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Chip Wells
This course focuses on data exploration, feature creation, and feature selection for time sequences. The topics discussed include binning, smoothing, transformations, and data set operations for time series, spectral analysis, singular spectrum analysis, distance measures, and motif analysis.
In this course you learn to perform motif analysis and implement analyses in the spectral or frequency domain. You also discover how distance measures work, implement applications, explore signal components, and create time series features.
Read more
This course focuses on data exploration, feature creation, and feature selection for time sequences. The topics discussed include binning, smoothing, transformations, and data set operations for time series, spectral analysis, singular spectrum analysis, distance measures, and motif analysis.
In this course you learn to perform motif analysis and implement analyses in the spectral or frequency domain. You also discover how distance measures work, implement applications, explore signal components, and create time series features.
Read more
This course focuses on data exploration, feature creation, and feature selection for time sequences. The topics discussed include binning, smoothing, transformations, and data set operations for time series, spectral analysis, singular spectrum analysis, distance measures, and motif analysis.
In this course you learn to perform motif analysis and implement analyses in the spectral or frequency domain. You also discover how distance measures work, implement applications, explore signal components, and create time series features.
This course is appropriate for analysts with a quantitative background as well as domain experts who would like to augment their time-series tool box. Before taking this course, you should be comfortable with basic statistical concepts. You can gain this experience by completing the Statistics with SAS course. Familiarity with matrices and principal component analysis are also helpful but not required.
Register for this course and see more details by visiting:
OpenCourser.com/course/4p1jmn/creating
What's inside
Syllabus
Specialization Overview
In this module you get an overview of the courses in this specialization and what you can expect.
Course Overview
In this module you learn about the scope of this course and you access the software and files you will use for practices in the course.
Read more
Syllabus
Specialization Overview
In this module you get an overview of the courses in this specialization and what you can expect.
Course Overview
In this module you learn about the scope of this course and you access the software and files you will use for practices in the course.
Read more
Time Series Basics
In this module, you learn about converting transactional sequences to time series. Other topics include exploring signal components in time series via decompositions and binning, and creating new time series features.
Distance Measures
In this module you learn about the usefulness of distance or similarity measures between time series. Calculated distance measure are used as the basis in two analyses.
Spectral Analysis and Singular Spectrum Analysis (SSA)
In this module, we discuss and illustrate the basic ideas and applications in frequency domain analysis. We also discuss SSA and present demonstrations of applied SSA.
Motif Analysis
In this module you learn about detecting motifs in times series and their usefulness.
Course Review
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores advanced time-series analysis techniques, which is uncommon in other time-series courses
Taught by Chip Wells, who authored a book on time-series analysis with extensive professional experience
For analysts with quantitative background, this course provides an opportunity to augment their time-series toolbox
Covers a range of topics, including binning, spectral analysis, and motif analysis, providing a well-rounded understanding
Requires comfort with basic statistical concepts and familiarity with matrices and principal component analysis, which may limit accessibility
Assumes students have access to software and files for practice, which could be a barrier for those without technical setup
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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 Creating Features for Time Series Data with these
activities:
Spectral Analysis Tutorial
Show steps
Reinforcing the concepts of spectral analysis using guided tutorials.
Browse courses on
Frequency Domain Analysis
Show steps
-
Explore the different topics covered in Spectral Analysis
-
Go through the tutorials for each topic
-
Solve practice questions and exercises
Time Series Dashboard Development
Show steps
Developing a time series dashboard to visualize and analyze time series data.
Browse courses on
Dashboard Development
Show steps
-
Gather the necessary tools and data for dashboard development
-
Design the dashboard layout and visualizations
-
Implement the dashboard using a suitable tool or platform
-
Test and refine the dashboard for usability and performance
Show all two activities
Spectral Analysis Tutorial
Show steps
Reinforcing the concepts of spectral analysis using guided tutorials.
Browse courses on
Frequency Domain Analysis
Show steps
Show steps
Show steps
- Explore the different topics covered in Spectral Analysis
- Go through the tutorials for each topic
- Solve practice questions and exercises
Time Series Dashboard Development
Show steps
Developing a time series dashboard to visualize and analyze time series data.
Browse courses on
Dashboard Development
Show steps
Show steps
Show steps
- Gather the necessary tools and data for dashboard development
- Design the dashboard layout and visualizations
- Implement the dashboard using a suitable tool or platform
- Test and refine the dashboard for usability and performance
Show all two activities
Career center
Learners who complete Creating Features for Time Series Data will develop knowledge and skills
that may be useful to these careers:
Time Series Analyst
Time Series Analyst
Time Series Analysts use statistical methods to analyze and forecast time series data. The course Creating Features for Time Series Data is specifically designed for Time Series Analysts, as it provides a comprehensive foundation in the analysis and interpretation of time series data.
Statistician
Statistician
Statisticians design and analyze experiments, collect and interpret data, and develop statistical models. The course Creating Features for Time Series Data may be useful for Statisticians, as it provides a foundation in the analysis and interpretation of time series data, which is often encountered in a variety of fields.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. The course Creating Features for Time Series Data may be useful for Data Analysts, as it provides a foundation in the analysis and interpretation of time series data, which is often encountered in business and industry.
Data Scientist
Data Scientist
Data Scientists use statistical methods and machine learning to extract insights from data. The course Creating Features for Time Series Data may be useful for Data Scientists, as it provides a foundation in the analysis and interpretation of time series data, which is often encountered in business and industry.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use statistical methods and financial data to develop trading strategies. The course Creating Features for Time Series Data may be useful for Quantitative Analysts, as it provides a foundation in the analysis and interpretation of time series data, which is often encountered in financial markets.
Financial Analyst
Financial Analyst
Financial Analysts use statistical methods and financial data to make investment recommendations. The course Creating Features for Time Series Data may be useful for Financial Analysts, as it provides a foundation in the analysis and interpretation of time series data, which is often encountered in financial markets.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use statistical methods and mathematical models to improve efficiency and productivity. The course Creating Features for Time Series Data may be useful for Operations Research Analysts, as it provides a foundation in the analysis and interpretation of time series data, which is often encountered in operations research.
Actuary
Actuary
Actuaries use statistical methods to assess risk and develop insurance products. The course Creating Features for Time Series Data may be useful for Actuaries, as it provides a foundation in the analysis and interpretation of time series data, which is often encountered in actuarial science.
Epidemiologist
Epidemiologist
Epidemiologists use statistical methods to study the distribution and determinants of disease. The course Creating Features for Time Series Data may be useful for Epidemiologists, as it provides a foundation in the analysis and interpretation of time series data, which is often encountered in epidemiology.
Risk Analyst
Risk Analyst
Risk Analysts use statistical methods and financial data to assess risk. The course Creating Features for Time Series Data may be useful for Risk Analysts, as it provides a foundation in the analysis and interpretation of time series data, which is often encountered in risk management.
Market Researcher
Market Researcher
Market Researchers collect and analyze data to identify trends and patterns in consumer behavior. The course Creating Features for Time Series Data may be useful for Market Researchers, as it provides a foundation in the analysis and interpretation of time series data, which is often encountered in market research.
Meteorologist
Meteorologist
Meteorologists use statistical methods to study the atmosphere and weather. The course Creating Features for Time Series Data may be useful for Meteorologists, as it provides a foundation in the analysis and interpretation of time series data, which is often encountered in meteorology.
Hydrologist
Hydrologist
Hydrologists use statistical methods to study water resources. The course Creating Features for Time Series Data may be useful for Hydrologists, as it provides a foundation in the analysis and interpretation of time series data, which is often encountered in hydrology.
Geostatistician
Geostatistician
Geostatisticians use statistical methods to analyze spatial data. The course Creating Features for Time Series Data may be useful for Geostatisticians, as it provides a foundation in the analysis and interpretation of time series data, which is often encountered in geostatistics.
Biostatistician
Biostatistician
Biostatisticians design and analyze experiments, collect and interpret data, and develop statistical models for use in biology and medicine. The course Creating Features for Time Series Data may be useful for Biostatisticians, as it provides a foundation in the analysis and interpretation of time series data, which is often encountered in biological and medical research.
For more career information including salaries, visit:
OpenCourser.com/course/4p1jmn/creating
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
Creating Features for Time Series Data.
Save
This classic textbook provides a comprehensive treatment of time series analysis, including topics such as ARIMA models, spectral analysis, and state-space models. It valuable resource for students and practitioners who want to learn more about time series analysis.
Provides a comprehensive treatment of time series analysis using state-space methods. It valuable resource for students and practitioners who want to learn more about state-space models.
Provides a comprehensive overview of time series analysis, including topics such as stationarity, autocorrelation, and forecasting. It valuable resource for students and practitioners who want to learn more about time series analysis.
Provides a comprehensive treatment of time series analysis and its applications. It valuable resource for students and practitioners who want to learn more about time series analysis and its applications.
Provides a comprehensive treatment of time series analysis: theory and methods. It valuable resource for students and practitioners who want to learn more about time series analysis: theory and methods.
Save
Provides a comprehensive treatment of time series analysis with applications in R. It valuable resource for students and practitioners who want to learn more about time series analysis with applications in R.
Provides a gentle introduction to time series analysis, making it a good choice for students and practitioners who are new to the field. It covers topics such as stationarity, autocorrelation, and forecasting.
Save
Provides a comprehensive treatment of time series analysis and applications. It valuable resource for students and practitioners who want to learn more about time series analysis and applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/4p1jmn/creating
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Level
Intermediate
Via
Coursera
Institution
SAS
Instructor
Chip Wells
Language
English
This course belongs to a
collection
called
Analyzing Time Series and Sequential Data. It's comprised of three courses:
- Creating Features for Time Series Data (viewing)
- Building a Large-Scale, Automated Forecasting System
- Modeling Time Series and Sequential Data
Good to know
Explores advanced time-series analysis techniques, which is uncommon in other time-series courses
Taught by Chip Wells, who authored a book on time-series analysis with extensive professional experience
For analysts with quantitative background, this course provides an opportunity to augment their time-series toolbox
Covers a range of topics, including binning, spectral analysis, and motif analysis, providing a well-rounded understanding
Requires comfort with basic statistical concepts and familiarity with matrices and principal component analysis, which may limit accessibility
Assumes students have access to software and files for practice, which could be a barrier for those without technical setup
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