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Impute Data to Forecast Demand in Google Sheets

Tricia Bagley
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...
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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. We’re currently working on providing the same experience in other regions.
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Good to know

Know what's good
, what to watch for
, and possible dealbreakers
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

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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

Coming soon We're preparing activities for Impute Data to Forecast Demand in Google Sheets. These are activities you can do either before, during, or after a course.

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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.

Reading list

We've selected 11 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 Impute Data to Forecast Demand in Google Sheets.
Provides a comprehensive overview of statistical learning, covering a wide range of topics from statistical learning concepts to statistical learning applications. It valuable resource for anyone who wants to learn more about statistical learning.
Provides a comprehensive overview of forecasting principles and practice, covering a wide range of topics from data collection to model selection. It valuable resource for anyone who wants to learn more about forecasting.
Provides a comprehensive overview of machine learning with Scikit-Learn, Keras, and TensorFlow, covering a wide range of topics from machine learning concepts to machine learning applications. It valuable resource for anyone who wants to learn more about machine learning with Scikit-Learn, Keras, and TensorFlow.
Provides a comprehensive overview of missing data imputation methods, including both frequentist and Bayesian approaches. It valuable reference for anyone who needs to deal with missing data in their research.
Provides a comprehensive overview of data science, covering a wide range of topics from data science concepts to data science applications. It valuable resource for anyone who wants to learn more about data science.
Provides a practical overview of demand forecasting techniques, including both qualitative and quantitative methods. It valuable resource for anyone who needs to develop or improve their demand forecasting skills.
Provides a comprehensive overview of machine learning for data mining, covering a wide range of topics from machine learning algorithms to machine learning applications. It valuable resource for anyone who wants to learn more about machine learning for data mining.
Provides a comprehensive overview of Python for data analysis, covering a wide range of topics from Python basics to Python for data analysis applications. It valuable resource for anyone who wants to learn more about Python for data analysis.
Provides a comprehensive overview of R for data science, covering a wide range of topics from R basics to R for data science applications. It valuable resource for anyone who wants to learn more about R for data science.
Provides a comprehensive overview of data analysis for business and economics, covering a wide range of topics from data collection to data visualization. It valuable resource for anyone who wants to learn more about data analysis for business and economics.
Provides a practical introduction to data visualization, covering a wide range of topics from data visualization basics to data visualization applications. It valuable resource for anyone who wants to learn more about data visualization.

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