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Vinod Bakthavachalam
In this 2 hour long project-based course, you will learn the basics of time series analysis in R. By the end of this project, you will understand the essential theory for time series analysis and have built each of the major model types (Autoregressive, Moving Average, ARMA, ARIMA, and decomposition) on a real world data set to forecast the future. We will go over the essential packages and functions in R as well to make time series analysis easy.
Register for this course and see more details by visiting:
OpenCourser.com/course/67t4bi/intro
What's inside
Syllabus
The Essentials of Time Series Analysis Made Easy Using R
Welcome to this project-based course on time series analysis in R. In this project, you will learn the basics of time series analysis in just under two hours! We will cover both the essential theory and model types within time series such as AR, MA, ARMA, ARIMA, and decomposition models. After the project you will be able to analyze time series data, fit a model, and make forecasts of the future.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Makes time series analysis easy by using R, which is standard in the industry
Covers the essential theory and model types for time series analysis
Taught by Vinod Bakthavachalam, who is recognized for their work in time series analysis
Suitable for beginners, as it builds a strong foundation in time series analysis
Offers hands-on labs and interactive materials, making learning more engaging
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Reviews summary
R time series basics
learners say this introductory course on time series analysis in R provides a good overview of the concepts. The guided project along with the lectures and readings are well received. However, some learners mention there were technical issues with the Rhyme interface.
Guided project is practical and easy to follow.
"The guided project is very easy to follow."
"This guided project is very helpful for practice and learning."
"Good practical examples."
Good course for introduction to time series analysis.
"Good for introduction of the concept."
"A good overview if you just need to apply the basics."
"Great introduction to time series forecasting."
Concepts are explained clearly.
"concepts are well explained"
"The basic concepts in time series are explained really well."
"Several modules with clear explanations of the theory behind the models discussed."
Some learners experienced technical issues.
"Technical errors from the programing tool don't allow me to code the exercises, awful experience and a waste of time."
"Rhyme interface is a bit slow"
"there was no subtitles which wasted a lot of my time as it was little difficult to understand the teachers english in a few places."
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 Intro to Time Series Analysis in R with these
activities:
Review basic statistics and probability concepts
Show steps
Strengthen your foundation in statistics and probability.
Browse courses on
Statistics
Show steps
-
Review textbooks or online resources.
-
Solve practice problems.
Analyze time series data using AR
Show steps
Develop familiarity with time series analysis and the use of the R programming language, ensuring a strong foundation for the course.
Browse courses on
Time Series Analysis
Show steps
-
Review concepts of time series analysis, focusing on Autoregressive (AR) models.
-
Practice analyzing time series data using the ARIMA package in R.
Solve time series analysis problem sets
Show steps
Enhance problem-solving abilities and deepen understanding of time series analysis concepts through practical application.
Browse courses on
Time Series Analysis
Show steps
-
Attempt a set of time series analysis problem sets provided by the course instructors.
-
Seek assistance from online forums or the course's instructors if needed.
-
Review solutions and compare them to your own approaches, identifying areas for improvement.
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Join a study group for time series analysis
Show steps
Engage with peers to discuss concepts and share insights.
Browse courses on
Time Series Analysis
Show steps
-
Find a study group or create your own.
-
Meet regularly to discuss course material and work on assignments together.
Explore the RStudio Time Series Workshop
Show steps
Gain practical experience with time series analysis techniques in R.
Browse courses on
Time Series Analysis
Show steps
-
Follow the workshop tutorial at https://rstudio.com/education/materials/time-series/
-
Complete the exercises and quizzes provided in the workshop.
Follow tutorials on ARIMA modeling in R
Show steps
Gain proficiency in ARIMA modeling, a commonly used technique in time series analysis.
Browse courses on
Time Series Analysis
Show steps
-
Identify online tutorials or resources that provide comprehensive guidance on ARIMA modeling in R.
-
Follow the tutorials step-by-step, implementing the concepts in R.
-
Practice applying ARIMA models to analyze time series data.
Solve time series analysis practice problems
Show steps
Reinforce your understanding of time series analysis concepts through practice.
Browse courses on
Time Series Analysis
Show steps
-
Solve practice problems from textbooks or online resources.
-
Participate in online coding challenges or competitions.
Review the text, Time-Series Forecasting using R
Show steps
Deepen your understanding of the theoretical underpinnings of time series analysis and forecasting.
View
Forecasting: Principles and Practice
on Amazon
Show steps
-
Read Chapters 1-4.
-
Complete the exercises at the end of each chapter.
Create a time series analysis project
Show steps
Apply your knowledge of time series analysis to a real-world dataset.
Browse courses on
Time Series Analysis
Show steps
-
Choose a dataset and define a research question.
-
Analyze the data using time series techniques.
-
Create visualizations and present your findings.
Attend a time series analysis workshop
Show steps
Learn from experts and network with others in the field.
Browse courses on
Time Series Analysis
Show steps
-
Find and register for a time series analysis workshop.
-
Attend the workshop and actively participate.
Review basic statistics and probability concepts
Show steps
Strengthen your foundation in statistics and probability.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review textbooks or online resources.
- Solve practice problems.
Analyze time series data using AR
Show steps
Develop familiarity with time series analysis and the use of the R programming language, ensuring a strong foundation for the course.
Browse courses on
Time Series Analysis
Show steps
Show steps
Show steps
- Review concepts of time series analysis, focusing on Autoregressive (AR) models.
- Practice analyzing time series data using the ARIMA package in R.
Solve time series analysis problem sets
Show steps
Enhance problem-solving abilities and deepen understanding of time series analysis concepts through practical application.
Browse courses on
Time Series Analysis
Show steps
Show steps
Show steps
- Attempt a set of time series analysis problem sets provided by the course instructors.
- Seek assistance from online forums or the course's instructors if needed.
- Review solutions and compare them to your own approaches, identifying areas for improvement.
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Join a study group for time series analysis
Show steps
Engage with peers to discuss concepts and share insights.
Browse courses on
Time Series Analysis
Show steps
Show steps
Show steps
- Find a study group or create your own.
- Meet regularly to discuss course material and work on assignments together.
Explore the RStudio Time Series Workshop
Show steps
Gain practical experience with time series analysis techniques in R.
Browse courses on
Time Series Analysis
Show steps
Show steps
Show steps
- Follow the workshop tutorial at https://rstudio.com/education/materials/time-series/
- Complete the exercises and quizzes provided in the workshop.
Follow tutorials on ARIMA modeling in R
Show steps
Gain proficiency in ARIMA modeling, a commonly used technique in time series analysis.
Browse courses on
Time Series Analysis
Show steps
Show steps
Show steps
- Identify online tutorials or resources that provide comprehensive guidance on ARIMA modeling in R.
- Follow the tutorials step-by-step, implementing the concepts in R.
- Practice applying ARIMA models to analyze time series data.
Solve time series analysis practice problems
Show steps
Reinforce your understanding of time series analysis concepts through practice.
Browse courses on
Time Series Analysis
Show steps
Show steps
Show steps
- Solve practice problems from textbooks or online resources.
- Participate in online coding challenges or competitions.
Review the text, Time-Series Forecasting using R
Show steps
Deepen your understanding of the theoretical underpinnings of time series analysis and forecasting.
View
Forecasting: Principles and Practice
on Amazon
Show steps
Show steps
Show steps
- Read Chapters 1-4.
- Complete the exercises at the end of each chapter.
Create a time series analysis project
Show steps
Apply your knowledge of time series analysis to a real-world dataset.
Browse courses on
Time Series Analysis
Show steps
Show steps
Show steps
- Choose a dataset and define a research question.
- Analyze the data using time series techniques.
- Create visualizations and present your findings.
Attend a time series analysis workshop
Show steps
Learn from experts and network with others in the field.
Browse courses on
Time Series Analysis
Show steps
Show steps
Show steps
- Find and register for a time series analysis workshop.
- Attend the workshop and actively participate.
Career center
Learners who complete Intro to Time Series Analysis in R will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts investigate data using advanced statistical and computing techniques to uncover meaningful trends and patterns. Those who wish to enter this field may find this course helpful as it provides a solid foundation in the theory of time series analysis, including AR, MA, ARMA, ARIMA, and decomposition models. Furthermore, the course covers key R functions and packages, making it applicable to real-world data analysis.
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, analyzing, and interpreting large amounts of data to extract insights and inform decision-making. The course on time series analysis in R is a valuable resource for aspiring Data Scientists, as it provides them with the necessary skills to analyze time-dependent data effectively. The course covers essential concepts like stationarity, differencing, and forecasting, empowering learners to tackle complex time series problems.
Statistician
Statistician
Statisticians use mathematical and statistical techniques to collect, analyze, interpret, and present data. This course on time series analysis in R may be useful for aspiring Statisticians, as it provides a comprehensive overview of the field. The course covers topics such as model selection, parameter estimation, and forecasting, enabling learners to develop a strong foundation in time series analysis.
Financial Analyst
Financial Analyst
Financial Analysts use financial data and statistical techniques to assess the performance and risk associated with investments. The course on time series analysis in R may be useful for aspiring Financial Analysts, as it provides a foundation for understanding and analyzing financial time series data. The course covers concepts like seasonality, trend analysis, and forecasting, which are essential for making informed financial decisions.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess and manage financial risks. The course on time series analysis in R may be useful for aspiring Actuaries, as it provides a foundation for understanding and analyzing time series data related to insurance and finance. The course covers concepts like time series modeling and forecasting, which are essential for actuarial work.
Risk Analyst
Risk Analyst
Risk Analysts assess and manage financial and operational risks within organizations. The course on time series analysis in R may be useful for aspiring Risk Analysts, as it provides a foundation for understanding and analyzing risk over time. The course covers concepts like time series modeling and forecasting, which are essential for identifying and mitigating risks.
Market Researcher
Market Researcher
Market Researchers collect, analyze, and interpret data to understand market trends and consumer behavior. The course on time series analysis in R may be useful for aspiring Market Researchers, as it provides a foundation for analyzing and forecasting time-dependent market data. The course covers concepts like time series decomposition and forecasting, enabling learners to identify trends and patterns in market data.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. Those who wish to enter this field may find this course useful as it provides a solid foundation in the theory of time series analysis, including AR, MA, ARMA, ARIMA, and decomposition models. Furthermore, the course covers key R functions and packages, making it applicable to real-world data analysis.
Business Analyst
Business Analyst
Business Analysts use data and statistical techniques to analyze business performance and identify areas for improvement. The course on time series analysis in R may be useful for aspiring Business Analysts, as it provides a foundation for analyzing and forecasting time-dependent business data. The course covers concepts like time series decomposition and forecasting, enabling learners to identify trends and patterns in business data.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. The course on time series analysis in R may be useful for aspiring Machine Learning Engineers who work with time-dependent data. The course covers concepts like time series modeling and forecasting, enabling learners to build machine learning models that can analyze and predict time-dependent data.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines and infrastructure. The course on time series analysis in R may be useful for aspiring Data Engineers who work with time-dependent data. The course covers concepts like time series modeling and forecasting, enabling learners to build data pipelines that can process and analyze time-dependent data.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. The course on time series analysis in R may be useful for aspiring Software Engineers who work with time-dependent data. The course covers concepts like time series modeling and forecasting, enabling learners to develop software solutions that can analyze and predict time-dependent data.
Financial Modeler
Financial Modeler
Financial Modelers use financial data and statistical techniques to build financial models. The course on time series analysis in R may be useful for aspiring Financial Modelers, as it provides a foundation for understanding and analyzing time series data related to finance. The course covers concepts like time series modeling and forecasting, which are essential for building accurate financial models.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical techniques to solve complex business problems. The course on time series analysis in R may be useful for aspiring Operations Research Analysts, as it provides a foundation for analyzing and forecasting time-dependent data. The course covers concepts like time series decomposition and forecasting, enabling learners to identify trends and patterns in operational data.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data and statistical techniques to analyze business performance and identify areas for improvement. The course on time series analysis in R may be useful for aspiring Business Intelligence Analysts, as it provides a foundation for analyzing and forecasting time-dependent business data. The course covers concepts like time series decomposition and forecasting, enabling learners to identify trends and patterns in business data.
For more career information including salaries, visit:
OpenCourser.com/course/67t4bi/intro
Reading list
We've selected 11 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
Intro to Time Series Analysis in R.
Widely-used textbook on time series analysis. It will be useful as a reference for this course.
Save
Comprehensive guide to time series forecasting. It will be a useful reference tool for this course.
Textbook on time series analysis using R. It will be useful as a reference for this course's material.
Comprehensive book on time series analysis. It will be useful as a reference for this course.
Save
Is an introductory textbook on time series analysis. It will be useful as a reference for this course.
More advanced book on time series analysis. It will be useful as a reference for this course.
This is an introduction to time series analysis. It will be helpful for providing a background for this course's material.
Will be useful as additional reading and can provide more breadth to the material.
Will be additional reading for this course and can provide more breadth to the material.
Introductory textbook on time series analysis. It will be helpful for providing a background for this course's material.
Save
Will be additional reading for this course and can provide more breadth to the material.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/67t4bi/intro
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Vinod Bakthavachalam
Language
English
Good to know
Makes time series analysis easy by using R, which is standard in the industry
Covers the essential theory and model types for time series analysis
Taught by Vinod Bakthavachalam, who is recognized for their work in time series analysis
Suitable for beginners, as it builds a strong foundation in time series analysis
Offers hands-on labs and interactive materials, making learning more engaging
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