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Google Cloud Training
This is a self-paced lab that takes place in the Google Cloud console. Time series forecasting can be framed as a supervised learning problem in which series of data points are indexed in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Analyzing such points allows for a machine learning model to identify patterns present on continuum data.
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What's inside
Syllabus
Understand, Explore, and Visualize a Time Series Dataset
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops skills in time series forecasting, which is a core skill for data scientists and machine learning engineers
Provides hands-on experience through self-paced labs, which reinforces understanding of concepts
Taught by Google Cloud Training, who are recognized for their expertise in cloud computing and machine learning
Covers the fundamentals of time series forecasting, providing a strong foundation for beginners
Focuses on practical applications, making it relevant to industry professionals
Save this course
Save Understand, Explore, and Visualize a Time Series Dataset 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 Understand, Explore, and Visualize a Time Series Dataset with these
activities:
Form a Time Series Study Group
Show steps
Enhance understanding through collaborative learning, by forming a study group to discuss concepts, solve problems, and share insights related to time series analysis and forecasting.
Browse courses on
Time Series Analysis
Show steps
-
Find other students interested in forming a study group
-
Schedule regular meetings and set study goals
-
Review course material, discuss concepts, and work on problems together
Review Statistics
Show steps
Review statistical concepts such as probability, hypothesis testing, and regression analysis to strengthen foundational knowledge and prepare for the course's quantitative nature.
Browse courses on
Statistical Modeling
Show steps
-
Go over basic probability distributions (e.g., normal, binomial, Poisson)
-
Practice hypothesis testing using statistical software
-
Review linear regression models and their assumptions
Explore Cloud Tools for Time Series Analysis
Show steps
Familiarize yourself with cloud-based tools for time series analysis, such as Google Cloud's BigQuery and AutoML, to enhance practical knowledge and prepare for real-world applications.
Browse courses on
Cloud Computing
Show steps
-
Create a Google Cloud account and explore the available services
-
Go through tutorials on using BigQuery for time series data analysis
-
Experiment with AutoML for time series forecasting
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore Time Series Analysis Techniques
Show steps
Enhance understanding of time series analysis techniques through guided tutorials, covering concepts such as stationarity, autocorrelation, and decomposition.
Browse courses on
Time Series Analysis
Show steps
-
Go through tutorials on identifying and dealing with non-stationarity
-
Practice decomposing time series using methods like moving averages and exponential smoothing
-
Learn about different time series forecasting models (e.g., ARIMA, SARIMA)
Attend a Time Series Analysis Workshop
Show steps
Participate in a workshop to interact with experts, learn from case studies, and refine skills in time series analysis and forecasting.
Browse courses on
Time Series Analysis
Show steps
-
Register for the workshop and prepare beforehand
-
Attend the workshop and actively engage with the content
-
Follow up after the workshop to reinforce learning
Create a Time Series Visualization
Show steps
Develop a deeper understanding of time series data by creating visualizations that showcase patterns, trends, and anomalies.
Browse courses on
Data Visualization
Show steps
-
Choose an appropriate visualization technique (e.g., line chart, scatter plot, heat map)
-
Prepare the time series data for visualization
-
Create the visualization using a tool like Plotly or Matplotlib
-
Interpret and analyze the visualization, identifying key insights
Solve Time Series Forecasting Problems
Show steps
Reinforce understanding of time series forecasting models by solving practice problems involving data pre-processing, model selection, and evaluation.
Browse courses on
Time Series Forecasting
Show steps
-
Practice forecasting time series using real-world datasets
-
Evaluate the performance of different forecasting models
-
Identify and address common challenges in time series forecasting
Complete a Time Series Forecasting Project
Show steps
Apply the skills learned to a capstone project, demonstrating proficiency in time series forecasting and analysis.
Browse courses on
Time Series Forecasting
Show steps
-
Define the problem and gather the necessary data
-
Pre-process and explore the time series data
-
Select and apply appropriate forecasting models
-
Evaluate and compare the forecasting performance
-
Write a report summarizing the project and findings
Form a Time Series Study Group
Show steps
Enhance understanding through collaborative learning, by forming a study group to discuss concepts, solve problems, and share insights related to time series analysis and forecasting.
Browse courses on
Time Series Analysis
Show steps
Show steps
Show steps
- Find other students interested in forming a study group
- Schedule regular meetings and set study goals
- Review course material, discuss concepts, and work on problems together
Review Statistics
Show steps
Review statistical concepts such as probability, hypothesis testing, and regression analysis to strengthen foundational knowledge and prepare for the course's quantitative nature.
Browse courses on
Statistical Modeling
Show steps
Show steps
Show steps
- Go over basic probability distributions (e.g., normal, binomial, Poisson)
- Practice hypothesis testing using statistical software
- Review linear regression models and their assumptions
Explore Cloud Tools for Time Series Analysis
Show steps
Familiarize yourself with cloud-based tools for time series analysis, such as Google Cloud's BigQuery and AutoML, to enhance practical knowledge and prepare for real-world applications.
Browse courses on
Cloud Computing
Show steps
Show steps
Show steps
- Create a Google Cloud account and explore the available services
- Go through tutorials on using BigQuery for time series data analysis
- Experiment with AutoML for time series forecasting
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore Time Series Analysis Techniques
Show steps
Enhance understanding of time series analysis techniques through guided tutorials, covering concepts such as stationarity, autocorrelation, and decomposition.
Browse courses on
Time Series Analysis
Show steps
Show steps
Show steps
- Go through tutorials on identifying and dealing with non-stationarity
- Practice decomposing time series using methods like moving averages and exponential smoothing
- Learn about different time series forecasting models (e.g., ARIMA, SARIMA)
Attend a Time Series Analysis Workshop
Show steps
Participate in a workshop to interact with experts, learn from case studies, and refine skills in time series analysis and forecasting.
Browse courses on
Time Series Analysis
Show steps
Show steps
Show steps
- Register for the workshop and prepare beforehand
- Attend the workshop and actively engage with the content
- Follow up after the workshop to reinforce learning
Create a Time Series Visualization
Show steps
Develop a deeper understanding of time series data by creating visualizations that showcase patterns, trends, and anomalies.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose an appropriate visualization technique (e.g., line chart, scatter plot, heat map)
- Prepare the time series data for visualization
- Create the visualization using a tool like Plotly or Matplotlib
- Interpret and analyze the visualization, identifying key insights
Solve Time Series Forecasting Problems
Show steps
Reinforce understanding of time series forecasting models by solving practice problems involving data pre-processing, model selection, and evaluation.
Browse courses on
Time Series Forecasting
Show steps
Show steps
Show steps
- Practice forecasting time series using real-world datasets
- Evaluate the performance of different forecasting models
- Identify and address common challenges in time series forecasting
Complete a Time Series Forecasting Project
Show steps
Apply the skills learned to a capstone project, demonstrating proficiency in time series forecasting and analysis.
Browse courses on
Time Series Forecasting
Show steps
Show steps
Show steps
- Define the problem and gather the necessary data
- Pre-process and explore the time series data
- Select and apply appropriate forecasting models
- Evaluate and compare the forecasting performance
- Write a report summarizing the project and findings
Career center
Learners who complete Understand, Explore, and Visualize a Time Series Dataset will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists develop, maintain, and deploy machine learning and predictive models that help organizations identify trends, risks, and opportunities in their data. The ability to analyze time series data is an essential skill for Data Scientists who want to build accurate models. This course in Time Series Forecasting can assist aspiring Data Scientists by providing them with a solid understanding of time series analysis best practices. Course topics like visualizing time series, analyzing time series patterns, and forecasting techniques are some fundamental capabilities of Data Scientists.
Statistician
Statistician
Statisticians apply mathematical and statistical methods to collect, analyze, interpret, and present data. Time series analysis is a crucial skill for Statisticians who need to identify patterns and trends in data over time. This course in Time Series Forecasting can greatly benefit Statisticians by offering them hands-on practice with time series modeling and forecasting. The course can enhance their ability to draw meaningful insights from time-based data, which is a critical component of many statistical analyses.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make informed decisions. Understanding time series data is becoming increasingly important for Data Analysts due to the prevalence of IoT (Internet of Things) devices that generate vast amounts of time-stamped data. This course in Time Series Forecasting can be valuable for Data Analysts who want to specialize in time series analysis or enhance their skills in handling temporal data. It can provide them with the tools and techniques they need to extract insights from time series data, a skill that is in high demand across industries.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models that solve real-world problems. Time series forecasting is a common application of machine learning, and it's crucial for Machine Learning Engineers to have a strong understanding of time series analysis techniques. This course in Time Series Forecasting can provide Machine Learning Engineers with a comprehensive overview of the field, including best practices for data preparation, model selection, and evaluation. Furthermore, the course can help Machine Learning Engineers build a solid foundation for developing accurate and reliable time series forecasting models, which is essential for success in this role.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data management systems and processes. Time series data is often stored in time-series databases, and Data Engineers need to have the skills to manage and analyze this type of data. This course in Time Series Forecasting can be beneficial for Data Engineers who want to develop expertise in time series management, as it provides hands-on practice with time series data ingestion, storage, and analysis. The course can also help Data Engineers build a foundation for developing scalable and efficient data pipelines for handling time series data.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts analyze financial data and make investment decisions using mathematical and statistical models. Time series forecasting is a key skill for Quantitative Analysts who need to predict future financial trends. This course in Time Series Forecasting can benefit Quantitative Analysts by providing them with a solid understanding of the principles and techniques of time series analysis. Course topics such as identifying trends, seasonality, and forecasting techniques can directly enhance their ability to make informed investment decisions based on historical financial data.
Business Analyst
Business Analyst
Business Analysts help businesses define their goals, analyze data, and make recommendations for improvements. Time series data is becoming increasingly important for Business Analysts, as many organizations collect large amounts of time-stamped data from various sources such as sales transactions, customer interactions, and production processes. This course in Time Series Forecasting can be helpful for Business Analysts who want to develop skills in analyzing and interpreting time series data. The course can help them identify trends and patterns, forecast future outcomes, and make data-driven recommendations for business improvements.
Financial Analyst
Financial Analyst
Financial Analysts evaluate the financial performance of companies and make investment recommendations. Time series forecasting is a crucial skill for Financial Analysts, as it allows them to predict future financial trends based on historical data. This course in Time Series Forecasting can benefit Financial Analysts by providing them with the tools and techniques for analyzing and forecasting time series data, such as identifying trends, seasonality, and cycles. The course can contribute to their ability to make sound investment decisions and provide valuable insights to clients.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex business problems. Time series analysis is a common technique used by Operations Research Analysts to optimize processes and improve efficiency. This course in Time Series Forecasting can be valuable for Operations Research Analysts who want to develop expertise in this area. The course provides a comprehensive understanding of time series analysis concepts and practical experience in applying these concepts to real-world problems. By taking this course, Operations Research Analysts can enhance their ability to analyze time series data, identify patterns and trends, and develop data-driven solutions for business challenges.
Actuary
Actuary
Actuaries assess and manage financial risks for insurance companies and other organizations. Time series forecasting is a crucial skill for Actuaries who need to predict future trends in mortality, morbidity, and other insurance-related factors. This course in Time Series Forecasting can benefit Actuaries by providing them with the knowledge and skills to analyze and forecast time series data. The course covers topics such as time series decomposition, trend analysis, and forecasting techniques, which are essential for Actuaries to effectively manage financial risks and make informed decisions.
Economist
Economist
Economists study the production, distribution, and consumption of goods and services. Time series forecasting is a valuable tool for Economists who need to predict future economic trends, such as GDP growth, inflation, and unemployment rates. By understanding time series analysis techniques, Economists can make informed predictions and develop policies to influence the economy.
Marketing Analyst
Marketing Analyst
Marketing Analysts research and analyze market data to help businesses develop and implement marketing strategies. Time series forecasting can be useful for Marketing Analysts who need to predict future trends in consumer behaviour, sales, and other marketing-related metrics. This course in Time Series Forecasting can benefit Marketing Analysts by providing them with the skills and knowledge to analyze and interpret time series data. By taking this course, Marketing Analysts can gain a deeper understanding of time series analysis concepts and techniques, and apply these skills to effectively plan and execute marketing campaigns.
Risk Analyst
Risk Analyst
Risk Analysts identify, assess, and mitigate risks for organizations. Time series data can be used to identify and predict potential risks, such as supply chain disruptions, financial crises, and natural disasters. By understanding time series analysis techniques, Risk Analysts can effectively analyze historical data to assess risks and develop mitigation strategies.
Sales Analyst
Sales Analyst
Sales Analysts track and analyze sales data to identify trends and opportunities for improving sales performance. Time series forecasting can be helpful for Sales Analysts who need to predict future sales and plan for future demand.
Supply Chain Analyst
Supply Chain Analyst
Supply Chain Analysts analyze and manage supply chains to ensure efficient and cost-effective delivery of goods and services. Time series data can be useful for Supply Chain Analysts who need to forecast demand, optimize inventory levels, and plan transportation routes. This course in Time Series Forecasting can benefit Supply Chain Analysts by providing them with the knowledge and skills to analyze and interpret time series data.
For more career information including salaries, visit:
OpenCourser.com/course/sgltda/understand
Reading list
We've selected six 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
Understand, Explore, and Visualize a Time Series Dataset.
Save
Comprehensive guide to forecasting methods, covering both classical and modern approaches. The authors provide a thorough discussion of the underlying principles of forecasting and the practical considerations involved in implementing forecasting models. The book is well-written and full of useful examples and case studies.
Save
Classic text on time series analysis. The authors provide a comprehensive overview of the field, covering both theoretical and practical aspects. The book is written in a clear and concise style, making it a valuable reference for both students and practitioners.
Save
This introductory text covers both the theoretical and practical aspects of time series analysis. It includes comprehensive examples and exercises to reinforce learning.
Provides a concise introduction to time series forecasting. The authors cover a wide range of topics, including stationarity, autocorrelation, and forecasting methods. The book is written in a clear and concise style, making it a valuable resource for students and practitioners.
Save
Covers a range of time series analysis methods, utilizing the R software environment. Readers will appreciate the use of real-world examples and case studies throughout the text.
Focuses on the practical applications of time series analysis in business forecasting. It makes use of real-world examples to illustrate the concepts and techniques discussed throughout the book.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/sgltda/understand
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Effort
90 minutes
Level
Introductory
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Good to know
Develops skills in time series forecasting, which is a core skill for data scientists and machine learning engineers
Provides hands-on experience through self-paced labs, which reinforces understanding of concepts
Taught by Google Cloud Training, who are recognized for their expertise in cloud computing and machine learning
Covers the fundamentals of time series forecasting, providing a strong foundation for beginners
Focuses on practical applications, making it relevant to industry professionals
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