Impute Data to Forecast Demand in Google Sheets from Coursera

This course will introduce you to cleaning data and replacing missing values with imputed data to support demand forecasting. Demand forecasts are used to maximize revenue, build efficiencies in operational planning, and to drive future growth. Forecasting techniques can be applied to make realistic predictions of outcomes of everything from how demand affects pricing and sales opportunities to operational planning for electrical utilities and healthcare facilities. We can only have confidence in the demand predictions we produce, when we also have confidence in the data quality feeding those predictions. Ensuring that confidence requires using clean data with no missing values for our forecast models. Handling missing data is an essential part of prepping clean data for a demand forecast. In this course, we will review the principles of applying central measures of tendency and regression techniques to impute missing values. As you clean the data, you will visualize it with charts, replace inconsistent values and impute values while comparing the outcomes of the statistical techniques you have applied. When your data is clean, you will create a demand forecast. You will do this as we work side-by-side in the free-to-use software Google Sheets. By the end of this course, you will understand use cases for imputing missing values and be able to confidently apply multiple statistical imputation techniques in any spreadsheet software. Note: This course works best for learners who are based in the North America region. Weâ€™re currently working on providing the same experience in other regions.

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Provides practical methods for cleaning data and imputation in demand forecasting

Suitable for learners with basic knowledge of data visualization and statistics

Led by an experienced instructor with expertise in data cleaning and forecasting

Concise and easy-to-follow format with hands-on exercises

Focuses on practical applications in various industries

May be less relevant for learners without any data analysis background

Reviews summary

Demand forecasting with imputation in google sheets

This course teaches how to clean data by replacing missing values with imputed data using central measures of tendency and regression techniques. Visualize and clean data in Google Sheets, and create demand forecasts. Course materials and instruction are best-suited for those in the North American region. While reviewers comment that the course is great, they also suggest adding more content and explaining terms more fully.

The course is great.

"Great"

The course should explain terms more.

"Should have explained the terms more."

The course needs more content.

"The course is ok but it needs some more content"

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 Impute Data to Forecast Demand in Google Sheets with these activities:

Find a mentor who can provide guidance on missing data imputation

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Seek guidance from an expert by finding a mentor who can provide personalized advice, support, and insights into the field of missing data imputation.

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Identify individuals in your network or online communities who have expertise in missing data imputation.
Reach out to potential mentors and express your interest in learning about their work.

Review spreadsheet functions

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Sharpen your skills in using spreadsheet functions, which are essential for data cleaning and analysis in demand forecasting.

Browse courses on Google Sheets

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Review the basic functions, such as SUM, AVERAGE, and IF.
Practice using more advanced functions, such as VLOOKUP and HLOOKUP.

Review basic statistics

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Warm up your brain by reviewing basic statistical concepts, covering everything from hypothesis testing to inferential statistics.

Browse courses on Descriptive Statistics

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Revisit concepts of descriptive statistics, such as mean, median, and mode.
Review different types of probability distributions and their applications.
Look over hypothesis testing and practice applying inferential statistical techniques.

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Practice basic descriptive statistics

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Brush up on the basics of descriptive statistics to strengthen your foundation for the course.

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Review the concepts of mean, median, mode, and standard deviation.
Solve practice problems involving descriptive statistics.

Read a book on missing data imputation

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Gain comprehensive knowledge by reading a book dedicated to missing data imputation, providing in-depth coverage of techniques and best practices.

View Data Mining: Know It All on Amazon

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Find a book on missing data imputation that aligns with your interests.
Read the book thoroughly, taking notes and highlighting important concepts.

Follow tutorials on data imputation

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Expand your knowledge of data imputation techniques by following guided tutorials.

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Search for tutorials on different imputation methods.
Follow the steps in the tutorials to apply the methods.

Learn about missing data imputation techniques

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Dig deeper into missing data imputation techniques by seeking out and completing online tutorials to strengthen your understanding.

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Find online tutorials that cover different missing data imputation techniques.
Work through the tutorials and practice using the techniques.

Apply imputation techniques to data sets

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Gain proficiency in applying imputation techniques to real-world data sets, a crucial skill for demand forecasting.

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Import a data set with missing values.
Apply different imputation techniques to handle the missing values.
Compare the results of different imputation methods.

Practice imputing missing values with various techniques

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Enhance your proficiency by practicing imputing missing values using various techniques to improve your accuracy and speed.

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Find practice exercises or online challenges that focus on missing data imputation.
Complete the exercises and compare your results with the provided solutions.
Repeat the exercises until you feel confident in your abilities.

Discuss data cleaning and imputation with peers

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Engage with classmates to exchange ideas and learn from their experiences with data cleaning and imputation.

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Join a study group or online forum.
Initiate discussions or respond to questions related to data cleaning and imputation.

Attend a conference or workshop on data imputation

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Expand your knowledge and connect with experts by attending an event centered around data imputation, offering valuable insights and networking opportunities.

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Find an upcoming conference or workshop on data imputation.
Register and attend the event.
Engage with speakers and attendees to learn about the latest techniques.

Build a demand forecast model

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Apply the concepts learned in the course to create a practical demand forecast model that can be used to make informed business decisions.

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Gather historical demand data.
Select appropriate forecasting techniques.
Develop and evaluate the forecast model.
Present the forecast results.

Impute missing data in a real-world dataset

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Put your skills to the test by selecting a real-world dataset, imputing missing data, and exploring the results to practice and reinforce what you've learned.

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Find a real-world dataset that contains missing data.
Apply different missing data imputation techniques to the dataset.
Explore the results and compare the effectiveness of different techniques.

Create a blog post or video tutorial on missing data imputation

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Solidify your understanding by creating a blog post or video tutorial that explains missing data imputation techniques, reinforcing your knowledge while potentially educating others.

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Choose a specific missing data imputation technique to focus on.
Write a detailed blog post or create a video tutorial explaining the technique.
Share your content with others and gather feedback.

Contribute to an open-source project related to missing data imputation

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Engage in practical application by contributing to an open-source project that focuses on missing data imputation, gaining hands-on experience while supporting the community.

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Find an open-source project related to missing data imputation.
Join the project's community and contribute in ways that align with your skills and interests.

Career center

Learners who complete Impute Data to Forecast Demand in Google Sheets will develop knowledge and skills that may be useful to these careers:

Demand Planner

Demand Planners are responsible for developing and maintaining demand forecasts that guide production, inventory management, and sales strategies. This course is highly relevant as it focuses specifically on imputing missing values to support demand forecasting. By gaining proficiency in these techniques, you can enhance the accuracy of your demand predictions and make more informed decisions to optimize supply chain operations.

See salaries and explore the career path for Demand Planner

Data Analyst

Data Analysts play a crucial role in ensuring data quality and accuracy, which is essential for demand forecasting. This course provides a solid foundation in data cleaning and imputation techniques, equipping you with the skills to handle missing values effectively. By mastering these techniques, you can contribute to reliable demand forecasts that drive informed decision-making and business growth.

See salaries and explore the career path for Data Analyst

Market Research Analyst

Market Research Analysts collect and analyze data to understand market trends and consumer behavior. This course provides valuable insights into data cleaning and imputation techniques, which are essential for ensuring the quality and reliability of market research data. By leveraging these techniques, you can gain deeper insights into market dynamics and make more accurate predictions to support business decisions.

See salaries and explore the career path for Market Research Analyst

Data Engineer

Data Engineers design and build data management systems. This course provides a foundation in data cleaning and imputation techniques, which are essential for ensuring the quality of data in these systems. By mastering these techniques, you can contribute to the development of more reliable and efficient data management systems.

See salaries and explore the career path for Data Engineer

Statistician

Statisticians collect, analyze, and interpret data to draw meaningful conclusions. This course provides a foundation in data cleaning and imputation techniques, which are essential for ensuring the quality of statistical data. By mastering these techniques, you can enhance the accuracy of your analysis and make more informed inferences to support decision-making.

See salaries and explore the career path for Statistician

Quantitative Analyst

Quantitative Analysts develop and implement mathematical models to assess financial risks and make investment decisions. This course provides a foundation in data cleaning and imputation techniques, which are essential for ensuring the quality of financial data. By mastering these techniques, you can enhance the accuracy of your models and make more informed investment decisions.

See salaries and explore the career path for Quantitative Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to solve complex business problems. This course complements this role by providing a foundation in data cleaning and imputation techniques, which are crucial for ensuring the accuracy of data used in optimization models. By mastering these techniques, you can enhance the effectiveness of your models and improve decision-making for operational efficiency.

See salaries and explore the career path for Operations Research Analyst

Data Scientist

Data Scientists leverage data to extract insights and build predictive models. This course provides a foundation in data cleaning and imputation techniques, which are essential for preparing high-quality data for analysis and modeling. By mastering these techniques, you can contribute to more accurate and reliable models, leading to better predictions and more informed decision-making.

See salaries and explore the career path for Data Scientist

Business Analyst

Business Analysts identify and analyze business needs and develop solutions to improve processes and operations. This course is relevant as it provides a foundation in data cleaning and imputation techniques, which are essential for ensuring the quality of data used in business analysis. By gaining proficiency in these techniques, you can contribute to more accurate and reliable analysis, leading to better decision-making and improved business outcomes.

See salaries and explore the career path for Business Analyst

Financial Analyst

Financial Analysts evaluate and make recommendations on financial investments. This course may be useful as it provides a foundation in data cleaning and imputation techniques, which are essential for ensuring the quality of financial data. By gaining proficiency in these techniques, you can enhance the accuracy of your analysis and make more informed investment decisions.

See salaries and explore the career path for Financial Analyst

Operations Manager

Operations Managers oversee the day-to-day operations of an organization. This course may be useful as it provides a foundation in data cleaning and imputation techniques, which are essential for ensuring the quality of operational data. By gaining proficiency in these techniques, you can enhance the accuracy of your performance analysis and make more informed decisions to optimize operations.

See salaries and explore the career path for Operations Manager

Product Manager

Product Managers oversee the development and launch of new products. This course may be useful as it provides a foundation in data cleaning and imputation techniques, which are essential for ensuring the quality of product data. By gaining proficiency in these techniques, you can enhance the accuracy of your product performance analysis and make more informed decisions to optimize product development and launch strategies.

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Actuary

Actuaries assess and manage financial risks. This course may be useful as it provides a foundation in data cleaning and imputation techniques, which are essential for ensuring the quality of actuarial data. By gaining proficiency in these techniques, you can enhance the accuracy of your risk assessments and make more informed decisions to protect against financial losses.

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Marketing Manager

Marketing Managers develop and execute marketing campaigns to promote products and services. This course may be useful as it provides a foundation in data cleaning and imputation techniques, which are essential for ensuring the quality of marketing data. By gaining proficiency in these techniques, you can enhance the accuracy of your campaign performance analysis and make more informed decisions to optimize marketing efforts.

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Software Engineer

Software Engineers design and develop software applications. This course may be useful as it provides a foundation in data cleaning and imputation techniques, which are essential for ensuring the quality of software data. By gaining proficiency in these techniques, you can enhance the accuracy of your software testing and debugging efforts, leading to more reliable and efficient software.

See salaries and explore the career path for Software Engineer