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This course will teach you how to explore, analyze, and visualize large datasets by using popular python libraries like NumPy, Pandas, and Matplotlib to perform Exploratory Data Analysis (EDA).
Exploratory Data Analysis (EDA) is a crucial step in any data analysis project.
In this course, Exploratory Data Analysis Techniques in Python, you'll gain the ability to perform EDA on large data sets using Python.
First, you'll learn about visual and clustering exploratory techniques to identify patterns, clusters, and relationships within your data.
Read more
This course will teach you how to explore, analyze, and visualize large datasets by using popular python libraries like NumPy, Pandas, and Matplotlib to perform Exploratory Data Analysis (EDA).
Exploratory Data Analysis (EDA) is a crucial step in any data analysis project.
In this course, Exploratory Data Analysis Techniques in Python, you'll gain the ability to perform EDA on large data sets using Python.
First, you'll learn about visual and clustering exploratory techniques to identify patterns, clusters, and relationships within your data.
Read more
This course will teach you how to explore, analyze, and visualize large datasets by using popular python libraries like NumPy, Pandas, and Matplotlib to perform Exploratory Data Analysis (EDA).
Exploratory Data Analysis (EDA) is a crucial step in any data analysis project.
In this course, Exploratory Data Analysis Techniques in Python, you'll gain the ability to perform EDA on large data sets using Python.
First, you'll learn about visual and clustering exploratory techniques to identify patterns, clusters, and relationships within your data.
Next, you'll discover data distribution, including quantitative, summary, and descriptive techniques that will help you understand the distribution of your data and its key features.
Finally, you'll understand how to use sampling and correlation techniques to explore the relationships between different variables in your data.
When you’re finished with this course, you’ll have the skills and knowledge of exploratory data analysis needed to analyze, visualize, and summarize your data better than ever before!
Register for this course and see more details by visiting:
OpenCourser.com/course/rtfrnn/exploratory
What's inside
Syllabus
Course Overview
Introducing Exploratory Data Analysis (EDA)
Using Visual Exploratory Techniques for Data Analysis
Clustering Techniques for EDA
Read more
Syllabus
Course Overview
Introducing Exploratory Data Analysis (EDA)
Using Visual Exploratory Techniques for Data Analysis
Clustering Techniques for EDA
Read more
Understanding Data Distribution Types for EDA
Exploring Quantitative, Summary, and Descriptive Techniques for EDA
Sampling and Correlation Techniques for EDA
Conclusion and Next Steps
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Expands learners' analytical literacy in data science by teaching exploratory data analysis techniques
Suits data analysts, developers, and scientists seeking to sharpen their data analysis skills
Leverages Python libraries widely used in data science, making it industry-relevant
Provides a comprehensive foundation in exploratory data analysis techniques, covering both visual and statistical methods
Taught by instructors with experience in data analysis and machine learning
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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 Techniques in Python with these
activities:
Review descriptive statistics
Show steps
Helps to understand the basic concepts of descriptive statistics, which is a prerequisite for this course.
Browse courses on
Descriptive Statistics
Show steps
-
Go over your notes from a previous course on statistics.
-
Review online tutorials on descriptive statistics.
-
Complete practice problems on descriptive statistics.
Brush up on basic statistics
Show steps
Review essential statistical concepts like central tendencies, hypothesis testing, and probability distributions to build a solid foundation for the course.
Browse courses on
Statistics
Show steps
-
Revisit key concepts from an introductory statistics textbook
-
Solve practice problems on topics like mean, median, and standard deviation
-
Go through online tutorials on probability distributions
Read 'Data Analysis with Python' by Wes McKinney
Show steps
Gain a comprehensive understanding of data analysis techniques and their implementation in Python by reading this highly-regarded book.
View
Python for Data Analysis: Data Wrangling with...
on Amazon
Show steps
-
Acquire a copy of 'Data Analysis with Python'
-
Read through the chapters and take notes on key concepts
-
Complete the exercises and coding challenges provided in the book
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Follow tutorials on NumPy and Pandas
Show steps
Familiarize yourself with NumPy and Pandas through guided tutorials to master data structures, indexing, and data manipulation techniques.
Browse courses on
NumPy
Show steps
-
Find tutorials on official NumPy and Pandas documentation
-
Go through video tutorials on data manipulation using these libraries
-
Complete hands-on exercises to apply your learnings
Practice data visualization techniques
Show steps
Provides an opportunity to practice the data visualization techniques that are covered in this course, which will help to improve your skills in this area.
Browse courses on
Data Visualization
Show steps
-
Find a dataset that you are interested in.
-
Create a variety of visualizations using the data.
-
Evaluate the effectiveness of your visualizations.
Solve practice problems on Exploratory Data Analysis
Show steps
Reinforce your understanding of EDA techniques by solving practice problems involving data exploration, visualization, and statistical analysis.
Browse courses on
Exploratory Data Analysis
Show steps
-
Find practice problems online or in textbooks
-
Apply EDA techniques to analyze datasets
-
Evaluate your solutions and identify areas for improvement
Join a peer study group for the course
Show steps
Enhance your learning experience by collaborating and discussing course concepts with peers in a supportive and interactive environment.
Show steps
-
Reach out to fellow classmates to form or join a study group
-
Schedule regular meetings to discuss course material
-
Share notes, insights, and perspectives with each other
Create a tutorial on exploratory data analysis
Show steps
Helps you to deepen your understanding of exploratory data analysis by teaching others about it.
Browse courses on
Exploratory Data Analysis
Show steps
-
Choose a specific topic within exploratory data analysis that you want to focus on.
-
Develop a lesson plan for your tutorial.
-
Create the content for your tutorial.
-
Share your tutorial with others.
Develop a data visualization dashboard
Show steps
Create an interactive data visualization dashboard using tools like Tableau or Power BI to present your exploratory data analysis findings in a compelling and visually appealing manner.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset and explore it using EDA techniques
-
Design and create visualizations to represent key insights
-
Develop an interactive dashboard to showcase your findings
Attend a workshop on Advanced Data Analysis
Show steps
Expand your knowledge and skills in advanced data analysis techniques such as machine learning and big data processing by attending a relevant workshop.
Browse courses on
Data Analysis
Show steps
-
Research and identify relevant workshops
-
Register for a workshop that aligns with your learning goals
-
Actively participate in the workshop sessions
Become a mentor for a beginner in data analysis
Show steps
Reinforce your understanding while helping others by mentoring a beginner in data analysis, sharing your knowledge and guiding their learning journey.
Show steps
-
Identify opportunities to mentor others on platforms like LinkedIn or online forums
-
Provide guidance and support on data analysis concepts and techniques
-
Offer feedback on their projects and assignments
Review descriptive statistics
Show steps
Helps to understand the basic concepts of descriptive statistics, which is a prerequisite for this course.
Browse courses on
Descriptive Statistics
Show steps
Show steps
Show steps
- Go over your notes from a previous course on statistics.
- Review online tutorials on descriptive statistics.
- Complete practice problems on descriptive statistics.
Brush up on basic statistics
Show steps
Review essential statistical concepts like central tendencies, hypothesis testing, and probability distributions to build a solid foundation for the course.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Revisit key concepts from an introductory statistics textbook
- Solve practice problems on topics like mean, median, and standard deviation
- Go through online tutorials on probability distributions
Read 'Data Analysis with Python' by Wes McKinney
Show steps
Gain a comprehensive understanding of data analysis techniques and their implementation in Python by reading this highly-regarded book.
View
Python for Data Analysis: Data Wrangling with...
on Amazon
Show steps
Show steps
Show steps
- Acquire a copy of 'Data Analysis with Python'
- Read through the chapters and take notes on key concepts
- Complete the exercises and coding challenges provided in the book
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Follow tutorials on NumPy and Pandas
Show steps
Familiarize yourself with NumPy and Pandas through guided tutorials to master data structures, indexing, and data manipulation techniques.
Browse courses on
NumPy
Show steps
Show steps
Show steps
- Find tutorials on official NumPy and Pandas documentation
- Go through video tutorials on data manipulation using these libraries
- Complete hands-on exercises to apply your learnings
Practice data visualization techniques
Show steps
Provides an opportunity to practice the data visualization techniques that are covered in this course, which will help to improve your skills in this area.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Find a dataset that you are interested in.
- Create a variety of visualizations using the data.
- Evaluate the effectiveness of your visualizations.
Solve practice problems on Exploratory Data Analysis
Show steps
Reinforce your understanding of EDA techniques by solving practice problems involving data exploration, visualization, and statistical analysis.
Browse courses on
Exploratory Data Analysis
Show steps
Show steps
Show steps
- Find practice problems online or in textbooks
- Apply EDA techniques to analyze datasets
- Evaluate your solutions and identify areas for improvement
Join a peer study group for the course
Show steps
Enhance your learning experience by collaborating and discussing course concepts with peers in a supportive and interactive environment.
Show steps
Show steps
Show steps
- Reach out to fellow classmates to form or join a study group
- Schedule regular meetings to discuss course material
- Share notes, insights, and perspectives with each other
Create a tutorial on exploratory data analysis
Show steps
Helps you to deepen your understanding of exploratory data analysis by teaching others about it.
Browse courses on
Exploratory Data Analysis
Show steps
Show steps
Show steps
- Choose a specific topic within exploratory data analysis that you want to focus on.
- Develop a lesson plan for your tutorial.
- Create the content for your tutorial.
- Share your tutorial with others.
Develop a data visualization dashboard
Show steps
Create an interactive data visualization dashboard using tools like Tableau or Power BI to present your exploratory data analysis findings in a compelling and visually appealing manner.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset and explore it using EDA techniques
- Design and create visualizations to represent key insights
- Develop an interactive dashboard to showcase your findings
Attend a workshop on Advanced Data Analysis
Show steps
Expand your knowledge and skills in advanced data analysis techniques such as machine learning and big data processing by attending a relevant workshop.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Research and identify relevant workshops
- Register for a workshop that aligns with your learning goals
- Actively participate in the workshop sessions
Become a mentor for a beginner in data analysis
Show steps
Reinforce your understanding while helping others by mentoring a beginner in data analysis, sharing your knowledge and guiding their learning journey.
Show steps
Show steps
Show steps
- Identify opportunities to mentor others on platforms like LinkedIn or online forums
- Provide guidance and support on data analysis concepts and techniques
- Offer feedback on their projects and assignments
Career center
Learners who complete Exploratory Data Analysis Techniques in Python will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst takes raw data and transforms it into understandable insights. Using various mathematic and statistical techniques, a Data Analyst will help businesses understand patterns or trends, which companies can then use to make informed decisions. This course helps build a foundation for a Data Analyst by exploring data and using proven techniques for data analysis and visualization. Skills from this course will help you succeed in this role and make you a more efficient and effective Data Analyst.
Data Scientist
Data Scientist
A Data Scientist uses data to solve complex business problems. Data Scientists need to not only understand the data, but also be able to translate their findings into real-world solutions and strategies. This course will help build a foundation for a Data Scientist by gaining the skills to explore data, make recommendations based on findings, and use programming to build solutions.
Statistician
Statistician
A Statistician collects, analyzes, interprets, and presents data. They use mathematical and statistical methods to gain insights into the data. A strong foundation in data analysis techniques is required to be successful in this role, and this course provides that essential foundation by introducing various techniques for exploratory data analysis and data visualization.
Business Analyst
Business Analyst
A Business Analyst helps companies improve their performance by analyzing data and making recommendations. They need to be able to understand the business and its goals, as well as the data that is available. This course will help build a foundation for a Business Analyst by providing the skills to explore and analyze data, which can then be used to provide insights and recommendations.
Market Researcher
Market Researcher
A Market Researcher conducts surveys, collects data, and analyzes market trends to help companies understand their customers and make informed decisions. The ability to analyze and interpret data is essential for success in this role, and this course will help build the skills needed to do so.
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains the infrastructure that is used to store and process data. They need to have a strong understanding of data and how it can be used to solve business problems. This course will help build a foundation for a Data Engineer by providing an introduction to data analysis techniques.
Financial Analyst
Financial Analyst
A Financial Analyst uses data to make recommendations about investments and financial decisions. They need to be able to understand financial data and make sound judgments based on their analysis. This course will help build a foundation for a Financial Analyst by providing an introduction to data analysis techniques and how they can be used in the financial industry.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and statistical techniques to solve complex problems in business and industry. They need to be able to understand the problem, collect and analyze data, and develop and implement solutions. This course will help build a foundation for an Operations Research Analyst by providing an introduction to data analysis techniques.
Actuary
Actuary
An Actuary uses mathematical and statistical techniques to assess risk and uncertainty. They need to be able to understand complex financial and insurance data and make sound judgments based on their analysis. This course may be useful for an Actuary by providing an introduction to data analysis techniques and how they can be used in the insurance industry.
Epidemiologist
Epidemiologist
An Epidemiologist studies the distribution and patterns of health events and diseases in a population. They need to be able to collect and analyze data, and make recommendations to prevent and control diseases. This course may be useful for an Epidemiologist by providing an introduction to data analysis techniques and how they can be used in the field of public health.
Biostatistician
Biostatistician
A Biostatistician applies statistical methods to data in the fields of biology and medicine. They need to be able to design studies, collect and analyze data, and interpret their findings. This course may be useful for a Biostatistician by providing an introduction to data analysis techniques and how they can be used in the field of biostatistics.
Data Journalist
Data Journalist
A Data Journalist uses data to tell stories and explain complex issues. They need to be able to understand data, analyze it, and present their findings in a clear and concise way. This course may be useful for a Data Journalist by providing an introduction to data analysis techniques and how they can be used in the field of journalism.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. They need to be able to understand the problem, collect and analyze data, and develop and implement solutions. This course may be useful for a Software Engineer by providing an introduction to data analysis techniques and how they can be used in the field of software development.
Web Developer
Web Developer
A Web Developer designs and develops websites and web applications. They need to be able to understand the problem, collect and analyze data, and develop and implement solutions. This course may be useful for a Web Developer by providing an introduction to data analysis techniques and how they can be used in the field of web development.
Product Manager
Product Manager
A Product Manager oversees the development and launch of new products. They need to be able to understand the market, collect and analyze data, and make decisions about product features and pricing. This course may be useful for a Product Manager by providing an introduction to data analysis techniques and how they can be used in the field of product management.
For more career information including salaries, visit:
OpenCourser.com/course/rtfrnn/exploratory
Reading list
We've selected 11 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 Techniques in Python.
Provides a practical introduction to data science, covering the full data science pipeline from data collection to modeling and deployment. It great resource for anyone looking to learn the basics of data science and how to apply it to real-world problems.
Provides a gentle introduction to machine learning, covering the basics of supervised and unsupervised learning. It great resource for anyone looking to learn the basics of machine learning and how to apply it to real-world problems.
Provides a comprehensive overview of Python libraries for data analysis, including NumPy, Pandas, and Matplotlib. It valuable resource for anyone looking to learn more about data analysis in Python.
Provides a comprehensive overview of data visualization techniques in Python, covering a wide range of plots and charts. It valuable resource for anyone looking to learn more about data visualization and how to communicate data effectively.
Provides a comprehensive overview of deep learning, covering the basics of neural networks and how to apply them to real-world problems. It valuable resource for anyone looking to learn more about deep learning and how to apply it to data analysis.
Provides a comprehensive overview of statistical learning, covering the basics of supervised and unsupervised learning. It great resource for anyone looking to learn the basics of statistical learning and how to apply it to real-world problems.
Provides a comprehensive overview of data analytics, covering a wide range of topics from data collection to modeling and deployment. It great resource for anyone looking to learn the basics of data analytics and how to apply it to real-world problems.
Provides a comprehensive overview of data science, covering a wide range of topics from data exploration to modeling and deployment. It great resource for anyone looking to learn the basics of data science and how to apply it to real-world problems.
Save
Provides a comprehensive overview of data mining, covering a wide range of topics from data preprocessing to modeling and evaluation. It great resource for anyone looking to learn the basics of data mining and how to apply it to real-world problems.
Save
Provides a comprehensive overview of machine learning, covering a wide range of topics from supervised and unsupervised learning to model selection and evaluation. It great resource for anyone looking to learn the basics of machine learning and how to apply it to real-world problems.
Provides a comprehensive overview of data science for business, covering a wide range of topics from data collection to modeling and deployment. It great resource for anyone looking to learn the basics of data science and how to apply it to real-world business problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/rtfrnn/exploratory
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Time
129 hours
Level
Intermediate
Via
Pluralsight
Language
English
Good to know
Expands learners' analytical literacy in data science by teaching exploratory data analysis techniques
Suits data analysts, developers, and scientists seeking to sharpen their data analysis skills
Leverages Python libraries widely used in data science, making it industry-relevant
Provides a comprehensive foundation in exploratory data analysis techniques, covering both visual and statistical methods
Taught by instructors with experience in data analysis and machine learning
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