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Evaluating a Data Model

This is a self-paced lab that takes place in the Google Cloud console. Learn the process for partitioning a data set into a training set that will be used to develop a model, and a test set that can then be used to evaluate the accuracy of the model and then independently evaluate predictive models in a repeatable manner.

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Provides a solid foundation for students interested in machine learning

This course uses Google Cloud console for hands-on experience, which is industry-standard

Meant for self-paced learning, accommodates diverse schedules

Requires some familiarity with data science concepts

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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 Evaluating a Data Model with these activities:

Review data set partitioning and model evaluation concepts

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Reinforce knowledge of data set partitioning and model evaluation to strengthen the foundation for the course.

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Review lecture notes and textbooks on data set partitioning and model evaluation.
Complete practice problems or online quizzes to test understanding.

Complete coding exercises on data set partitioning and model evaluation

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Gain hands-on experience with data set partitioning and model evaluation through guided exercises.

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Set up a development environment for Python programming.
Work through coding exercises provided in the course materials or online resources.
Debug and refine code to improve accuracy and efficiency.

Participate in a peer study group for data set partitioning and model evaluation

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Enhance understanding and develop collaborative skills by engaging in discussions and knowledge sharing with peers.

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Find or form a study group with peers who share similar interests.
Establish a regular meeting schedule and set clear goals for each session.
Take turns presenting concepts, leading discussions, and providing feedback to each other.

Four other activities

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Attend a workshop on data science project implementation

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Gain practical insights and hands-on experience in implementing data science projects through a structured workshop.

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Identify and register for a relevant workshop focused on data science project implementation.
Attend the workshop, actively participate in discussions, and take notes.
Apply the knowledge and skills gained to personal projects or assignments.

Develop a presentation on best practices for model evaluation

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Enhance understanding and communication skills by creating a presentation on effective model evaluation techniques.

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Research and gather information on best practices for model evaluation.
Organize and structure the content into a clear and engaging presentation.
Create visual aids, such as charts and graphs, to illustrate key concepts.
Practice delivering the presentation to receive feedback and improve delivery.

Mentor a junior learner in data set partitioning and model evaluation

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Solidify understanding and develop communication skills by guiding and supporting a junior learner in the subject matter.

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Identify a junior learner who is seeking guidance in data set partitioning and model evaluation.
Establish regular communication and provide support through discussions, code reviews, or project assistance.
Share knowledge, resources, and insights to facilitate the learner's progress.

Contribute to an open-source project related to data set partitioning or model evaluation

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Deepen understanding and gain practical experience by actively contributing to an open-source project in the field.

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Identify an open-source project that aligns with interests and skill level.
Join the project community, engage in discussions, and review code.
Identify an issue or feature that can be addressed through a contribution.
Develop and submit a pull request with a proposed solution.

Career center

Learners who complete Evaluating a Data Model will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts build and manage data models, and are responsible for organizing, cleaning, and visualizing data. This course can help build a foundation for Data Analysts by enabling them to independently evaluate the accuracy of predictive models in an unbiased and repeatable manner. This course is particularly relevant to Data Analysts because it will help them to ensure the accuracy of the data models they build, which is essential for making informed decisions.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Scientists collect, analyze, and interpret data to extract meaningful insights. Like Data Analysts, they build and manage data models. This course is especially helpful to Data Scientists because it provides instruction on the thorough evaluation of data models. This is a key skill for Data Scientists to have, as it allows them to determine which data models are most accurate and effective for a given problem.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers develop and deploy machine learning models to solve real-world problems. This course is useful for Machine Learning Engineers because it provides a framework for thoroughly evaluating the accuracy of models, which is essential for ensuring the reliability and effectiveness of the machine learning solutions they develop.

See salaries and explore the career path for Machine Learning Engineer

Statistician

Statisticians collect, analyze, and interpret data to provide insights and make predictions. This course may be helpful to Statisticians by providing them with a process for independently evaluating the accuracy of predictive models. This skill can be valuable for Statisticians who work in fields such as finance, healthcare, or marketing research, where accurate predictions are critical.

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Data Engineer

Data Engineers build and maintain the infrastructure that stores and processes data. This course may be helpful to Data Engineers by providing them with a better understanding of how to evaluate the accuracy of data models. This skill can be valuable for ensuring that the data infrastructure they build is reliable and efficient.

See salaries and explore the career path for Data Engineer

Business Analyst

Business Analysts use data to identify and solve business problems. This course may be useful to Business Analysts by providing them with a framework for evaluating the accuracy and effectiveness of data-driven solutions. This can help Business Analysts to make more informed and effective recommendations to stakeholders.

See salaries and explore the career path for Business Analyst

Software Engineer

Software Engineers design, develop, and maintain software applications. This course may be helpful to Software Engineers who work on data-driven applications by providing them with a better understanding of how to evaluate the accuracy and performance of data models. This can help Software Engineers to develop more reliable and efficient software applications.

See salaries and explore the career path for Software Engineer

Product Manager

Product Managers are responsible for the development and success of products. This course may be useful to Product Managers who work on data-driven products by providing them with a framework for evaluating the accuracy and effectiveness of data models. This can help Product Managers to make more informed decisions about product development and marketing.

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Database Administrator

Database Administrators manage and maintain databases. This course may be useful to Database Administrators by providing them with a better understanding of how to evaluate the accuracy and performance of data models. This can help Database Administrators to ensure that the databases they manage are reliable and efficient.

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Data Architect

Data Architects design and build data architectures to meet the needs of businesses. This course may be helpful to Data Architects by providing them with a better understanding of how to evaluate the accuracy and performance of data models. This can help Data Architects to design and build more reliable and efficient data architectures.

See salaries and explore the career path for Data Architect

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. This course may be helpful to Quantitative Analysts by providing them with a better understanding of how to evaluate the accuracy and performance of data models. This can help Quantitative Analysts to develop more reliable and accurate financial models.

See salaries and explore the career path for Quantitative Analyst

Actuary

Actuaries use mathematical and statistical models to assess risk and uncertainty. This course may be helpful to Actuaries by providing them with a better understanding of how to evaluate the accuracy and performance of data models. This can help Actuaries to develop more accurate and reliable risk and uncertainty assessments.

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Economist

Economists use economic models to analyze economic data and make predictions about the economy. This course may be helpful to Economists by providing them with a better understanding of how to evaluate the accuracy and performance of data models. This can help Economists to develop more accurate and reliable economic models.

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Risk Manager

Risk Managers identify, assess, and manage risks to organizations. This course may be helpful to Risk Managers by providing them with a better understanding of how to evaluate the accuracy and performance of data models. This can help Risk Managers to make more informed and effective risk management decisions.

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Financial Analyst

Financial Analysts use financial models to analyze and make recommendations about investments. This course may be helpful to Financial Analysts by providing them with a better understanding of how to evaluate the accuracy and performance of data models. This can help Financial Analysts to develop more accurate and reliable financial models.

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Reading list

We've selected 14 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 Evaluating a Data Model.

Deep Learning

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This comprehensive textbook provides an in-depth look at deep learning theory and applications, making it ideal for those seeking a deep understanding of the field.

Evaluating a Data Model

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