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Ross Maciejewsk
and
K. Selcuk Candan
—Data repositories in which cases are related to subcases are identified as hierarchical. This course covers the representation schemes of hierarchies and algorithms that enable analysis of hierarchical data, as well as provides opportunities to apply several methods of analysis.
Register for this course and see more details by visiting:
OpenCourser.com/course/3w5v24/temporal
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Excavates models of hierarchy and their analysis in computer science
Taught by respected instructors: Ross Maciejewsk and K. Selcuk Candan
Might not appeal to those without an interest in the representation and analysis of hierarchical data
Requires some computer science background
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Save Temporal and Hierarchical Data Analysis to your list so you can find it easily later:
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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 Temporal and Hierarchical Data Analysis with these
activities:
Organize and review course materials
Show steps
Enhance retention by actively organizing and reviewing materials.
Show steps
-
Create a central repository for all course materials
-
Review and summarize key concepts regularly
-
Annotate and highlight important sections
Review Exploratory Data Analysis
Show steps
Start the journey on the right foot by ensuring foundational skills are up to speed.
Browse courses on
Exploratory Data Analysis
Show steps
-
Review core statistical concepts
-
Brush up on basic programming and data manipulation
Read "Data Science for Business"
Show steps
Read an authoritative text to augment and consolidate course materials.
View
Data Science para Negócios
on Amazon
Show steps
-
Read chapters 1-4 to gain an overview of data science for business
-
Complete the end-of-chapter exercises to test your understanding
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow online tutorials on hierarchical data analysis
Show steps
Expand knowledge by seeking additional resources and guidance.
Show steps
-
Identify reputable online platforms and tutorials
-
Follow tutorials on specific topics of interest
-
Complete exercises and assignments to reinforce learning
Practice with Kaggle datasets
Show steps
Solidify understanding by working through real-world datasets.
Show steps
-
Select a dataset aligned with course topics
-
Explore and analyze the dataset using appropriate techniques
-
Write a report summarizing your findings
Develop a hierarchical data analysis model
Show steps
Apply course concepts to a practical project.
Show steps
-
Identify a suitable dataset
-
Design and implement a hierarchical data analysis model
-
Evaluate the performance of the model
-
Document the project and present your findings
Mentor junior data analysts
Show steps
Reinforce learning by sharing knowledge and guiding others.
Show steps
-
Identify opportunities to mentor junior data analysts
-
Provide guidance and support on technical and analytical topics
-
Share your experiences and best practices
Contribute to the hierarchical data analysis community
Show steps
Extend learning beyond the classroom by engaging with the wider community.
Show steps
-
Identify open-source projects related to hierarchical data analysis
-
Contribute to discussions, report bugs, and suggest improvements
Organize and review course materials
Show steps
Enhance retention by actively organizing and reviewing materials.
Show steps
Show steps
Show steps
- Create a central repository for all course materials
- Review and summarize key concepts regularly
- Annotate and highlight important sections
Review Exploratory Data Analysis
Show steps
Start the journey on the right foot by ensuring foundational skills are up to speed.
Browse courses on
Exploratory Data Analysis
Show steps
Show steps
Show steps
- Review core statistical concepts
- Brush up on basic programming and data manipulation
Read "Data Science for Business"
Show steps
Read an authoritative text to augment and consolidate course materials.
View
Data Science para Negócios
on Amazon
Show steps
Show steps
Show steps
- Read chapters 1-4 to gain an overview of data science for business
- Complete the end-of-chapter exercises to test your understanding
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow online tutorials on hierarchical data analysis
Show steps
Expand knowledge by seeking additional resources and guidance.
Show steps
Show steps
Show steps
- Identify reputable online platforms and tutorials
- Follow tutorials on specific topics of interest
- Complete exercises and assignments to reinforce learning
Practice with Kaggle datasets
Show steps
Solidify understanding by working through real-world datasets.
Show steps
Show steps
Show steps
- Select a dataset aligned with course topics
- Explore and analyze the dataset using appropriate techniques
- Write a report summarizing your findings
Develop a hierarchical data analysis model
Show steps
Apply course concepts to a practical project.
Show steps
Show steps
Show steps
- Identify a suitable dataset
- Design and implement a hierarchical data analysis model
- Evaluate the performance of the model
- Document the project and present your findings
Mentor junior data analysts
Show steps
Reinforce learning by sharing knowledge and guiding others.
Show steps
Show steps
Show steps
- Identify opportunities to mentor junior data analysts
- Provide guidance and support on technical and analytical topics
- Share your experiences and best practices
Contribute to the hierarchical data analysis community
Show steps
Extend learning beyond the classroom by engaging with the wider community.
Show steps
Show steps
Show steps
- Identify open-source projects related to hierarchical data analysis
- Contribute to discussions, report bugs, and suggest improvements
Career center
Learners who complete Temporal and Hierarchical Data Analysis will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts often find employment with companies of various sizes to organize and analyze data. They will prepare reports, create data visualizations, and present these findings to help companies make informed decisions. Temporal and Hierarchical Data Analysis will help you build a foundation in understanding how to analyze hierarchical data structures. This can help you understand how data is connected within a hierarchical organization.
Data Scientist
Data Scientist
Data Scientists apply scientific methods and processes to extract knowledge from data. They will often build models using complex statistical computations based on data analysis, and they will develop techniques for visualizing complex data. Because Temporal and Hierarchical Data Analysis will help you build a foundation in understanding how to analyze hierarchical data structures, you will be able to perform more in-depth data analysis and build more effective models as a Data Scientist.
Statistician
Statistician
Statisticians use mathematical and statistical methods to collect, analyze, interpret, and present data. They apply their knowledge to a wide range of fields, including business, government, and healthcare. By studying Temporal and Hierarchical Data Analysis, you will build a strong foundation in statistical methods. This will help you succeed as a Statistician, particularly when analyzing large, complex datasets with hierarchical structures.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex business problems. They develop and implement models to improve efficiency, productivity, and profitability. Temporal and Hierarchical Data Analysis can help you develop the skills needed to analyze complex data and develop effective solutions as an Operations Research Analyst.
Market Researcher
Market Researcher
Market Researchers study market conditions, consumer behavior, and industry trends. They collect, analyze, and interpret data to help businesses make informed decisions about product development, marketing campaigns, and customer service. Temporal and Hierarchical Data Analysis can help you understand how to analyze market data, including data related to customer demographics, buying behavior, and market trends, which can help you excel as a Market Researcher.
Business Analyst
Business Analyst
Business Analysts use data analysis to understand business needs and identify opportunities for improvement. They work with stakeholders to define requirements, analyze data, and develop solutions. Temporal and Hierarchical Data Analysis can help you understand how to analyze business data, including data related to sales, marketing, and operations. This can improve your ability to identify trends, solve problems, and make recommendations as a Business Analyst.
Actuary
Actuary
Actuaries use mathematical and statistical methods to assess risk and uncertainty. They work in a variety of industries, including insurance, finance, and healthcare. Temporal and Hierarchical Data Analysis can help you develop the skills needed to analyze risk and uncertainty, and make sound decisions as an Actuary.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to evaluate investment opportunities, make recommendations, and manage portfolios. They analyze financial statements, conduct research, and develop financial models. Temporal and Hierarchical Data Analysis can help you develop the skills needed to analyze financial data, identify trends, and make sound investment decisions as a Financial Analyst.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical methods to analyze financial data and make investment decisions. They work in investment banks, hedge funds, and other financial institutions. Temporal and Hierarchical Data Analysis may help you understand how to analyze financial data and make sound investment decisions as a Quantitative Analyst.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. They ensure that data is stored, organized, and accessible. Temporal and Hierarchical Data Analysis may help you understand how to manage and maintain databases that store hierarchical data structures.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work in a variety of industries, including technology, finance, and healthcare. Temporal and Hierarchical Data Analysis may help you understand how to design and develop software applications that can handle hierarchical data structures.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines and systems. They work with data scientists, analysts, and other stakeholders to ensure that data is available, reliable, and secure. Temporal and Hierarchical Data Analysis may help you understand how to design and build data pipelines and systems that can handle hierarchical data structures.
Information Security Analyst
Information Security Analyst
Information Security Analysts protect computer systems and networks from unauthorized access, use, disclosure, disruption, modification, or destruction. Temporal and Hierarchical Data Analysis may help you understand how to protect computer systems and networks that store hierarchical data structures.
Data Architect
Data Architect
Data Architects design and manage data systems. They work with stakeholders to define data requirements, develop data models, and implement data solutions. Temporal and Hierarchical Data Analysis may help you understand how to design and manage data systems that can handle hierarchical data structures.
Computer Network Architect
Computer Network Architect
Computer Network Architects design, build, and maintain computer networks. They ensure that networks are reliable, secure, and efficient. Temporal and Hierarchical Data Analysis may help you understand how to design and build computer networks that can handle hierarchical data structures.
For more career information including salaries, visit:
OpenCourser.com/course/3w5v24/temporal
Reading list
We've selected ten 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
Temporal and Hierarchical Data Analysis.
Save
This comprehensive textbook covers data mining fundamentals, techniques, and applications, providing a strong foundation in the field related to this course.
This widely used textbook provides a thorough introduction to statistical learning methods, including those applicable to hierarchical data, offering a comprehensive understanding of the underlying principles.
Save
This advanced book explores sophisticated data mining techniques, including methods for hierarchical data analysis, providing in-depth knowledge for those interested in further study.
While this book primarily focuses on network data analysis, it also covers hierarchical data analysis methods, providing additional insights and techniques that can complement the course content.
Save
This comprehensive book covers fundamental concepts in pattern recognition and machine learning, providing a broader perspective on techniques that can be applied to hierarchical data analysis.
Specializes in Bayesian hierarchical models, which are often used in hierarchical data analysis, offering a comprehensive understanding of their theory and applications.
This introductory textbook covers data analysis techniques commonly used in social sciences, including hierarchical data analysis, offering a foundation for understanding its concepts.
While this book focuses on temporal data analysis, it also covers methods applicable to hierarchical data, providing insights into analyzing data with both temporal and hierarchical structures.
Provides a comprehensive overview of data structures and algorithms, including those used in hierarchical data analysis, offering a strong foundation for understanding the implementation of these techniques.
This specialized book focuses on hierarchical linear modeling, a technique commonly used in educational research, providing a practical understanding of its application in this specific domain.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/3w5v24/temporal
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Time
30 hours
Via
Coursera
Institution
Arizona State University
Instructors
Ross Maciejewsk
K. Selcuk Candan
This course belongs to a
collection
called
Data Visualization. It's comprised of four courses:
- Multivariate and Geographical Data Analysis
- Introduction to Data Exploration and Visualization
- Additional Tools Used for Data Visualization
- Temporal and Hierarchical Data Analysis (viewing)
Good to know
Excavates models of hierarchy and their analysis in computer science
Taught by respected instructors: Ross Maciejewsk and K. Selcuk Candan
Might not appeal to those without an interest in the representation and analysis of hierarchical data
Requires some computer science background
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