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Bassim Eledath
In this 2-hour long project-based course, you will learn how to perform Exploratory Data Analysis (EDA) in Python. You will use external Python packages such as Pandas, Numpy, Matplotlib, Seaborn etc. to conduct univariate analysis, bivariate analysis, correlation analysis and identify and handle duplicate/missing data.
Note: 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/4s3knu/exploratory
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What's inside
Syllabus
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Teaches methods for conducting a variety of analyses including univariate, bivariate, and correlation analyses
Develops skills in identifying and handling missing or duplicate data
Appropriate for learners who have some experience or background in data analysis
Taught by Bassim Eledath, an experienced data scientist
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Reviews summary
Practical eda with python and pandas
According to students, this course offers a largely positive and highly practical introduction to Exploratory Data Analysis using Python and Pandas. Learners praise its clear instruction and hands-on, project-based approach, making complex concepts accessible for beginners. The course effectively covers foundational EDA techniques like univariate, bivariate, and correlation analysis, along with handling missing and duplicate data. While many find its concise 2-hour format perfect for focused learning, some experienced users report the content can be too basic. Notably, past technical issues with the lab environment seem to have improved in recent months, with newer reviews citing seamless integration.
Past technical issues appear to be resolved or lessened.
"I encountered several issues with the lab environment not loading correctly, which wasted a lot of my time."
"The Jupyter environment integration was seamless."
"My experience with the hands-on labs was smooth, they solidified my understanding."
Thorough coverage of essential data analysis methods.
"Covers the basics of EDA comprehensively within its short duration... conducting univariate analysis, bivariate analysis, correlation analysis..."
"Handling missing data with real datasets was extremely beneficial."
"The techniques for handling missing and duplicate data were particularly illuminating."
Perfect for those new to EDA or needing a quick start.
"Good introductory course for EDA. The explanations were clear..."
"A perfect course for getting started with Exploratory Data Analysis."
"I'd recommend it for beginners who have some Python exposure."
Excellent explanations and hands-on application.
"The instructor explains the concepts clearly and provides practical examples using Python and Pandas."
"Fantastic project-based learning experience! The course focuses on practical application rather than just theory..."
"The clarity of instruction and the practical exercises really stand out. I feel much more confident in performing EDA tasks now."
Content may be too fundamental for advanced learners.
"As someone with some prior experience in data analysis, I found it a bit too basic and didn't learn much new. It's truly for beginners."
"More advanced learners might find it too superficial, but for someone needing a quick overview or practical application, it's very effective."
"The information was a bit redundant if you've already touched upon data analysis. I expected more depth given the subject."
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 Python and Pandas with these
activities:
Review Python
Show steps
Review the basics of Python to prepare for this course.
Browse courses on
Python
Show steps
-
Review Python syntax
-
Practice writing simple Python programs
-
Complete a few Python coding challenges
Follow Pandas Tutorial
Show steps
Learn how to use Pandas to manipulate and analyze data in Python.
Browse courses on
Pandas
Show steps
-
Follow the official Pandas tutorial
-
Complete the practice exercises
Follow Numpy Tutorial
Show steps
Learn how to use Numpy for numerical operations in Python.
Browse courses on
NumPy
Show steps
-
Follow the official Numpy tutorial
-
Complete the practice exercises
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow Seaborn Tutorial
Show steps
Learn how to use Seaborn to create visualizations in Python.
Browse courses on
Seaborn
Show steps
-
Follow the official Seaborn tutorial
-
Complete the practice exercises
Practice EDA Coding Challenges
Show steps
Apply your knowledge of EDA by completing coding challenges.
Browse courses on
EDA
Show steps
-
Find online EDA coding challenges
-
Solve the challenges using Python
Lead a Study Group
Show steps
Consolidate your knowledge by leading a study group for other students in the course.
Browse courses on
EDA
Show steps
-
Find a group of students who are interested in studying together
-
Plan the study sessions
-
Lead the study sessions
Find an EDA Mentor
Show steps
Seek out an experienced professional who can provide guidance and support with EDA.
Browse courses on
EDA
Show steps
-
Identify potential mentors
-
Reach out to them and express your interest in mentoring
Review Python
Show steps
Review the basics of Python to prepare for this course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review Python syntax
- Practice writing simple Python programs
- Complete a few Python coding challenges
Follow Pandas Tutorial
Show steps
Learn how to use Pandas to manipulate and analyze data in Python.
Browse courses on
Pandas
Show steps
Show steps
Show steps
- Follow the official Pandas tutorial
- Complete the practice exercises
Follow Numpy Tutorial
Show steps
Learn how to use Numpy for numerical operations in Python.
Browse courses on
NumPy
Show steps
Show steps
Show steps
- Follow the official Numpy tutorial
- Complete the practice exercises
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow Seaborn Tutorial
Show steps
Learn how to use Seaborn to create visualizations in Python.
Browse courses on
Seaborn
Show steps
Show steps
Show steps
- Follow the official Seaborn tutorial
- Complete the practice exercises
Practice EDA Coding Challenges
Show steps
Apply your knowledge of EDA by completing coding challenges.
Browse courses on
EDA
Show steps
Show steps
Show steps
- Find online EDA coding challenges
- Solve the challenges using Python
Lead a Study Group
Show steps
Consolidate your knowledge by leading a study group for other students in the course.
Browse courses on
EDA
Show steps
Show steps
Show steps
- Find a group of students who are interested in studying together
- Plan the study sessions
- Lead the study sessions
Find an EDA Mentor
Show steps
Seek out an experienced professional who can provide guidance and support with EDA.
Browse courses on
EDA
Show steps
Show steps
Show steps
- Identify potential mentors
- Reach out to them and express your interest in mentoring
Career center
Learners who complete Exploratory Data Analysis With Python and Pandas will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst is responsible for collecting, cleaning, and analyzing data to help businesses make informed decisions. Exploratory Data Analysis (EDA) with Python and Pandas is a valuable skill for this role, as it allows Data Analysts to quickly explore and summarize large datasets, identify trends and patterns, and make meaningful conclusions. This course provides a solid foundation in EDA techniques, which can help Data Analysts become more efficient and effective in their work.
Data Scientist
Data Scientist
Data Scientists use scientific methods, processes, algorithms, and systems to extract knowledge and insights from data. Exploratory Data Analysis (EDA) with Python and Pandas is a fundamental skill for this role, as it allows Data Scientists to understand the structure of data, identify outliers, and prepare data for modeling. This course provides a comprehensive overview of EDA techniques, which can help Data Scientists improve the quality and accuracy of their models.
Business Analyst
Business Analyst
A Business Analyst is responsible for understanding business requirements and identifying opportunities for improvement. Exploratory Data Analysis (EDA) with Python and Pandas can be a valuable tool for Business Analysts, as it allows them to quickly analyze data, identify trends and patterns, and make recommendations for improving business processes. This course provides a practical introduction to EDA techniques, which can help Business Analysts become more effective in their work.
Data Engineer
Data Engineer
A Data Engineer is responsible for designing, building, and maintaining data pipelines and infrastructure. Exploratory Data Analysis (EDA) with Python and Pandas is a useful skill for this role, as it allows Data Engineers to understand the structure of data, identify data quality issues, and develop data processing pipelines. This course provides a foundation in EDA techniques, which can help Data Engineers improve the efficiency and reliability of their data pipelines.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and analytical techniques to solve business problems. Exploratory Data Analysis (EDA) with Python and Pandas can be a valuable tool for this role, as it allows Operations Research Analysts to quickly analyze data, identify patterns and trends, and develop models to optimize business processes. This course provides an introduction to EDA techniques, which can help Operations Research Analysts become more effective in their work.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze financial data. Exploratory Data Analysis (EDA) with Python and Pandas can be a valuable tool for this role, as it allows Quantitative Analysts to quickly explore and summarize large datasets, identify trends and patterns, and make informed investment decisions. This course provides a solid foundation in EDA techniques, which can help Quantitative Analysts become more efficient and effective in their work.
Market Researcher
Market Researcher
A Market Researcher is responsible for conducting research to understand market trends and consumer behavior. Exploratory Data Analysis (EDA) with Python and Pandas can be a valuable tool for this role, as it allows Market Researchers to quickly analyze data, identify trends and patterns, and make recommendations for marketing campaigns. This course provides a practical introduction to EDA techniques, which can help Market Researchers become more effective in their work.
Actuary
Actuary
An Actuary is responsible for assessing and managing financial risks. Exploratory Data Analysis (EDA) with Python and Pandas can be a valuable tool for this role, as it allows Actuaries to quickly analyze data, identify patterns and trends, and develop models to assess financial risks. This course provides a foundation in EDA techniques, which can help Actuaries become more efficient and effective in their work.
Statistician
Statistician
A Statistician is responsible for collecting, analyzing, and interpreting data. Exploratory Data Analysis (EDA) with Python and Pandas is a fundamental skill for this role, as it allows Statisticians to quickly explore and summarize data, identify trends and patterns, and make inferences about the population. This course provides a comprehensive overview of EDA techniques, which can help Statisticians become more efficient and effective in their work.
Financial Analyst
Financial Analyst
A Financial Analyst is responsible for analyzing financial data and making investment recommendations. Exploratory Data Analysis (EDA) with Python and Pandas can be a valuable tool for this role, as it allows Financial Analysts to quickly explore and summarize financial data, identify trends and patterns, and make informed investment decisions. This course provides a solid foundation in EDA techniques, which can help Financial Analysts become more efficient and effective in their work.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer is responsible for designing, building, and deploying machine learning models. Exploratory Data Analysis (EDA) with Python and Pandas can be a valuable tool for this role, as it allows Machine Learning Engineers to understand the structure of data, identify data quality issues, and prepare data for modeling. This course provides a foundation in EDA techniques, which can help Machine Learning Engineers improve the efficiency and accuracy of their models.
Software Engineer
Software Engineer
A Software Engineer is responsible for designing, developing, and maintaining software applications. Exploratory Data Analysis (EDA) with Python and Pandas can be a valuable tool for this role, as it allows Software Engineers to understand the structure of data, identify data quality issues, and develop data-driven applications. This course provides a foundation in EDA techniques, which can help Software Engineers improve the efficiency and quality of their software applications.
Data Visualization Analyst
Data Visualization Analyst
A Data Visualization Analyst is responsible for creating visualizations to communicate data insights. Exploratory Data Analysis (EDA) with Python and Pandas can be a valuable tool for this role, as it allows Data Visualization Analysts to quickly explore and summarize data, identify trends and patterns, and create effective visualizations. This course provides a practical introduction to EDA techniques, which can help Data Visualization Analysts become more effective in their work.
Business Intelligence Analyst
Business Intelligence Analyst
A Business Intelligence Analyst is responsible for analyzing data to identify trends and patterns that can help businesses make informed decisions. Exploratory Data Analysis (EDA) with Python and Pandas can be a valuable tool for this role, as it allows Business Intelligence Analysts to quickly explore and summarize data, identify trends and patterns, and make recommendations for improving business processes. This course provides a practical introduction to EDA techniques, which can help Business Intelligence Analysts become more effective in their work.
Data Architect
Data Architect
A Data Architect is responsible for designing and managing data systems. Exploratory Data Analysis (EDA) with Python and Pandas can be a valuable tool for this role, as it allows Data Architects to understand the structure of data, identify data quality issues, and develop data management strategies. This course provides a foundation in EDA techniques, which can help Data Architects improve the efficiency and reliability of their data systems.
For more career information including salaries, visit:
OpenCourser.com/course/4s3knu/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 Python and Pandas.
Provides a comprehensive overview of Python for data analysis, covering topics such as data manipulation, data exploration, and data visualization. It valuable resource for learners who are new to Python or who want to improve their data analysis skills.
Save
Provides a comprehensive overview of exploratory data analysis with R. It covers topics such as data visualization, data transformation, and statistical modeling. It valuable resource for learners who want to learn how to use R for data analysis.
Save
Provides a practical introduction to data visualization. It covers topics such as choosing the right charts and graphs, designing effective visualizations, and communicating data insights. It good choice for learners who want to learn how to create effective data visualizations.
Save
Provides a thought-provoking look at the challenges and opportunities of data science. It covers topics such as the ethics of data science, the role of data in decision-making, and the future of data science. It valuable resource for learners who want to understand the broader context of data science.
Save
Provides a gentle introduction to machine learning. It covers topics such as supervised learning, unsupervised learning, and model evaluation. It good choice for learners who want to learn the basics of machine learning.
Save
Provides a comprehensive overview of deep learning. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for learners who want to learn the basics of deep learning.
Provides a comprehensive overview of statistical learning. It covers topics such as linear regression, logistic regression, and decision trees. It valuable resource for learners who want to learn the basics of statistical learning.
Provides a gentle introduction to statistical learning. It covers topics such as supervised learning, unsupervised learning, and model evaluation. It good choice for learners who want to learn the basics of statistical learning without getting bogged down in the mathematics.
Provides a practical introduction to machine learning with Python. It covers topics such as supervised learning, unsupervised learning, and model evaluation. It good choice for learners who want to learn how to use Python for machine learning.
Provides a practical introduction to machine learning with Scikit-Learn, Keras, and TensorFlow. It covers topics such as supervised learning, unsupervised learning, and model evaluation. It good choice for learners who want to learn how to use these popular machine learning libraries.
Provides a practical introduction to machine learning with R. It covers topics such as supervised learning, unsupervised learning, and model evaluation. It good choice for learners who want to learn how to use R for machine learning.
Provides a practical introduction to deep learning with Python. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It good choice for learners who want to learn how to use Python for deep learning.
Provides a practical introduction to TensorFlow for deep learning. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It good choice for learners who want to learn how to use TensorFlow for deep learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/4s3knu/exploratory
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