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Farhad Abdi
In this 2-hour long project-based course, you will learn how to use ARIMA model for time series analysis and forecasting. Time series exists every where in our life from nature to stock market. You will learn how to do the basic statistical tests for times series and implement them in Python.
By the end of this project you will be able to understand times series concepts and analyze different datasets.
Register for this course and see more details by visiting:
OpenCourser.com/course/twnjuy/time
Good to know
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Know what's good ,
what to watch for , and
possible dealbreakers
Covers time series concepts and their analysis
Provides a practical hands-on approach with Python implementation
Taught by Farhad Abdi, an expert in the field
Suitable for beginners seeking an introduction to time series analysis
Complements existing knowledge and skills in data analysis
Requires basic statistical knowledge as a prerequisite
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Reviews summary
Introduction to time series in python
This 2-hour course on time series forecasting and ARIMA models in Python provides a good foundation in the basics of time series analysis. The course is well-suited for beginners who are looking to learn the fundamental concepts and how to implement them in Python. However, more advanced learners may find the content to be too basic.
Good for learning the basics of time series analysis.
"It's a good introduction to python code for Time Series modelling, but pretty basic."
"By the end of this project you will be able to understand times series concepts and analyze different datasets."
The audio and video quality is poor.
"Very hard to understand what instructor on video."
"There doesn't seem to be QC of the audo/video of the course."
The course lacks in-depth explanations.
"It's very poorly explained. "
"No reasonable explanation of the theory, do just for sake of completion. Not recommended at all."
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 Time Series Forecasting and ARIMA Models in Python with these
activities:
Review your notes from previous courses on statistics and probability
Show steps
This course builds on your knowledge of statistics and probability. Reviewing your notes from previous courses will help you to refresh your memory and to ensure that you have a strong foundation for success in this course.
Browse courses on
Statistics
Show steps
-
Gather your notes from previous courses on statistics and probability
-
Review your notes
-
Complete any practice problems
Start a Study Group for Time Series Analysis
Show steps
Engaging with other students on a regular basis will help you to better understand the course material and to retain it over the long term. A dedicated group is likely to be more productive and motivated.
Browse courses on
Time Series Analysis
Show steps
-
Find other students who are interested in forming a study group
-
Schedule regular meeting times
-
Discuss the course material
Read Introduction to Time Series Analysis and Forecasting
Show steps
This book provides a comprehensive overview of time series analysis and forecasting, which will help you understand the concepts discussed in this course and apply them in practice.
View
Introduction to Time Series and Forecasting
on Amazon
Show steps
-
Read the book's introduction and first chapter
-
Review the basics of probability and statistics
-
Complete the practice problems at the end of each chapter
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow the ARIMA Time Series Forecasting Tutorial
Show steps
This tutorial provides a step-by-step guide to using ARIMA for time series forecasting. You will learn how to prepare your data, fit an ARIMA model, and evaluate its performance.
Browse courses on
Time Series Forecasting
Show steps
-
Read the tutorial and follow the instructions
-
Complete the practice exercises
-
Apply the ARIMA model to a real-world dataset
Solve Time Series Forecasting Practice Problems
Show steps
These practice problems will help you test your understanding of time series forecasting and ARIMA. By solving these problems, you will develop the skills necessary to apply these techniques in practice.
Browse courses on
Time Series Forecasting
Show steps
-
Download the practice problems
-
Solve the practice problems
-
Check your answers against the provided solutions
Create a Study Guide for Time Series Analysis
Show steps
Creating a study guide will help you to organize your notes and to identify the most important concepts in this course. This will make it easier for you to review the material and prepare for exams.
Browse courses on
Time Series Analysis
Show steps
-
Gather your notes, assignments, and other course materials
-
Organize your materials by topic
-
Create a study guide that includes key concepts, definitions, and examples
Build a Time Series Forecasting Model for a Real-World Dataset
Show steps
This project will allow you to apply the skills you have learned in this course to a real-world problem. By building a time series forecasting model, you will gain valuable experience in data analysis and machine learning.
Browse courses on
Time Series Forecasting
Show steps
-
Choose a real-world dataset
-
Prepare the data for time series forecasting
-
Fit an ARIMA model to the data
-
Evaluate the performance of the model
-
Write a report on your findings
Review your notes from previous courses on statistics and probability
Show steps
This course builds on your knowledge of statistics and probability. Reviewing your notes from previous courses will help you to refresh your memory and to ensure that you have a strong foundation for success in this course.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Gather your notes from previous courses on statistics and probability
- Review your notes
- Complete any practice problems
Start a Study Group for Time Series Analysis
Show steps
Engaging with other students on a regular basis will help you to better understand the course material and to retain it over the long term. A dedicated group is likely to be more productive and motivated.
Browse courses on
Time Series Analysis
Show steps
Show steps
Show steps
- Find other students who are interested in forming a study group
- Schedule regular meeting times
- Discuss the course material
Read Introduction to Time Series Analysis and Forecasting
Show steps
This book provides a comprehensive overview of time series analysis and forecasting, which will help you understand the concepts discussed in this course and apply them in practice.
View
Introduction to Time Series and Forecasting
on Amazon
Show steps
Show steps
Show steps
- Read the book's introduction and first chapter
- Review the basics of probability and statistics
- Complete the practice problems at the end of each chapter
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow the ARIMA Time Series Forecasting Tutorial
Show steps
This tutorial provides a step-by-step guide to using ARIMA for time series forecasting. You will learn how to prepare your data, fit an ARIMA model, and evaluate its performance.
Browse courses on
Time Series Forecasting
Show steps
Show steps
Show steps
- Read the tutorial and follow the instructions
- Complete the practice exercises
- Apply the ARIMA model to a real-world dataset
Solve Time Series Forecasting Practice Problems
Show steps
These practice problems will help you test your understanding of time series forecasting and ARIMA. By solving these problems, you will develop the skills necessary to apply these techniques in practice.
Browse courses on
Time Series Forecasting
Show steps
Show steps
Show steps
- Download the practice problems
- Solve the practice problems
- Check your answers against the provided solutions
Create a Study Guide for Time Series Analysis
Show steps
Creating a study guide will help you to organize your notes and to identify the most important concepts in this course. This will make it easier for you to review the material and prepare for exams.
Browse courses on
Time Series Analysis
Show steps
Show steps
Show steps
- Gather your notes, assignments, and other course materials
- Organize your materials by topic
- Create a study guide that includes key concepts, definitions, and examples
Build a Time Series Forecasting Model for a Real-World Dataset
Show steps
This project will allow you to apply the skills you have learned in this course to a real-world problem. By building a time series forecasting model, you will gain valuable experience in data analysis and machine learning.
Browse courses on
Time Series Forecasting
Show steps
Show steps
Show steps
- Choose a real-world dataset
- Prepare the data for time series forecasting
- Fit an ARIMA model to the data
- Evaluate the performance of the model
- Write a report on your findings
Career center
Learners who complete Time Series Forecasting and ARIMA Models in Python will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists work with data to build models and find patterns that can be used to make better decisions. They use a variety of statistical and machine learning techniques to analyze data and identify trends. The Time Series Forecasting and ARIMA Models in Python course can be useful for this role, as it helps build a foundation in time series analysis and forecasting. This knowledge can be helpful for understanding data trends and making more informed predictions.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to provide insights and make predictions. They use a variety of statistical techniques to analyze data and draw conclusions. The Time Series Forecasting and ARIMA Models in Python course can be useful for this role, as it helps build a foundation in time series analysis and forecasting. This knowledge can be helpful for understanding data trends and making more informed predictions.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. They use a variety of data analysis and forecasting techniques to identify investment opportunities and manage risk. The Time Series Forecasting and ARIMA Models in Python course can be useful for this role, as it helps build a foundation in time series analysis and forecasting. This knowledge can be helpful for understanding market trends and making more informed investment recommendations.
Risk Analyst
Risk Analyst
Risk Analysts identify, assess, and mitigate risks for businesses. They use a variety of data analysis and forecasting techniques to identify potential risks and develop strategies to mitigate them. The Time Series Forecasting and ARIMA Models in Python course can be useful for this role, as it helps build a foundation in time series analysis and forecasting. This knowledge can be helpful for understanding risk trends and making more informed recommendations.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. They work with data scientists and other stakeholders to ensure that models are accurate and efficient. The Time Series Forecasting and ARIMA Models in Python course can be useful for this role, as it helps build a foundation in time series analysis and forecasting. This knowledge can be helpful for understanding data trends and making more informed decisions about machine learning models.
Financial Analyst
Financial Analyst
Financial Analysts study and interpret financial information and data. They use this information to make recommendations on investments, and to plan for the future. The Time Series Forecasting and ARIMA Models in Python course can be useful for this role, as it helps build a foundation in time series analysis and forecasting. This knowledge can be helpful for understanding market trends and making more informed investment recommendations.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. They use this information to develop insurance products and pricing. The Time Series Forecasting and ARIMA Models in Python course can be useful for this role, as it helps build a foundation in time series analysis and forecasting. This knowledge can be helpful for understanding risk trends and making more informed recommendations.
Market Researcher
Market Researcher
Market Researchers study consumer behavior and trends to help businesses make better decisions. They use a variety of data collection and analysis techniques to gather insights into consumer needs and wants. The Time Series Forecasting and ARIMA Models in Python course can be useful for this role, as it helps build a foundation in time series analysis and forecasting. This knowledge can be helpful for understanding market trends and making more informed recommendations.
Business Analyst
Business Analyst
Business Analysts work with businesses to understand their needs and develop solutions to improve their operations. They use data analysis and forecasting to identify areas for improvement and to develop strategies for growth. The Time Series Forecasting and ARIMA Models in Python course can be useful for this role, as it helps build a foundation in time series analysis and forecasting. This knowledge can be helpful for understanding business trends and making more informed recommendations.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve complex business problems. They use these models to improve efficiency, productivity, and profit. The Time Series Forecasting and ARIMA Models in Python course can be useful for this role, as it helps build a foundation in time series analysis and forecasting. This knowledge can be helpful for understanding business trends and making more informed recommendations.
Actuarial Analyst
Actuarial Analyst
Actuarial Analysts use mathematical and statistical models to assess risk and uncertainty. They use this information to develop insurance products and pricing. The Time Series Forecasting and ARIMA Models in Python course may be useful for this role, as it helps build a foundation in time series analysis and forecasting. This knowledge can be helpful for understanding risk trends and making more informed recommendations.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data systems. They work with data scientists and other stakeholders to ensure that data is available and accessible for analysis. The Time Series Forecasting and ARIMA Models in Python course can be useful for this role, as it helps build a foundation in time series analysis and forecasting. This knowledge can be helpful for understanding data trends and making more informed decisions about data systems.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with a variety of stakeholders to ensure that software is functional and efficient. The Time Series Forecasting and ARIMA Models in Python course may be useful for this role, as it helps build a foundation in time series analysis and forecasting. This knowledge can be helpful for understanding data trends and making more informed decisions about software design and development.
Financial Planner
Financial Planner
Financial Planners help individuals and families manage their finances. They use a variety of data analysis and forecasting techniques to understand financial trends and make recommendations. The Time Series Forecasting and ARIMA Models in Python course may be useful for this role, as it helps build a foundation in time series analysis and forecasting. This knowledge can be helpful for understanding financial trends and making more informed recommendations.
Economist
Economist
Economists study the economy and how it works. They use a variety of data analysis and forecasting techniques to understand economic trends and make predictions. The Time Series Forecasting and ARIMA Models in Python course may be useful for this role, as it helps build a foundation in time series analysis and forecasting. This knowledge can be helpful for understanding economic trends and making more informed predictions.
For more career information including salaries, visit:
OpenCourser.com/course/twnjuy/time
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
Time Series Forecasting and ARIMA Models in Python.
Save
Provides a comprehensive overview of time series analysis and forecasting techniques. It classic text in the field and is widely used as a textbook in academic institutions.
Save
Provides a practical guide to time series forecasting. It covers a wide range of forecasting techniques, including ARIMA models.
Provides a comprehensive overview of time series analysis and its applications. It covers a wide range of topics, including ARIMA models.
Provides a practical guide to time series analysis in R. It covers a wide range of forecasting techniques, including ARIMA models.
Save
Provides a practical guide to time series analysis in R. It covers a wide range of forecasting techniques, including ARIMA models.
Provides a comprehensive overview of time series analysis theory and methods. It covers a wide range of topics, including ARIMA models.
Provides a practical guide to time series prediction. It covers a wide range of forecasting techniques, including ARIMA models.
Provides a comprehensive introduction to time series analysis. It covers a wide range of topics, including ARIMA models.
Save
Provides a comprehensive overview of statistical methods for time series analysis. It covers a wide range of topics, including ARIMA models.
Provides a comprehensive introduction to time series analysis. It covers a wide range of topics, including ARIMA models.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/twnjuy/time
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Farhad Abdi
Good to know
Covers time series concepts and their analysis
Provides a practical hands-on approach with Python implementation
Taught by Farhad Abdi, an expert in the field
Suitable for beginners seeking an introduction to time series analysis
Complements existing knowledge and skills in data analysis
Requires basic statistical knowledge as a prerequisite
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