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Martin Burger
Master time series analysis in Python and be able to produce powerful quantitative forecasts.
Read more
Master time series analysis in Python and be able to produce powerful quantitative forecasts.
Read more
Master time series analysis in Python and be able to produce powerful quantitative forecasts.
Are you struggling with the analysis of time series data or do you want to create a powerful quantitative forecasting model in Python? In this course, Mining Data from Time Series, you will gain the ability to model and forecast time series in Python. First, you will learn about time series data, which is data captured along a timeline with specific statistical traits crucial for any model. Then, you will see the statistical foundations first before diving into the classic time series models of ARIMA, seasonal decomposition as well as exponential smoothing. Finally, you will explore some advanced concepts like the new Prophet package from Facebook or multivariate time series. When you are finished with this course, you will have the skills and knowledge of time series analysis needed to model and forecast standard univariate time series data sets.
Register for this course and see more details by visiting:
OpenCourser.com/course/435g71/mining
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What's inside
Syllabus
Course Overview
Introduction
The Statistics of Time Series
Using ARIMA Models in Python
Read more
Syllabus
Course Overview
Introduction
The Statistics of Time Series
Using ARIMA Models in Python
Read more
Handling Seasonal Datasets and Working with Seasonality
Using Exponential Smoothing Models in Python
Multivariate Statistics, Prophet, and Further Resources
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops advanced time series models using Python and popular libraries, making it suitable for data scientists and analysts
Taught by Martin Burger, an expert in time series analysis and forecasting
Covers a wide range of time series analysis techniques, including ARIMA, seasonal decomposition, and exponential smoothing
Utilizes the powerful Python ecosystem with popular libraries for time series analysis, including statsmodels
Suitable for students who have already taken some statistical analysis courses
Students should have some familiarity with Python programming language
Save this course
Save Mining Data from Time Series 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 Mining Data from Time Series with these
activities:
Organize course materials
Show steps
Build a solid foundation by organizing and compiling notes, assignments, and other course materials for future reference and review.
Show steps
-
Create a digital or physical folder to keep all course materials organized
-
File and label notes, assignments, and other handouts systematically
-
Use colors, tabs, or other visual cues for easy access and retrieval
Review textbook
Show steps
Become more familiar with the topic and some key terms, models, and theories surrounding time series data analysis.
Show steps
-
Read Chapter 1: Introduction
-
Read Chapter 2: Fundamentals of Time Series
-
Read Chapter 3: Stationarity and Time Series Models
Join a study group with other learners taking the course
Show steps
Collaborate and learn from other students, exchange ideas, and enhance your understanding of the course material.
Show steps
-
Find a study group on Piazza or another online platform
-
Attend study group meetings regularly
-
Participate in discussions and ask questions
Four other activities
Expand to see all activities and additional details
Show all seven activities
Work on time series practice problems
Show steps
Reinforce your understanding of time series models by working on practice problems.
Show steps
-
Solve 10 practice problems on ARIMA models
-
Solve 10 practice problems on exponential smoothing models
Follow tutorials on Prophet
Show steps
Learn how to use Prophet, a popular time series forecasting package developed by Facebook.
Browse courses on
Prophet
Show steps
-
Watch the Prophet tutorial on the Facebook Developers website
-
Complete the Prophet tutorial on the Kaggle website
Build a time series forecasting model for a real-world dataset
Show steps
Apply your time series forecasting skills to a real-world dataset to solve a specific business problem.
Show steps
-
Choose a dataset from Kaggle or another reputable source
-
Explore the data and identify the time series component
-
Select an appropriate time series forecasting model
-
Train and evaluate the model
-
Deploy the model and monitor its performance
Contribute to the Prophet open-source project
Show steps
Gain hands-on experience with time series forecasting by contributing to the development of the Prophet open-source library.
Show steps
-
Fork the Prophet repository on GitHub
-
Make a small contribution to the codebase
-
Submit a pull request
Organize course materials
Show steps
Build a solid foundation by organizing and compiling notes, assignments, and other course materials for future reference and review.
Show steps
Show steps
Show steps
- Create a digital or physical folder to keep all course materials organized
- File and label notes, assignments, and other handouts systematically
- Use colors, tabs, or other visual cues for easy access and retrieval
Review textbook
Show steps
Become more familiar with the topic and some key terms, models, and theories surrounding time series data analysis.
Show steps
Show steps
Show steps
- Read Chapter 1: Introduction
- Read Chapter 2: Fundamentals of Time Series
- Read Chapter 3: Stationarity and Time Series Models
Join a study group with other learners taking the course
Show steps
Collaborate and learn from other students, exchange ideas, and enhance your understanding of the course material.
Show steps
Show steps
Show steps
- Find a study group on Piazza or another online platform
- Attend study group meetings regularly
- Participate in discussions and ask questions
Four other activities
Expand to see all activities and additional details
Show all seven activities
Work on time series practice problems
Show steps
Reinforce your understanding of time series models by working on practice problems.
Show steps
Show steps
Show steps
- Solve 10 practice problems on ARIMA models
- Solve 10 practice problems on exponential smoothing models
Follow tutorials on Prophet
Show steps
Learn how to use Prophet, a popular time series forecasting package developed by Facebook.
Browse courses on
Prophet
Show steps
Show steps
Show steps
- Watch the Prophet tutorial on the Facebook Developers website
- Complete the Prophet tutorial on the Kaggle website
Build a time series forecasting model for a real-world dataset
Show steps
Apply your time series forecasting skills to a real-world dataset to solve a specific business problem.
Show steps
Show steps
Show steps
- Choose a dataset from Kaggle or another reputable source
- Explore the data and identify the time series component
- Select an appropriate time series forecasting model
- Train and evaluate the model
- Deploy the model and monitor its performance
Contribute to the Prophet open-source project
Show steps
Gain hands-on experience with time series forecasting by contributing to the development of the Prophet open-source library.
Show steps
Show steps
Show steps
- Fork the Prophet repository on GitHub
- Make a small contribution to the codebase
- Submit a pull request
Career center
Learners who complete Mining Data from Time Series will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts build and manage systems used to gather, archive, and organize massive amounts of structured and unstructured data. To be effective in this data-driven industry, you need to be able to interpret data, and identify trends and patterns. This course can help you develop these crucial skills, which can help launch your career as a Data Analyst.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts help companies make better decisions by providing them with data-driven insights. To be effective in this role, you need to be able to understand business problems, collect and analyze data, and communicate your findings. This course can help you build a solid foundation in data analysis, which can help you succeed as a Business Intelligence Analyst.
Data Scientist
Data Scientist
Data Scientists use their knowledge of statistics, mathematics, and computer science to extract insights from data. To be effective in this role, you need to be able to collect, clean, and analyze data, and develop predictive models. This course can help you develop the skills you need to become a successful Data Scientist.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. To be effective in this role, you need to have a strong understanding of statistics, and be able to use statistical software to analyze data. This course can help you develop the skills you need to become a successful Statistician.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. To be effective in this role, you need to have a strong understanding of mathematics, statistics, and finance. This course can help you develop the skills you need to become a successful Quantitative Analyst.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve business problems. To be effective in this role, you need to have a strong understanding of mathematics, statistics, and operations research. This course can help you develop the skills you need to become a successful Operations Research Analyst.
Market Research Analyst
Market Research Analyst
Market Research Analysts collect and analyze data about markets, consumers, and competitors. To be effective in this role, you need to have a strong understanding of market research methods and be able to interpret data. This course can help you develop the skills you need to become a successful Market Research Analyst.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment recommendations. To be effective in this role, you need to have a strong understanding of finance and be able to analyze financial data. This course can help you develop the skills you need to become a successful Financial Analyst.
Risk Analyst
Risk Analyst
Risk Analysts identify and assess risks to businesses. To be effective in this role, you need to have a strong understanding of risk management and be able to analyze data. This course can help you develop the skills you need to become a successful Risk Analyst.
Actuary
Actuary
Actuaries use mathematics and statistics to assess risk and uncertainty. To be effective in this role, you need to have a strong understanding of mathematics, statistics, and actuarial science. This course can help you develop the skills you need to become a successful Actuary.
Insurance Analyst
Insurance Analyst
Insurance Analysts use financial and statistical data to assess risk and make underwriting decisions. To be effective in this role, you need to have a strong understanding of insurance and be able to analyze data. This course can help you develop the skills you need to become a successful Insurance Analyst.
Investment Analyst
Investment Analyst
Investment Analysts use financial data to make investment recommendations. To be effective in this role, you need to have a strong understanding of finance and be able to analyze data. This course can help you develop the skills you need to become a successful Investment Analyst.
Financial Planner
Financial Planner
Financial Planners help individuals and families plan for their financial future. To be effective in this role, you need to have a strong understanding of finance and be able to analyze data. This course can help you develop the skills you need to become a successful Financial Planner.
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists create visual representations of data. To be effective in this role, you need to have a strong understanding of data visualization techniques and be able to use data visualization software. This course can help you develop the skills you need to become a successful Data Visualization Specialist.
Business Analyst
Business Analyst
Business Analysts use data to solve business problems. To be effective in this role, you need to have a strong understanding of business and be able to analyze data. This course can help you develop the skills you need to become a successful Business Analyst.
For more career information including salaries, visit:
OpenCourser.com/course/435g71/mining
Reading list
We've selected seven 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
Mining Data from Time Series.
Save
Is used as a textbook by many academic institutions for introductory graduate level time series analysis. It covers a wide range of time series modeling from ARIMA, SARIMA, exponential smoothing including the excellent time series analysis software package forecast.
Save
Is an excellent resource text and goes into detail on topics such as time series decomposition and ensemble forecasting. It is freely available online.
Will provide a very good background in time series analysis concepts including seasonality, stationarity and more.
Save
Leverages the free statistical software R to work through many time series data examples and gives an introduction to many R time series packages.
Provides a comprehensive overview of multivariate time series analysis. It is written in a clear and concise style and includes many examples and exercises.
This textbook that provides a comprehensive overview of time series analysis. It is written in a clear and concise style and includes many examples and exercises.
Save
Provides a gentle introduction to time series analysis. It is written in R, which popular language for data analysis.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/435g71/mining
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Time
179 hours
Level
Advanced
Via
Pluralsight
Instructor
Martin Burger
Language
English
Good to know
Develops advanced time series models using Python and popular libraries, making it suitable for data scientists and analysts
Taught by Martin Burger, an expert in time series analysis and forecasting
Covers a wide range of time series analysis techniques, including ARIMA, seasonal decomposition, and exponential smoothing
Utilizes the powerful Python ecosystem with popular libraries for time series analysis, including statsmodels
Suitable for students who have already taken some statistical analysis courses
Students should have some familiarity with Python programming language
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