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Mining Data from Time Series

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.

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Time Series Analysis ARIMA Models Seasonal Decomposition Exponential Smoothing Prophet

What's inside

Syllabus

Course Overview

Introduction

The Statistics of Time Series

Using ARIMA Models in Python

Traffic lights

Read about what's good

what should give you pause

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

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Reviews summary

Practical time series analysis with python

According to learners, this course offers a solid introduction and excellent foundation in time series analysis using Python, particularly excelling in its practical application. Students frequently highlight the instructor's ability to explain complex concepts like ARIMA and Prophet very clearly, making them accessible. The hands-on labs and coding exercises are considered a major strength, allowing for direct application of learned skills. While the course provides a comprehensive overview of key models, some learners with less statistical or Python background found the pace occasionally rushed, suggesting some prior knowledge is beneficial. Advanced users sometimes desire greater depth in specific areas, such as multivariate time series.

Complex time series concepts are explained clearly.

"The instructor explained complex concepts like ARIMA and Prophet very clearly, which helped me grasp them."

"I appreciate how clear the explanations were for concepts like stationarity and autocorrelation."

"The instructor made complex topics accessible, and the hands-on coding really solidified my understanding."

Strong emphasis on hands-on coding and real-world applicability.

"The hands-on labs were extremely helpful, and I now feel much more confident applying these techniques in my work."

"I found the focus on practical application with Python invaluable, unlike other courses that were too theoretical."

"The coding exercises reinforced the theory perfectly, making it one of the best for practical application."

Pacing can feel fast for some, requiring review.

"I found the explanations sometimes rushed, especially being new to time series."

"I felt the course moved too quickly without sufficient foundational explanations for some parts."

"It felt a bit rushed, particularly the section on multivariate time series was very brief."

Provides a solid foundation but less depth in advanced topics.

"While comprehensive as an introduction, I wanted more depth in certain advanced topics like multivariate time series."

"I see this more as a quick overview for foundational learning rather than a deep dive into every aspect."

"Some sections could use more detailed mathematical intuition, but for applied work, I found it great."

Learners benefit from existing Python and statistical background.

"I found it assumed a certain level of statistical background, and I had to re-watch some lectures multiple times."

"I struggled because it moved too quickly; if you're not already comfortable with statistics and Python, it's challenging."

"The pace felt just right for me, having a basic understanding of Python and statistics beforehand."

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

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Build a solid foundation by organizing and compiling notes, assignments, and other course materials for future reference and review.

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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

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Become more familiar with the topic and some key terms, models, and theories surrounding time series data analysis.

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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

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Collaborate and learn from other students, exchange ideas, and enhance your understanding of the course material.

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Find a study group on Piazza or another online platform
Attend study group meetings regularly
Participate in discussions and ask questions

Four other activities

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Work on time series practice problems

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Reinforce your understanding of time series models by working on practice problems.

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Solve 10 practice problems on ARIMA models
Solve 10 practice problems on exponential smoothing models

Follow tutorials on Prophet

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Learn how to use Prophet, a popular time series forecasting package developed by Facebook.

Browse courses on Prophet

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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

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Apply your time series forecasting skills to a real-world dataset to solve a specific business problem.

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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

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Gain hands-on experience with time series forecasting by contributing to the development of the Prophet open-source library.

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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 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.

See salaries and explore the career path for Data 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.

See salaries and explore the career path for Business Intelligence Analyst

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Statistician

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.

See salaries and explore the career path for Quantitative 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.

See salaries and explore the career path for Operations 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.

See salaries and explore the career path for Market Research 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.

See salaries and explore the career path for Financial 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.

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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.

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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.

See salaries and explore the career path for Insurance 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.

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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.

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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.

See salaries and explore the career path for Data Visualization Specialist

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.

See salaries and explore the career path for Business Analyst

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.

Time Series Analysis

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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.

Time Series Analysis: Forecasting and Control...

Hardcover

$$$$

Time Series Analysis: Forecasting and Control...

Kindle Edition

$$$$

Forecasting

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Is an excellent resource text and goes into detail on topics such as time series decomposition and ensemble forecasting. It is freely available online.

Forecasting: Principles and Practice

Paperback

$$$

Forecasting: principles and practice

Paperback

$$$

Forecasting: principles and practice

Paperback

$$$

Time Series Analysis and Its Applications

Save

Will provide a very good background in time series analysis concepts including seasonality, stationarity and more.

Time Series Analysis and Its Applications: With R...

Paperback

$$$

Time Series Analysis and Its Applications: With R...

Hardcover

$$$

Time Series Analysis and Its Applications (Springer...

Kindle Edition

$$$

Time Series Analysis

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.

Time Series Analysis: Forecasting and Control...

Hardcover

$$$$

Time Series Analysis: Forecasting and Control...

Kindle Edition

$$$$

Multivariate Time Series Analysis and Applications

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Provides a comprehensive overview of multivariate time series analysis. It is written in a clear and concise style and includes many examples and exercises.

Multivariate Time Series Analysis and Applications...

Hardcover

$$$$

Multivariate Time Series Analysis and Applications...

Kindle Edition

$$$

Introduction to Coastal Processes and Geomorphology

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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.

Introduction to Coastal Processes and Geomorphology

Paperback

Introduction to Coastal Processes and Geomorphology

Kindle Edition

$$$