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Anton Dolganov
The project is intended for students of various fields who are faced with the tasks of data processing and analysis. The project introduces the basic functionality of the Pandas library for loading, analyzing and processing a dataset. The project also introduces the listener to data visualization methods that will help to visualize data and allow formulating basic hypotheses about the data.
Register for this course and see more details by visiting:
OpenCourser.com/course/znz34c/data
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces Python library for data processing and analysis
Covers basics of data visualization for effective data representation
Suitable for learners with diverse backgrounds
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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 Data Presentation with Pandas with these
activities:
Review foundational Python concepts
Show steps
Ensure a strong understanding of Python fundamentals, which are essential for effectively utilizing Pandas.
Browse courses on
Python
Show steps
-
Review online tutorials or documentation covering Python data types, variables, and control flow.
-
Complete practice exercises or coding challenges to test your understanding.
Review 'Python Data Science Handbook'
Show steps
Gain a solid foundation in data science concepts and techniques used in this course, including data manipulation and visualization with Pandas.
View
Python Data Science Handbook: Essential Tools...
on Amazon
Show steps
-
Read the selected chapters covering topics relevant to this course, such as data cleaning, exploration, and visualization.
-
Take notes and highlight key concepts.
Practice data manipulation and analysis with Pandas tutorials
Show steps
Practice using Pandas commands and functions by following guided tutorials and completing exercises.
Browse courses on
Pandas
Show steps
-
Locate online Pandas tutorials tailored to beginners.
-
Work through the tutorials, practicing loading data, manipulating dataframes, and performing basic analysis.
Five other activities
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Show all eight activities
Join a study group to discuss course concepts
Show steps
Engage with peers to clarify concepts, share insights, and support each other's learning journey.
Show steps
-
Find or create a study group with fellow course participants.
-
Establish regular meeting times and a structured format for discussions.
-
Take turns presenting and discussing course material, sharing perspectives and asking questions.
Explore advanced Pandas features and techniques
Show steps
Expand your knowledge of Pandas by exploring advanced features and techniques, enabling you to handle larger datasets and complex data transformations.
Browse courses on
Pandas
Show steps
-
Locate online tutorials or documentation on advanced Pandas topics, such as data merging, grouping, and reshaping.
-
Follow the tutorials and practice implementing the new techniques.
Create a visualization dashboard to explore data
Show steps
Develop proficiency in data visualization and storytelling by creating an interactive dashboard to explore and present data insights.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset to work with and identify key variables for visualization.
-
Use Pandas to manipulate and prepare the data for visualization.
-
Select appropriate visualization charts and graphs to display the data effectively.
-
Create an interactive dashboard using a tool like Tableau or Power BI.
-
Present the dashboard, explaining the data insights and providing context.
Build a data analysis project using Pandas
Show steps
Demonstrate your understanding of Pandas and data analysis by completing a project that involves data cleaning, manipulation, and visualization.
Browse courses on
Data Analysis
Show steps
-
Identify a real-world dataset that aligns with your interests.
-
Load and clean the data using Pandas.
-
Perform exploratory data analysis and identify patterns and insights.
-
Visualize the data using appropriate charts and graphs.
-
Write a report summarizing your findings and presenting your recommendations.
Organize course notes and materials
Show steps
Enhance your learning and retention by organizing and reviewing your course notes, assignments, and other materials.
Show steps
-
Review all course materials, including notes, slides, and assignments.
-
Create a structured system for organizing the materials, such as folders or digital notebooks.
-
Regularly review and revisit the organized materials to reinforce your understanding.
Review foundational Python concepts
Show steps
Ensure a strong understanding of Python fundamentals, which are essential for effectively utilizing Pandas.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review online tutorials or documentation covering Python data types, variables, and control flow.
- Complete practice exercises or coding challenges to test your understanding.
Review 'Python Data Science Handbook'
Show steps
Gain a solid foundation in data science concepts and techniques used in this course, including data manipulation and visualization with Pandas.
View
Python Data Science Handbook: Essential Tools...
on Amazon
Show steps
Show steps
Show steps
- Read the selected chapters covering topics relevant to this course, such as data cleaning, exploration, and visualization.
- Take notes and highlight key concepts.
Practice data manipulation and analysis with Pandas tutorials
Show steps
Practice using Pandas commands and functions by following guided tutorials and completing exercises.
Browse courses on
Pandas
Show steps
Show steps
Show steps
- Locate online Pandas tutorials tailored to beginners.
- Work through the tutorials, practicing loading data, manipulating dataframes, and performing basic analysis.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a study group to discuss course concepts
Show steps
Engage with peers to clarify concepts, share insights, and support each other's learning journey.
Show steps
Show steps
Show steps
- Find or create a study group with fellow course participants.
- Establish regular meeting times and a structured format for discussions.
- Take turns presenting and discussing course material, sharing perspectives and asking questions.
Explore advanced Pandas features and techniques
Show steps
Expand your knowledge of Pandas by exploring advanced features and techniques, enabling you to handle larger datasets and complex data transformations.
Browse courses on
Pandas
Show steps
Show steps
Show steps
- Locate online tutorials or documentation on advanced Pandas topics, such as data merging, grouping, and reshaping.
- Follow the tutorials and practice implementing the new techniques.
Create a visualization dashboard to explore data
Show steps
Develop proficiency in data visualization and storytelling by creating an interactive dashboard to explore and present data insights.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset to work with and identify key variables for visualization.
- Use Pandas to manipulate and prepare the data for visualization.
- Select appropriate visualization charts and graphs to display the data effectively.
- Create an interactive dashboard using a tool like Tableau or Power BI.
- Present the dashboard, explaining the data insights and providing context.
Build a data analysis project using Pandas
Show steps
Demonstrate your understanding of Pandas and data analysis by completing a project that involves data cleaning, manipulation, and visualization.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Identify a real-world dataset that aligns with your interests.
- Load and clean the data using Pandas.
- Perform exploratory data analysis and identify patterns and insights.
- Visualize the data using appropriate charts and graphs.
- Write a report summarizing your findings and presenting your recommendations.
Organize course notes and materials
Show steps
Enhance your learning and retention by organizing and reviewing your course notes, assignments, and other materials.
Show steps
Show steps
Show steps
- Review all course materials, including notes, slides, and assignments.
- Create a structured system for organizing the materials, such as folders or digital notebooks.
- Regularly review and revisit the organized materials to reinforce your understanding.
Career center
Learners who complete Data Presentation with Pandas will develop knowledge and skills
that may be useful to these careers:
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists create visual representations of data to help people understand and communicate data. This course will help build a foundation for your career as a Data Visualization Specialist by introducing you to the Pandas library, a powerful tool for data manipulation and analysis. You will learn how to load, clean, and analyze data, as well as how to visualize data to help you communicate your findings effectively.
Data Analyst
Data Analyst
Data Analysts apply analytical and technical skills to solve business problems. You will help identify data sources, gather and clean data, and identify meaningful patterns and trends to derive insights. This course will help build a foundation for your career as a Data Analyst by introducing you to the Pandas library, a powerful tool for data manipulation and analysis. You will learn how to load, clean, and analyze data, as well as how to visualize data to help you communicate your findings effectively.
Data Scientist
Data Scientist
Data Scientists use scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured. This course will help build a foundation for your career as a Data Scientist by introducing you to the Pandas library, a powerful tool for data manipulation and analysis. You will learn how to load, clean, and analyze data, as well as how to visualize data to help you communicate your findings effectively.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex problems in a variety of industries. This course may be useful for Operations Research Analysts as it introduces the Pandas library, a tool for data analysis and manipulation. Learning how to use Pandas can help Operations Research Analysts better understand data and make more informed decisions.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. This course may be useful for Statisticians as it introduces the Pandas library, a tool for data analysis and manipulation. Learning how to use Pandas can help Statisticians better understand data and make more informed decisions about data analysis and interpretation.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course may be useful for Software Engineers as it introduces the Pandas library, a tool for data analysis and manipulation. Learning how to use Pandas can help Software Engineers better understand data and make more informed decisions about software design and development.
Financial Analyst
Financial Analyst
Financial Analysts provide advice and guidance to individuals and organizations on financial matters. This course may be useful for Financial Analysts as it introduces the Pandas library, a tool for data analysis and manipulation. Learning how to use Pandas can help Financial Analysts better understand data and make more informed decisions.
Product Manager
Product Manager
Product Managers lead the development and launch of new products and services. This course may be useful for Product Managers as it introduces the Pandas library, a tool for data analysis and manipulation. Learning how to use Pandas can help Product Managers better understand data and make more informed decisions about product development and launch.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines and infrastructure. This course may be useful for Data Engineers as it introduces the Pandas library, a tool for data analysis and manipulation. Learning how to use Pandas can help Data Engineers better understand data and make more informed decisions about data pipeline design and maintenance.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze data and make predictions. This course may be useful for Quantitative Analysts as it introduces the Pandas library, a tool for data analysis and manipulation. Learning how to use Pandas can help Quantitative Analysts better understand data and make more informed decisions.
Market Researcher
Market Researcher
Market Researchers conduct surveys, collect data, and analyze market trends to help businesses make informed decisions. This course may be useful for Market Researchers as it introduces the Pandas library, a tool for data analysis and manipulation. Learning how to use Pandas can help Market Researchers better understand data and make more informed decisions.
Information Architect
Information Architect
Information Architects design and organize information systems to make them easy to find and use. This course may be useful for Information Architects as it introduces the Pandas library, a tool for data analysis and manipulation. Learning how to use Pandas can help Information Architects better understand data and make more informed decisions about information system design and organization.
User Experience Researcher
User Experience Researcher
User Experience Researchers conduct research to understand how users interact with products and services. This course may be useful for User Experience Researchers as it introduces the Pandas library, a tool for data analysis and manipulation. Learning how to use Pandas can help User Experience Researchers better understand data and make more informed decisions about product and service design.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning models. This course may be useful for Machine Learning Engineers as it introduces the Pandas library, a tool for data analysis and manipulation. Learning how to use Pandas can help Machine Learning Engineers better understand data and make more informed decisions about machine learning model design and maintenance.
Business Analyst
Business Analyst
Business Analysts bridge the gap between business and IT by understanding the business needs and translating them into technical requirements. This course may be useful for Business Analysts as it introduces the Pandas library, a tool for data analysis and manipulation. Learning how to use Pandas can help Business Analysts better understand data and make more informed decisions.
For more career information including salaries, visit:
OpenCourser.com/course/znz34c/data
Reading list
We've selected 12 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
Data Presentation with Pandas.
Provides a comprehensive introduction to using Python for data analysis, from data acquisition and cleaning to data manipulation and visualization. It valuable resource for anyone who wants to learn how to use Python for data analysis.
Hands-on guide to using Pandas for data analysis. It covers a wide range of topics, from data cleaning and transformation to data analysis and visualization. It valuable resource for anyone who wants to learn how to use Pandas for data analysis.
Comprehensive introduction to machine learning using Python. It covers a wide range of topics, from supervised learning to unsupervised learning. It valuable resource for anyone who wants to learn how to use machine learning for data analysis.
Comprehensive introduction to deep learning using Python. It covers a wide range of topics, from convolutional neural networks to recurrent neural networks. It valuable resource for anyone who wants to learn how to use deep learning for data analysis.
Comprehensive introduction to natural language processing using Python. It covers a wide range of topics, from text classification to text generation. It valuable resource for anyone who wants to learn how to use natural language processing for data analysis.
Save
Comprehensive introduction to probability and statistics using R. It covers a wide range of topics, from probability theory to statistical inference. It valuable resource for anyone who wants to learn how to use probability and statistics for data analysis.
Comprehensive introduction to Bayesian data analysis. It covers a wide range of topics, from Bayesian inference to Bayesian modeling. It valuable resource for anyone who wants to learn how to use Bayesian data analysis for data analysis.
Comprehensive introduction to causal inference in statistics. It covers a wide range of topics, from the foundations of causal inference to the application of causal inference to real-world problems. It valuable resource for anyone who wants to learn how to use causal inference for data analysis.
Save
Classic introduction to data visualization. It covers a wide range of topics, from the principles of data visualization to the application of data visualization to real-world problems. It valuable resource for anyone who wants to learn how to use data visualization to analyze data.
Save
Practical introduction to data visualization. It covers a wide range of topics, from the principles of data visualization to the application of data visualization to real-world problems. It valuable resource for anyone who wants to learn how to use data visualization to analyze data.
Comprehensive introduction to information visualization. It covers a wide range of topics, from the principles of information visualization to the application of information visualization to real-world problems. It valuable resource for anyone who wants to learn how to use information visualization to analyze data.
Save
Practical introduction to data visualization. It covers a wide range of topics, from the principles of data visualization to the application of data visualization to real-world problems. It valuable resource for anyone who wants to learn how to use data visualization to analyze data.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/znz34c/data
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Time
3 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Anton Dolganov
Good to know
Introduces Python library for data processing and analysis
Covers basics of data visualization for effective data representation
Suitable for learners with diverse backgrounds
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