We may earn an affiliate commission when you visit our partners.
Exploratory Data Analysis Techniques in Python
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).
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).
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
Save this course
Save Exploratory Data Analysis Techniques in Python to your list so you can find it easily later:
Save
Save
Activities
Coming soon
We're preparing activities for
Exploratory Data Analysis Techniques in Python.
These are activities you can do either before, during, or after a course.
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
Share
Similar courses
Here are nine courses similar to
Exploratory Data Analysis Techniques in Python.
Exploratory Data Analysis with Complex Data Sets in Python
OpenCourser.com/course/arcvmu/exploratory
Exploratory Data Analysis with Complex Data Sets in Python
Most relevant
Exploratory Data Analysis with Python
OpenCourser.com/course/wxulfr/exploratory
Exploratory Data Analysis with Python
Most relevant
Exploratory Data Analysis (EDA) in Google Sheets
OpenCourser.com/course/0bh1mu/exploratory
Exploratory Data Analysis (EDA) in Google Sheets
Most relevant
Data Processing and Feature Engineering with MATLAB
OpenCourser.com/course/x5buqh/data
Data Processing and Feature Engineering with MATLAB
Most relevant
Data Analysis with Python
OpenCourser.com/course/pfqo74/data
Data Analysis with Python
Most relevant
Exploratory Data Analysis With Python and Pandas
OpenCourser.com/course/4s3knu/exploratory
Exploratory Data Analysis With Python and Pandas
Most relevant
Practical Data Wrangling with Pandas
OpenCourser.com/course/p10yjt/practical
Practical Data Wrangling with Pandas
Most relevant
Exploratory Data Analysis in R
OpenCourser.com/course/0bht5r/exploratory
Exploratory Data Analysis in R
Most relevant
Exploratory Data Analysis with Seaborn
OpenCourser.com/course/5rwny4/exploratory
Exploratory Data Analysis with Seaborn
Most relevant
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
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser