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João Vitor Boverio da Silva Gomes
Conduct exploratory data analysis with a systematic approach to investigate different aspects of your data: comparisons, relationships, compositions, and distributions. This guided project gives you a framework so you can conduct your own exploratory data analysis and make your work more professional and organized. The language is Python and the libraries used are seaborn, pandas, and matplotlib.
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What's inside
Syllabus
Project Overview
Conduct exploratory data analysis with a systematic approach to investigate different aspects of your data: comparisons, relationships, compositions, and distributions. This guided project gives you a framework so you can conduct your own exploratory data analysis and make your work more professional and organized. The language is Python and the libraries used are seaborn, pandas, and matplotlib.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Delves into essential data analysis concepts, making it suitable for beginners
Utilizes Python, a widely used language in data analysis
Emphasizes hands-on practice and guided projects, providing practical experience
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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 Conducting Exploratory Data Analysis with these
activities:
Review Python Syntax
Show steps
Revisit the fundamentals of Python programming to strengthen your understanding of its syntax and structures, ensuring a solid foundation for the course content.
Browse courses on
Python Basics
Show steps
-
Go through online tutorials or documentation to recap Python basics.
-
Practice writing simple Python code snippets.
-
Complete coding exercises or puzzles to reinforce your understanding.
Gather Existing Course Materials
Show steps
Locate and review all course materials, including the syllabus, schedule, and textbooks, to preview the course expectations and key concepts.
Show steps
-
Download and print the syllabus and schedule.
-
Identify the required textbooks and gather them.
-
Create a dedicated folder or workspace for course materials.
Attend Study Sessions
Show steps
Participate in study or discussion groups to connect with peers, exchange knowledge, and reinforce course concepts through collaborative learning.
Show steps
-
Arrange to meet with classmates regularly for study sessions.
-
Review lecture notes and assigned readings together.
-
Discuss and clarify complex concepts.
-
Work on practice problems or assignments collaboratively.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore Data Visualization with Seaborn
Show steps
Complement your understanding of data visualization by following guided tutorials or online courses focused on using Seaborn, a powerful Python library for creating informative and visually appealing data visualizations.
Show steps
-
Locate tutorials or courses that cover Seaborn basics.
-
Follow the instructions and create various data visualizations.
-
Experiment with different Seaborn functions and options.
Solve Data Analysis Problems
Show steps
Engage in targeted practice by solving data analysis problems and exercises related to the course topics, enhancing your problem-solving skills and solidifying your understanding.
Show steps
-
Find online platforms or textbooks with data analysis problems.
-
Attempt to solve the problems using the techniques covered in class.
-
Review your solutions and identify areas for improvement.
Connect with Data Analysis Professionals
Show steps
Seek guidance and insights from experienced data analysts by connecting with professionals in the field, either through LinkedIn, industry events, or online forums.
Show steps
-
Identify professionals in your network or industry.
-
Reach out and express your interest in connecting.
-
Arrange a meeting or video call to discuss your career aspirations and seek advice.
-
Maintain regular contact and seek ongoing mentorship.
Develop a Data Analysis Plan
Show steps
Apply your learning by creating a comprehensive data analysis plan for a real-world dataset, outlining your approach to data exploration, analysis, and visualization.
Show steps
-
Select a dataset and define the research question.
-
Outline your data exploration and analysis strategy.
-
Describe the data visualizations you plan to create.
-
Set a timeline for completing your analysis.
Contribute to Data Analysis Projects
Show steps
Gain practical experience and stay abreast of the latest data science advancements by contributing to open-source data analysis projects on platforms like GitHub.
Show steps
-
Identify open-source projects relevant to your interests.
-
Review the project documentation and codebase.
-
Suggest improvements or contribute code to enhance the project.
-
Collaborate with project maintainers to refine your contributions.
Review Python Syntax
Show steps
Revisit the fundamentals of Python programming to strengthen your understanding of its syntax and structures, ensuring a solid foundation for the course content.
Browse courses on
Python Basics
Show steps
Show steps
Show steps
- Go through online tutorials or documentation to recap Python basics.
- Practice writing simple Python code snippets.
- Complete coding exercises or puzzles to reinforce your understanding.
Gather Existing Course Materials
Show steps
Locate and review all course materials, including the syllabus, schedule, and textbooks, to preview the course expectations and key concepts.
Show steps
Show steps
Show steps
- Download and print the syllabus and schedule.
- Identify the required textbooks and gather them.
- Create a dedicated folder or workspace for course materials.
Attend Study Sessions
Show steps
Participate in study or discussion groups to connect with peers, exchange knowledge, and reinforce course concepts through collaborative learning.
Show steps
Show steps
Show steps
- Arrange to meet with classmates regularly for study sessions.
- Review lecture notes and assigned readings together.
- Discuss and clarify complex concepts.
- Work on practice problems or assignments collaboratively.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore Data Visualization with Seaborn
Show steps
Complement your understanding of data visualization by following guided tutorials or online courses focused on using Seaborn, a powerful Python library for creating informative and visually appealing data visualizations.
Show steps
Show steps
Show steps
- Locate tutorials or courses that cover Seaborn basics.
- Follow the instructions and create various data visualizations.
- Experiment with different Seaborn functions and options.
Solve Data Analysis Problems
Show steps
Engage in targeted practice by solving data analysis problems and exercises related to the course topics, enhancing your problem-solving skills and solidifying your understanding.
Show steps
Show steps
Show steps
- Find online platforms or textbooks with data analysis problems.
- Attempt to solve the problems using the techniques covered in class.
- Review your solutions and identify areas for improvement.
Connect with Data Analysis Professionals
Show steps
Seek guidance and insights from experienced data analysts by connecting with professionals in the field, either through LinkedIn, industry events, or online forums.
Show steps
Show steps
Show steps
- Identify professionals in your network or industry.
- Reach out and express your interest in connecting.
- Arrange a meeting or video call to discuss your career aspirations and seek advice.
- Maintain regular contact and seek ongoing mentorship.
Develop a Data Analysis Plan
Show steps
Apply your learning by creating a comprehensive data analysis plan for a real-world dataset, outlining your approach to data exploration, analysis, and visualization.
Show steps
Show steps
Show steps
- Select a dataset and define the research question.
- Outline your data exploration and analysis strategy.
- Describe the data visualizations you plan to create.
- Set a timeline for completing your analysis.
Contribute to Data Analysis Projects
Show steps
Gain practical experience and stay abreast of the latest data science advancements by contributing to open-source data analysis projects on platforms like GitHub.
Show steps
Show steps
Show steps
- Identify open-source projects relevant to your interests.
- Review the project documentation and codebase.
- Suggest improvements or contribute code to enhance the project.
- Collaborate with project maintainers to refine your contributions.
Career center
Learners who complete Conducting Exploratory Data Analysis will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists leverage data to solve complex business problems. This course aligns well with their role as it provides a structured approach to exploratory data analysis. By mastering the techniques taught in this course, Data Scientists can effectively uncover patterns, identify trends, and gain valuable insights from data. The emphasis on Python, seaborn, pandas, and matplotlib aligns with the tools commonly used in the field, enhancing the fit for this role.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. This course provides a structured approach to exploratory data analysis, which is a crucial step in the statistical process. By mastering the techniques taught in this course, Statisticians can effectively uncover patterns, identify trends, and draw meaningful conclusions from data.
Data Analyst
Data Analyst
Data Analysts play a key role in driving business decisions by collecting, cleaning, and analyzing data. With the knowledge gained from this course, they can develop a systematic approach to exploratory data analysis, which involves investigating different aspects of data such as comparisons, relationships, compositions, and distributions. This course provides a framework that enables Data Analysts to conduct comprehensive and professional data analysis, leveraging Python, seaborn, pandas, and matplotlib libraries.
Healthcare Data Analyst
Healthcare Data Analyst
Healthcare Data Analysts play a crucial role in the healthcare industry by analyzing data to improve patient outcomes and optimize healthcare processes. This course provides a structured approach to exploratory data analysis, enabling them to effectively extract insights from healthcare data. The emphasis on Python, seaborn, pandas, and matplotlib aligns with the tools commonly used in the field, making this course highly relevant for Healthcare Data Analysts.
Market Research Analyst
Market Research Analyst
Market Research Analysts gather and analyze data to understand market trends and consumer behavior. This course aligns well with their role as it provides a systematic approach to exploratory data analysis, allowing them to effectively uncover patterns and insights from market research data. The emphasis on Python, seaborn, pandas, and matplotlib aligns with the tools used in the field, making this course highly relevant.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers play a crucial role in developing and deploying machine learning models. This course provides a solid foundation for exploratory data analysis, a critical step in the machine learning lifecycle. By understanding how to effectively analyze data, Machine Learning Engineers can gain insights into data distributions, identify patterns, and make informed decisions when designing and training machine learning models.
Business Analyst
Business Analyst
Business Analysts leverage data to understand business needs and drive decision-making. This course provides a framework for conducting exploratory data analysis, which is essential for Business Analysts to effectively gather insights from data. The emphasis on Python, seaborn, pandas, and matplotlib aligns with the tools commonly used in the field, making this course particularly relevant.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex business problems. This course aligns well with their role as it provides a structured approach to data analysis, enabling them to gain deeper insights into business data. The emphasis on Python, seaborn, pandas, and matplotlib aligns with the tools used in the field, further enhancing the fit for this role.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to make investment recommendations. This course provides a framework for conducting exploratory data analysis, which is essential for Financial Analysts to effectively gather insights from financial data. The emphasis on Python, seaborn, pandas, and matplotlib aligns with the tools used in the field, making this course particularly relevant.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. This course aligns well with their role as it provides a structured approach to data analysis, enabling them to gain deeper insights into financial data. The emphasis on Python, seaborn, pandas, and matplotlib aligns with the tools used in the field, further enhancing the fit for this role.
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists design and create visual representations of data. This course provides a foundation for understanding data and its characteristics. By gaining insights into exploratory data analysis, Data Visualization Specialists can develop effective and visually appealing data visualizations.
Risk Analyst
Risk Analyst
Risk Analysts identify, assess, and mitigate risks. This course provides a foundation for understanding data and its characteristics. By gaining insights into exploratory data analysis, Risk Analysts can better understand the data they work with and make informed decisions about risk management.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data infrastructure. While this course may not directly align with their core responsibilities, it provides a foundation for understanding data and its characteristics. By gaining insights into exploratory data analysis, Data Engineers can better understand the data they work with and make informed decisions about data management and processing.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. While this course may not directly align with their core responsibilities, it provides a foundation for understanding data and its characteristics. By gaining insights into exploratory data analysis, Software Engineers can develop software that effectively handles and analyzes data.
Product Manager
Product Manager
Product Managers oversee the development and launch of products. While this course may not directly align with their core responsibilities, it provides a foundation for understanding data and its characteristics. By gaining insights into exploratory data analysis, Product Managers can make informed decisions about product design and features based on data-driven insights.
For more career information including salaries, visit:
OpenCourser.com/course/4ajh6s/conducting
Reading list
We've selected eight 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
Conducting Exploratory Data Analysis.
As the creator of the Pandas library, Wes McKinney offers a unique perspective on data analysis in Python. covers data manipulation, cleaning, and visualization, providing a solid foundation for learners.
This comprehensive handbook covers various aspects of data science in Python, including exploratory data analysis, machine learning, and data visualization. It offers a valuable reference for learners looking to broaden their knowledge and skills beyond the scope of the course.
Save
While this book focuses on R rather than Python, it provides a comprehensive guide to exploratory data analysis techniques. Learners can gain valuable insights by applying these concepts to their own Python-based projects.
This classic textbook provides a comprehensive overview of statistical learning methods. While it may not cover Python-specific libraries, it offers a deeper understanding of the statistical principles underlying data analysis techniques.
Effective data visualization is crucial for exploratory data analysis. offers a practical approach to creating clear and compelling visualizations using Python libraries like Seaborn and Matplotlib, complementing the course's focus on data exploration.
Save
Discusses the practical aspects of data science, emphasizing the importance of ethical considerations, communication, and collaboration. It provides insights into the real-world challenges and opportunities of data analysis.
This textbook provides a comprehensive overview of statistical methods commonly used in data analysis. While it may not directly cover the Python libraries used in the course, it offers a solid foundation in statistical concepts, enhancing learners' understanding of the underlying principles.
While machine learning is not the primary focus of this course, this book provides a valuable introduction to the field, covering supervised and unsupervised learning techniques. It complements the course's emphasis on data exploration by demonstrating how to build models based on the insights gained.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/4ajh6s/conducting
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Effort
1.5 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
João Vitor Boverio da Silva Gomes
Language
English
Good to know
Delves into essential data analysis concepts, making it suitable for beginners
Utilizes Python, a widely used language in data analysis
Emphasizes hands-on practice and guided projects, providing practical experience
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