers topics such as data collection, analysis, and interpretation.
A comprehensive guide to creating effective data visualizations. Covers topics such as choosing the right chart type, designing for different audiences, and communicating insights from data.
A comprehensive introduction to data science. Covers topics such as data cleaning, analysis, modeling, and visualization.
Save
A guide to using open source tools for data analysis.
A guide to using Python for natural language processing. Covers topics such as tokenization, stemming, lemmatization, and named entity recognition.
Save
A practical guide to data visualization. Covers topics such as choosing the right chart type, designing effective visualizations, and communicating insights from data.
Save
A guide to communicating statistical information effectively.
Save
A step-by-step guide to creating effective data visualizations.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/5rwny4/exploratory
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Effort
1.5 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Snehan Kekre
Language
English
Good to know
Suitable for aspiring data scientists interested in learning Seaborn, a statistical data visualization library
Uses Breast Cancer Wisconsin (Diagnostic) Data Set, which is relevant to healthcare and medical professionals
Introduces key concepts in exploratory data analysis through hands-on project
Leverages cloud desktops with preconfigured software, allowing for convenient project execution
Learners have limited access to the cloud desktop for project completion
Course availability is currently limited to learners in the North America region
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