Model Diagnostics and Remedial Measures from Coursera

This course is best suited for individuals who have a technical background in mathematics/statistics/computer science/engineering pursuing a career change to jobs or industries that are data-driven such as finance, retain, tech, healthcare, government and many more. The opportunity is endless.

This course is part of the Performance Based Admission courses for the Data Science program.

In this course, we will learn what happens to our regression model when these assumptions have not been met. How can we detect these discrepancies in model assumptions and how do we remediate the problems will be addressed in this course.

Upon successful completion of this course, you will be able to:

-describe the assumptions of the linear regression models.

-use diagnostic plots to detect violations of the assumptions of a linear regression model.

-perform a transformation of variables in building regression models.

-use suitable tools to detect and remove heteroscedastic errors.

-use suitable tools to remediate autocorrelation.

-use suitable tools to remediate collinear data.

-perform variable selections and model validations.

What's inside

Syllabus

Module 1: Model Diagnostics and Remediation Part I

Welcome to Model Diagnostics and Remediation Measures! In this course, we will cover the topics of: Regression Diagnostics, Variance Stabilizing Transformations, Box-Cox Transformation, Transformations to Linearized the Model, Weighted Least Squares, Autocorrelation, Multicollinearity, Variable Selection and Model Validation. In Module 1, we will cover four topics including: Regression Diagnostics, Variance Stabilizing Transformations, Box-Cox Transformation and Transformations to Linearize the model. There is a lot to read, watch, and consume in this module so, let’s get started!

Module 2: Model Diagnostics and Remediation Part II

Welcome to Module 2 – This module will cover four topics including: Weighted Least Squares, Autocorrelation, Multicollinearity, and Variable Selection and Model Validation. There is a lot to read, watch, and consume in this module so, let’s get started!

Summative Course Assessment

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

The course belongs to Performance Based Admission Courses for the Data Science program

Engages participants with a highly relevant industry topic--remediation of problems that arise when assumptions of linear regression models are not met

Addresses an industry need for skilled workers in data-driven fields

Taught by instructors with expertise in mathematics, statistics, computer science, and engineering

Requires proficiency in mathematics/statistics/computer science/engineering

Focuses on the practical application of detecting and remediating problems in linear regression models, rather than theoretical underpinnings

Career center

Learners who complete Model Diagnostics and Remedial Measures will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist combines advanced statistical modeling and machine learning techniques to solve complex business problems and derive meaningful insights from data. The course's emphasis on regression diagnostics, variable selection, and model validation enables learners to build robust and accurate predictive models. By developing expertise in these areas, one can excel in data science roles that require a comprehensive understanding of data analysis and model development.

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Statistician

A Statistician collects, analyzes, and interprets data to provide insights and solve problems. The course's focus on regression diagnostics, variable selection, and model validation is essential for statisticians to ensure the accuracy and reliability of their analyses. By developing proficiency in these areas, learners build a strong foundation for conducting rigorous statistical studies and effectively communicating research findings.

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Biostatistician

A Biostatistician applies statistical methods to medical and health-related data to improve public health. The course's focus on regression diagnostics and model validation is crucial for ensuring the accuracy and reliability of biostatistical analyses. By developing proficiency in these areas, learners can contribute to the advancement of medical research and the development of effective healthcare interventions.

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Data Analyst

A Data Analyst collects, processes, and interprets data to identify trends and patterns, utilizing statistical, qualitative, and predictive modeling techniques. The course's coverage of model diagnostics and error remediation provides a solid foundation for addressing discrepancies in data assumptions and enhancing the reliability of data analysis. By mastering these principles, learners develop skills essential for data analysis in various domains.

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Quantitative Analyst

A Quantitative Analyst utilizes statistical and mathematical models to assess financial risks and develop trading strategies. The course's emphasis on regression diagnostics, autocorrelation remediation, and variable selection provides essential skills for building robust financial models and making informed investment decisions. By mastering these techniques, individuals prepare themselves for a successful career in quantitative finance.

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Market Researcher

A Market Researcher gathers and analyzes data to understand consumer behavior and market trends. The course's focus on regression diagnostics and model validation provides a solid foundation for evaluating the accuracy and reliability of market research data. By developing proficiency in these areas, learners enhance their ability to conduct effective market research, identify market opportunities, and make data-driven business decisions.

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Machine Learning Engineer

A Machine Learning Engineer builds and deploys machine learning models to solve complex business problems. The course's emphasis on regression diagnostics and model validation provides a solid foundation for evaluating and improving the performance of machine learning models. By developing proficiency in these areas, machine learning engineers can ensure the accuracy and reliability of their models, leading to more effective outcomes.

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Operations Research Analyst

An Operations Research Analyst uses mathematical and analytical techniques to optimize business processes and improve decision-making. The course's emphasis on regression diagnostics and model validation provides a solid foundation for analyzing and interpreting data, enabling learners to develop efficient and effective operations research models. By mastering these skills, individuals can contribute to improved decision-making and enhance the efficiency of organizations.

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Epidemiologist

An Epidemiologist investigates the causes and distribution of diseases within populations. The course's emphasis on regression diagnostics and heteroscedasticity detection provides valuable skills for analyzing and interpreting epidemiological data. By understanding the assumptions and limitations of regression models, epidemiologists can make more informed conclusions and develop effective public health strategies.

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Financial Analyst

A Financial Analyst evaluates and interprets financial data to make informed investment decisions. The course's focus on regression diagnostics and heteroscedasticity detection provides valuable skills for identifying and mitigating risks in financial models. By understanding the assumptions and limitations of regression models, learners can make more reliable financial projections and contribute to sound investment strategies.

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Actuary

An Actuary assesses and manages risks related to insurance, investments, and pensions. The course's coverage of regression diagnostics and heteroscedasticity detection provides a solid foundation for understanding and mitigating risks within actuarial models. By developing skills in these areas, individuals can enhance the accuracy of their risk assessments and contribute to the financial stability of insurance companies and pension funds.

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Risk Manager

A Risk Manager identifies, assesses, and mitigates potential risks within an organization. The course's coverage of regression diagnostics and heteroscedasticity detection helps learners understand the risks associated with data analysis and modeling. By developing skills in these areas, individuals enhance their ability to assess the reliability of risk models, allocate resources effectively, and develop comprehensive risk management strategies.

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Business Analyst

A Business Analyst bridges the gap between business and technology, translating business requirements into technical solutions. The course's emphasis on regression diagnostics and model validation provides valuable skills for evaluating the effectiveness of business decisions and processes. By understanding the assumptions and limitations of models, business analysts can make more informed recommendations and contribute to the success of their organizations.

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Data Engineer

A Data Engineer designs and builds data pipelines to collect, store, and process data effectively. The course's emphasis on regression diagnostics and model validation provides a solid foundation for understanding and addressing data quality issues. By developing skills in these areas, data engineers can ensure the reliability and integrity of data, enabling organizations to make informed decisions.

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Software Engineer

A Software Engineer designs, develops, and maintains software applications. While not directly related to the specific topics covered in the course, the course's emphasis on logical thinking and problem-solving may be helpful in developing software applications that process and analyze data effectively.

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