Practical Time Series Analysis from Coursera

Welcome to Practical Time Series Analysis!

Many of us are "accidental" data analysts. We trained in the sciences, business, or engineering and then found ourselves confronted with data for which we have no formal analytic training. This course is designed for people with some technical competencies who would like more than a "cookbook" approach, but who still need to concentrate on the routine sorts of presentation and analysis that deepen the understanding of our professional topics.

In practical Time Series Analysis we look at data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, the price of agricultural products, and more. We look at several mathematical models that might be used to describe the processes which generate these types of data. We also look at graphical representations that provide insights into our data. Finally, we also learn how to make forecasts that say intelligent things about what we might expect in the future.

Please take a few minutes to explore the course site. You will find video lectures with supporting written materials as well as quizzes to help emphasize important points. The language for the course is R, a free implementation of the S language. It is a professional environment and fairly easy to learn.

You can discuss material from the course with your fellow learners. Please take a moment to introduce yourself!

Time Series Analysis can take effort to learn- we have tried to present those ideas that are "mission critical" in a way where you understand enough of the math to fell satisfied while also being immediately productive. We hope you enjoy the class!

What's inside

Syllabus

WEEK 1: Basic Statistics

During this first week, we show how to download and install R on Windows and the Mac. We review those basics of inferential and descriptive statistics that you'll need during the course.

Week 2: Visualizing Time Series, and Beginning to Model Time Series

In this week, we begin to explore and visualize time series available as acquired data sets. We also take our first steps on developing the mathematical models needed to analyze time series data.

Week 3: Stationarity, MA(q) and AR(p) processes

In Week 3, we introduce few important notions in time series analysis: Stationarity, Backward shift operator, Invertibility, and Duality. We begin to explore Autoregressive processes and Yule-Walker equations.

Week 4: AR(p) processes, Yule-Walker equations, PACF

In this week, partial autocorrelation is introduced. We work more on Yule-Walker equations, and apply what we have learned so far to few real-world datasets.

Week 5: Akaike Information Criterion (AIC), Mixed Models, Integrated Models

In Week 5, we start working with Akaike Information criterion as a tool to judge our models, introduce mixed models such as ARMA, ARIMA and model few real-world datasets.

Week 6: Seasonality, SARIMA, Forecasting

In the last week of our course, another model is introduced: SARIMA. We fit SARIMA models to various datasets and start forecasting.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops a foundation of core time series analysis skillsets

Appropriate for intermediate learners who wish to learn time series analysis skill

Emphasizes mission critical ideas to help learners understand the subject matter

Taught by professionals in the field of time series analysis

Incorporates real-world datasets into the learning process

It requires students to come in with some technical competency

Reviews summary

Time series analysis: practical applications

learners say this course is largely positive, offering a solid foundation in time series concepts and practical implementation using R. The course delves into the underlying math and provides ample examples to enhance understanding. However, it can be challenging for those without a strong background in statistics and math.

Both instructors present the material clearly and simplify complex concepts.

"The course instructors explain the concepts very well, especially the background maths part, unlike many other courses on Time Series Analysis where they just introduce the concept and then without much explanation directly jump to the code implementation."

"Despite some issues re data availability the course is excellent, well-structured and explained."

"Both instructors are very competent and are complementary in their way of explaining and simplifying complex concepts."

Provides a strong theoretical foundation in time series analysis, including mathematical explanations and proofs.

"All major aspects of time series analysis has been covered thoroughly."

"The instructors explain the concepts very well, especially the background maths part, unlike many other courses on Time Series Analysis where they just introduce the concept and then without much explanation directly jump to the code implementation."

"I think the information taught in this course is very valuable since I'm able to use what I learned in my job."

Covers practical applications of time series models using R, including hands-on examples and exercises.

"very good course for a person who doesn't have an experience in time series and R."

"the two teachers are amazings and they absolutely know how to send the most helpful and important messages information and methods throughout all the lesson."

"The instructors took the effort and dedication create a comprehensive and well-structured course."

Some links and code may be outdated or broken.

"The Discussion threads have the answer for these sometimes thorny issues around current R code versions."

"While professor Thistetlon gave the classes in a way that kept my attention,Prof Sadigov wasn't so good at it. Clearly he know a lot but not as a teacher."

"The code provided in the course does not work in my R environment."

Can be challenging for learners without a strong background in statistics and math.

"It was definitely a gentle and accessible introduction to time series analysis."

"If you want to see a whole lot of proofs and have someone tell you complicated things are really simple and obvious, then this is the course for you."

"This course requires intermediate math and statistics background for grasping the intuition"

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 Practical Time Series Analysis with these activities:

Review Probability and Statistics

Show steps

Strengthen your foundational knowledge in probability and statistics, essential for understanding time series concepts.

Browse courses on Probability

Show steps

Review textbook chapters or lecture notes on basic probability and statistics.
Solve practice problems to test your understanding.
Identify areas where you need additional support.

Review Time Series Analysis by Forecasting: Methods and Applications

Show steps

Reinforce your understanding of fundamental concepts in time series analysis, building a strong foundation for the course.

View Time Series Analysis: Forecasting and Control... on Amazon

Show steps

Read the first three chapters of the book.
Summarize key concepts in your own words.
Identify areas where you need further clarification.

Organize Course Materials

Show steps

Enhance your learning experience by organizing and reviewing course materials proactively, ensuring better retention and understanding.

Show steps

Create a system to store and organize lecture notes, assignments, and quizzes.
Review the materials regularly to reinforce your learning.
Identify sections that require further clarification or review.

Five other activities

Expand to see all activities and additional details

Show all eight activities

Time Series Visualization Practice

Show steps

Develop proficiency in visualizing time series data, enhancing your ability to identify trends and patterns.

Browse courses on Time Series Analysis

Show steps

Use the R package `ggplot2` to create line plots, scatter plots, and histograms of time series data.
Experiment with different visualization techniques to find the most effective way to represent your data.
Practice identifying patterns and trends in the visualizations.

Time Series Analysis Discussion Group

Show steps

Engage with peers to clarify concepts, share insights, and expand your understanding of time series analysis.

Browse courses on Time Series Analysis

Show steps

Join a study group or online forum dedicated to time series analysis.
Participate in discussions and ask questions.
Share your own insights and knowledge with others.

Applied Time Series Forecasting Tutorial

Show steps

Gain hands-on experience in applying time series forecasting techniques, solidifying your understanding of the course material.

Browse courses on Time Series Forecasting

Show steps

Follow the tutorial on 'Time Series Forecasting with R' from DataCamp.
Complete the exercises in the tutorial.
Apply the techniques to a real-world time series dataset.

Time Series Modeling Practice

Show steps

Develop proficiency in fitting and evaluating time series models, enhancing your ability to analyze and forecast time-dependent data.

Browse courses on Time Series Modeling

Show steps

Use the R package `forecast` to fit ARMA and ARIMA models to time series data.
Experiment with different model parameters and evaluate their performance.
Practice forecasting future values using the fitted models.

Time Series Analysis Project

Show steps

Deepen your understanding of the course material by applying it to a real-world problem, fostering critical thinking and problem-solving abilities.

Browse courses on Time Series Analysis

Show steps

Identify a real-world dataset that exhibits time dependence.
Analyze the data and identify potential patterns and trends.
Select appropriate time series models and fit them to the data.
Evaluate the performance of the models and select the best one.
Write a report summarizing your findings and insights.

Career center

Learners who complete Practical Time Series Analysis will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use data to develop and implement solutions to business problems. This course may be useful for those interested in pursuing a career as a Data Scientist because it provides a foundation in the statistical methods and techniques used in data science. The course also covers topics such as time series analysis, which is a specialized field of data science that is used to analyze data that is collected over time. This course may help you develop the skills and knowledge needed to succeed in this role.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers develop and implement machine learning models to solve business problems. This course may be useful for those interested in pursuing a career as a Machine Learning Engineer because it provides a foundation in the statistical methods and techniques used in machine learning. The course also covers topics such as time series analysis, which is a specialized field of machine learning that is used to analyze data that is collected over time. This course may help you develop the skills and knowledge needed to succeed in this role.

See salaries and explore the career path for Machine Learning Engineer

Data Architect

Data Architects design and implement data management systems. This course may be useful for those interested in pursuing a career as a Data Architect because it provides a foundation in the statistical methods and techniques used in data architecture. The course also covers topics such as time series analysis, which is a specialized field of data architecture that is used to analyze data that is collected over time. This course may help you develop the skills and knowledge needed to succeed in this role.

See salaries and explore the career path for Data Architect

Actuary

Actuaries use mathematical and statistical models to assess risk and uncertainty. This course may be useful for those interested in pursuing a career as an Actuary because it provides a foundation in the statistical methods and techniques used in actuarial science. The course also covers topics such as time series analysis, which is a specialized field of actuarial science that is used to analyze data that is collected over time. This course may help you develop the skills and knowledge needed to succeed in this role.

See salaries and explore the career path for Actuary

Statistician

Statisticians collect, analyze, interpret, and present data. This course may be useful for those interested in pursuing a career as a Statistician because it provides a foundation in the statistical methods and techniques used in statistics. The course also covers topics such as time series analysis, which is a specialized field of statistics that is used to analyze data that is collected over time. This course may help you develop the skills and knowledge needed to succeed in this role.

See salaries and explore the career path for Statistician

Epidemiologist

Epidemiologists investigate the causes and patterns of disease. This course may be useful for those interested in pursuing a career as an Epidemiologist because it provides a foundation in the statistical methods and techniques used in epidemiology. The course also covers topics such as time series analysis, which is a specialized field of epidemiology that is used to analyze data that is collected over time. This course may help you develop the skills and knowledge needed to succeed in this role.

See salaries and explore the career path for Epidemiologist

Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to improve the efficiency of organizations. This course may be useful for those interested in pursuing a career as an Operations Research Analyst because it provides a foundation in the statistical methods and techniques used in operations research. The course also covers topics such as time series analysis, which is a specialized field of operations research that is used to analyze data that is collected over time. This course may help you develop the skills and knowledge needed to succeed in this role.

See salaries and explore the career path for Operations Research Analyst

Database Administrator

Database Administrators manage and maintain databases. This course may be useful for those interested in pursuing a career as a Database Administrator because it provides a foundation in the statistical methods and techniques used in database administration. The course also covers topics such as time series analysis, which is a specialized field of database administration that is used to analyze data that is collected over time. This course may help you develop the skills and knowledge needed to succeed in this role.

See salaries and explore the career path for Database Administrator

Financial Analyst

Financial Analysts use financial data to make investment recommendations. This course may be useful for those interested in pursuing a career as a Financial Analyst because it provides a foundation in the statistical methods and techniques used in financial analysis. The course also covers topics such as time series analysis, which is a specialized field of financial analysis that is used to analyze financial data over time. This course may help you develop the skills and knowledge needed to succeed in this role.

See salaries and explore the career path for Financial Analyst

Quantitative Analyst

Quantitative Analysts use mathematical and statistical modeling to analyze data and make predictions. This course may be useful for those interested in pursuing a career as a Quantitative Analyst because it provides a foundation in the statistical methods and techniques used in quantitative analysis. The course also covers topics such as time series analysis, which is a specialized field of quantitative analysis that is used to analyze data that is collected over time. This course may help you develop the skills and knowledge needed to succeed in this role.

See salaries and explore the career path for Quantitative Analyst

Marketing Analyst

Marketing Analysts use data to understand consumer behavior and develop marketing campaigns. This course may be useful for those interested in pursuing a career as a Marketing Analyst because it provides a foundation in the statistical methods and techniques used in marketing analysis. The course also covers topics such as time series analysis, which is a specialized field of marketing analysis that is used to analyze data that is collected over time. This course may help you develop the skills and knowledge needed to succeed in this role.

See salaries and explore the career path for Marketing Analyst

Data Analyst

Data Analysts collect, clean, and analyze data to help organizations make informed decisions. This course may be useful for those interested in pursuing a career as a Data Analyst because it provides a foundation in the statistical methods and techniques used in data analysis. The course also covers topics such as time series analysis, which is a specialized field of data analysis that is used to analyze data that is collected over time. This course may help you develop the skills and knowledge needed to succeed in this role.

See salaries and explore the career path for Data Analyst

Business Analyst

Business Analysts use data to identify and solve business problems. This course may be useful for those interested in pursuing a career as a Business Analyst because it provides a foundation in the statistical methods and techniques used in business analysis. The course also covers topics such as time series analysis, which is a specialized field of business analysis that is used to analyze data that is collected over time. This course may help you develop the skills and knowledge needed to succeed in this role.

See salaries and explore the career path for Business Analyst

Information Security Analyst

Information Security Analysts protect computer systems and networks from unauthorized access and attacks. This course may be useful for those interested in pursuing a career as an Information Security Analyst because it provides a foundation in the statistical methods and techniques used in information security. The course also covers topics such as time series analysis, which is a specialized field of information security that is used to analyze data that is collected over time. This course may help you develop the skills and knowledge needed to succeed in this role.

See salaries and explore the career path for Information Security Analyst

Software Engineer

Software Engineers design, develop, and implement software applications. This course may be useful for those interested in pursuing a career as a Software Engineer because it provides a foundation in the statistical methods and techniques used in software engineering. The course also covers topics such as time series analysis, which is a specialized field of software engineering that is used to analyze data that is collected over time. This course may help you develop the skills and knowledge needed to succeed in this role.

See salaries and explore the career path for Software Engineer