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Ahmad Varasteh
By the end of this project you will learn how to analyze Hierarchical Data. we are going to work with a dataset related to Mexico toy sales. The dataset contains some hierarchical data about different products sold in different stores in different cities in Mexico. we are going to load this data and after some preprocessing steps, we are going to learn how to analyze this data using different visualization techniques. During this project we are going to learn about a very important concept called Data Granularity. And we will also learn how to use different levels of granularity to answer some analytical question. and at the end we are going to talk about Treemaps and Sunburst Diagram, two handy visualization techniques used for hierarchical data.
Register for this course and see more details by visiting:
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Syllabus
Hierarchical relational data analysis using python
By the end of this project you will learn how to analyze Hierarchical Data. we are going to work with a dataset related to Mexico toy sales. The dataset contains some hierarchical data about different products sold in different stores in different cities in Mexico. we are going to load this data and after some preprocessing steps, we are going to learn how to analyze this data using different visualization techniques. During this project we are going to learn about a very important concept called Data Granularity. And we will also learn how to use different levels of granularity to answer some analytical question. and at the end we are going to talk about Treemaps and Sunburst Diagram, two handy visualization techniques used for hierarchical data.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops skills in analyzing and visualizing hierarchical data
Uses a dataset related to real-world Mexico toy sales for hands-on learning
Emphasizes the importance of data granularity for effective analysis
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activities:
Review basic data structures
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Refresh your memory on the fundamentals of data structures to be better prepared for the course material.
Browse courses on
Data Structures
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Review the concepts of arrays, lists, stacks, and queues
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Implement these data structures in a programming language of your choice
Organize course materials
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Create a system for organizing all the course materials to make subsequent study sessions more efficient.
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Establish a dedicated workspace
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Create folders for different topics and materials
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Regularly sort and update the materials
Explore resources on Hierarchical Data Analysis
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Expand your understanding of how Hierarchical Data Analysis is used by watching online tutorials.
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Search for tutorials on Hierarchical Data Analysis
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Select tutorials that align with your skill level
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Follow along and complete the examples provided in the tutorials
Four other activities
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Practice visualizing Hierarchical Data
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Sharpen your visualization skills by working through practice exercises.
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-
Find online resources or datasets with Hierarchical Data
-
Choose different visualization techniques and apply them to the data
-
Analyze the results and interpret the patterns
Develop a Hierarchical Data Analysis case study
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Demonstrate your understanding by creating a case study that applies Hierarchical Data Analysis techniques.
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Identify a real-world scenario where Hierarchical Data Analysis would be valuable
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Gather data and analyze it using Hierarchical Data Analysis techniques
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Present your findings in a written report or presentation
Volunteer as a tutor for Hierarchical Data Analysis
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Deepen your understanding by helping others grasp Hierarchical Data Analysis concepts.
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Join a tutoring organization or online platform
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Prepare lesson plans and support materials
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Provide guidance and support to students learning Hierarchical Data Analysis
Participate in Kaggle competitions on Hierarchical Data Analysis
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Challenge yourself and test your skills by participating in competitions.
Show steps
-
Identify relevant Kaggle competitions focused on Hierarchical Data Analysis
-
Form a team or work individually to develop solutions
-
Submit your solutions and analyze the results to improve your understanding
Review basic data structures
Show steps
Refresh your memory on the fundamentals of data structures to be better prepared for the course material.
Browse courses on
Data Structures
Show steps
Show steps
Show steps
- Review the concepts of arrays, lists, stacks, and queues
- Implement these data structures in a programming language of your choice
Organize course materials
Show steps
Create a system for organizing all the course materials to make subsequent study sessions more efficient.
Show steps
Show steps
Show steps
- Establish a dedicated workspace
- Create folders for different topics and materials
- Regularly sort and update the materials
Explore resources on Hierarchical Data Analysis
Show steps
Expand your understanding of how Hierarchical Data Analysis is used by watching online tutorials.
Show steps
Show steps
Show steps
- Search for tutorials on Hierarchical Data Analysis
- Select tutorials that align with your skill level
- Follow along and complete the examples provided in the tutorials
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice visualizing Hierarchical Data
Show steps
Sharpen your visualization skills by working through practice exercises.
Show steps
Show steps
Show steps
- Find online resources or datasets with Hierarchical Data
- Choose different visualization techniques and apply them to the data
- Analyze the results and interpret the patterns
Develop a Hierarchical Data Analysis case study
Show steps
Demonstrate your understanding by creating a case study that applies Hierarchical Data Analysis techniques.
Show steps
Show steps
Show steps
- Identify a real-world scenario where Hierarchical Data Analysis would be valuable
- Gather data and analyze it using Hierarchical Data Analysis techniques
- Present your findings in a written report or presentation
Volunteer as a tutor for Hierarchical Data Analysis
Show steps
Deepen your understanding by helping others grasp Hierarchical Data Analysis concepts.
Show steps
Show steps
Show steps
- Join a tutoring organization or online platform
- Prepare lesson plans and support materials
- Provide guidance and support to students learning Hierarchical Data Analysis
Participate in Kaggle competitions on Hierarchical Data Analysis
Show steps
Challenge yourself and test your skills by participating in competitions.
Show steps
Show steps
Show steps
- Identify relevant Kaggle competitions focused on Hierarchical Data Analysis
- Form a team or work individually to develop solutions
- Submit your solutions and analyze the results to improve your understanding
Career center
Learners who complete Hierarchical relational data analysis using python will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use data to solve complex problems and make predictions. This course can help you develop the skills needed to be a successful Data Scientist, including data analysis, machine learning, and artificial intelligence. The course also provides an overview of hierarchical data analysis, which is a valuable skill for Data Scientists who work with data that has a hierarchical structure, such as social network data or customer data.
Business Analyst
Business Analyst
Business Analysts help organizations improve their performance by identifying and solving business problems. This course can help you develop the skills needed to be a successful Business Analyst, including data analysis, process improvement, and project management. The course also provides an overview of hierarchical data analysis, which is a valuable skill for Business Analysts who work with data that has a hierarchical structure, such as financial data or operational data.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to help businesses and organizations make informed decisions. This course can help you develop the skills needed to be a successful Statistician, including data analysis, statistical modeling, and probability theory. The course also provides an overview of hierarchical data analysis, which is a valuable skill for Statisticians who work with data that has a hierarchical structure, such as medical data or financial data.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course can help you develop the skills needed to be a successful Software Engineer, including data structures, algorithms, and software design. The course also provides an overview of hierarchical data analysis, which is a valuable skill for Software Engineers who work with software applications that have a hierarchical structure, such as operating systems or enterprise software.
Data Analyst
Data Analyst
Data Analysts help organizations make data-driven decisions by collecting, cleaning, and analyzing data. This course can help you develop the skills needed to be a successful Data Analyst, including data visualization, data mining, and statistical modeling. The course also provides an overview of hierarchical data analysis, which is a valuable skill for Data Analysts who work with data that has a hierarchical structure, such as sales data or customer data.
Database Administrator
Database Administrator
Database Administrators design, implement, and maintain databases. This course can help you develop the skills needed to be a successful Database Administrator, including data modeling, database design, and database optimization. The course also provides an overview of hierarchical data analysis, which is a valuable skill for Database Administrators who work with databases that have a hierarchical structure, such as relational databases or object-oriented databases.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to make investment decisions and provide financial advice. This course can help you develop the skills needed to be a successful Financial Analyst, including data analysis, financial modeling, and investment analysis. The course also provides an overview of hierarchical data analysis, which is a valuable skill for Financial Analysts who work with financial data that has a hierarchical structure, such as stock market data or bond market data.
Risk Analyst
Risk Analyst
Risk Analysts identify, assess, and manage risks for businesses and organizations. This course can help you develop the skills needed to be a successful Risk Analyst, including data analysis, risk assessment, and risk management. The course also provides an overview of hierarchical data analysis, which is a valuable skill for Risk Analysts who work with data that has a hierarchical structure, such as operational data or financial data.
Product Manager
Product Manager
Product Managers lead the development and launch of new products and services. This course can help you develop the skills needed to be a successful Product Manager, including market research, product development, and project management. The course also provides an overview of hierarchical data analysis, which is a valuable skill for Product Managers who work with data that has a hierarchical structure, such as customer data or sales data.
Data Management Consultant
Data Management Consultant
Data Management Consultants help businesses and organizations manage their data. This course can help you develop the skills needed to be a successful Data Management Consultant, including data architecture, data governance, and data quality management. The course also provides an overview of hierarchical data analysis, which is a valuable skill for Data Management Consultants who work with data that has a hierarchical structure, such as customer data or financial data.
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists design and create visual representations of data. This course can help you develop the skills needed to be a successful Data Visualization Specialist, including data visualization techniques, data storytelling, and user experience design. The course also provides an overview of hierarchical data analysis, which is a valuable skill for Data Visualization Specialists who work with data that has a hierarchical structure, such as sales data or customer data.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course can help you develop the skills needed to be a successful Quantitative Analyst, including data analysis, financial modeling, and risk management. The course also provides an overview of hierarchical data analysis, which is a valuable skill for Quantitative Analysts who work with financial data that has a hierarchical structure, such as stock market data or bond market data.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risks and make financial decisions. This course can help you develop the skills needed to be a successful Actuary, including data analysis, financial modeling, and risk management. The course also provides an overview of hierarchical data analysis, which is a valuable skill for Actuaries who work with data that has a hierarchical structure, such as insurance data or pension data.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to improve business performance. This course can help you develop the skills needed to be a successful Business Intelligence Analyst, including data analysis, data visualization, and business intelligence tools. The course also provides an overview of hierarchical data analysis, which is a valuable skill for Business Intelligence Analysts who work with data that has a hierarchical structure, such as sales data or customer data.
Market Research Analyst
Market Research Analyst
Market Research Analysts collect and analyze data to help businesses understand their customers and make informed decisions. This course can help you develop the skills needed to be a successful Market Research Analyst, including data visualization, data analysis, and customer segmentation. The course also provides an overview of hierarchical data analysis, which is a valuable skill for Market Research Analysts who work with data that has a hierarchical structure, such as sales data or customer data.
For more career information including salaries, visit:
OpenCourser.com/course/bzxw6n/hierarchical
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
Hierarchical relational data analysis using python.
Provides a comprehensive overview of pattern recognition and machine learning, covering a wide range of topics including supervised learning, unsupervised learning, and Bayesian inference. It valuable resource for learners who want to understand the theoretical foundations of machine learning.
Classic in the field of information visualization and provides a deep dive into the cognitive and perceptual aspects of data visualization. It valuable resource for learners who want to understand the theoretical foundations of data visualization and how to design effective visualizations.
Provides a comprehensive overview of statistical learning, covering a wide range of topics including linear regression, logistic regression, and support vector machines. It valuable resource for learners who want to understand the theoretical foundations of machine learning.
Focuses on the practical aspects of creating interactive data visualizations for the web using JavaScript and D3.js. It good choice for learners who want to learn how to build interactive visualizations and who have some experience with web development.
Provides a comprehensive overview of natural language processing using Python, covering a wide range of topics including text preprocessing, text classification, and sentiment analysis. It valuable resource for learners who want to learn how to use Python for natural language processing.
Provides a practical introduction to deep learning using Python and Keras, a high-level neural networks API. It good choice for learners who want to learn how to build and train deep learning models.
Provides a comprehensive overview of data science using Python, covering a wide range of topics including data manipulation, machine learning, and data visualization. It valuable resource for learners who want to learn how to use Python for data analysis and visualization.
Provides a practical introduction to data analysis using Pandas, a popular Python library for data manipulation and analysis. It good choice for learners who want to learn how to use Pandas for data cleaning, data exploration, and data visualization.
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Provides a comprehensive introduction to data visualization, covering a wide range of topics relevant to the course, including data exploration, visual encoding, and interactive visualization. It is particularly useful for learners who are new to data visualization or who want to refresh their understanding of the basics.
Provides a hands-on introduction to data science, covering a wide range of topics including data cleaning, data analysis, and machine learning. It good choice for learners who are new to data science or who want to learn how to build machine learning models from scratch.
Provides a practical introduction to machine learning using R, a popular statistical programming language. It good choice for learners who are familiar with R and who want to learn how to use it for machine learning.
Provides a gentle introduction to machine learning using Python, covering a range of topics including supervised learning, unsupervised learning, and model evaluation. It good choice for learners who are new to machine learning or who want to learn how to use Python for machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/bzxw6n/hierarchical
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Ahmad Varasteh
Language
English
Good to know
Develops skills in analyzing and visualizing hierarchical data
Uses a dataset related to real-world Mexico toy sales for hands-on learning
Emphasizes the importance of data granularity for effective analysis
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