Experimental Design for Data Analysis from Pluralsight

This course covers conceptual and practical aspects of building and evaluating machine learning models in a way that uses data judiciously, while also accounting for considerations such as ordering and relationships within data and other biases.

Providing crisp, clear, actionable points-of-view to senior executives is becoming an increasingly important role of data scientists and data professionals these days. Now, a point-of-view must represent a hypothesis, ideally backed by data. In this course, Experimental Design for Data Analysis, you will gain the ability to construct such hypotheses from data and use rigorous frameworks to test whether they hold true. First, you will learn how inferential statistics and hypothesis testing form the basis of data modeling and machine learning. Next, you will discover how the process of building machine learning models is akin to that of designing an experiment and how training and validation techniques help rigorously evaluate the results of such experiments. Then, you will round out the course by studying various forms of cross-validation, including both singular and iterative techniques to cope with independent, identically distributed data and grouped data. Finally, you will also learn how you can refine your models using these techniques with hyperparameter tuning. When you’re finished with this course, you will have the skills and knowledge to build and evaluate models, specifically including machine learning models, using rigorous cross-validation frameworks and hyperparameter tuning.

What's inside

Syllabus

Course Overview

Designing an Experiment for Data Analysis

Building and Training a Machine Learning Model

Understanding and Overcoming Common Problems in Data Modeling

Leveraging Different Validation Strategies in Data Modeling

Tuning Hyperparameters Using Cross Validation Scores

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches modern machine learning techniques, which are essential in modern industry

Relevant for data scientists and data professionals, who are increasingly providing point-of-views backed by data

Led by instructors Janani Ravi, who bring industry expertise

Requires students to come in with background knowledge of inferential statistics and hypothesis testing

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 Experimental Design for Data Analysis with these activities:

Review basic probability and statistics concepts

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Strengthen your foundation by revisiting basic probability and statistics concepts, ensuring a solid understanding of the building blocks for experimental design and model evaluation.

Browse courses on Probability

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Identify areas where you need to refresh your knowledge
Review textbooks or online resources
Solve practice problems to reinforce your understanding

Review 'Applied Predictive Modeling' by Max Kuhn and Kjell Johnson

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Expand your knowledge by delving into a comprehensive resource that provides in-depth insights into predictive modeling techniques, enhancing your understanding of the concepts covered in this course.

View Tidy Modeling with R: A Framework for Modeling... on Amazon

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Read chapters relevant to experimental design and model evaluation
Take notes and summarize key concepts
Apply what you learn to your coursework

Explore tutorials on hypothesis testing and inferential statistics

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Enhance your foundational knowledge by seeking out tutorials that provide clear explanations and examples of hypothesis testing and inferential statistics, strengthening your ability to make data-driven decisions.

Browse courses on Hypothesis Testing

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Identify tutorials that align with the course material
Set aside dedicated time for tutorial exploration
Take notes and revisit key concepts regularly
Apply what you learn to real-world examples

Five other activities

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Engage in peer discussions on experimental design and model evaluation

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Foster your understanding by engaging in thought-provoking discussions with peers. Exchange ideas, challenge perspectives, and deepen your knowledge of experimental design and model evaluation.

Browse courses on Experimental Design

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Identify peers with similar interests
Set up regular discussion sessions
Prepare questions and share insights
Actively listen and engage in constructive dialogue

Practice using training and validation techniques

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Engage in hands-on practice to solidify your understanding of training and validation techniques, improving your ability to evaluate machine learning models effectively.

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Create a dataset and split it into training and validation sets
Train a machine learning model using the training set
Evaluate the model's performance on the validation set
Adjust the model's parameters and retrain it
Repeat steps 2-4 until satisfied with the model's performance

Compile a resource list on experimental design and model evaluation techniques

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Enhance your knowledge base by compiling a curated list of resources that cover experimental design and model evaluation techniques, providing easy access to valuable materials.

Browse courses on Experimental Design

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Conduct research to identify relevant resources
Organize resources into categories or topics
Include brief descriptions or annotations for each resource
Share your compilation with peers or the broader learning community

Develop a case study demonstrating the application of cross-validation techniques

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Deepen your understanding of cross-validation by creating a case study that showcases its practical application. This will enhance your ability to effectively evaluate and select machine learning models.

Browse courses on Machine Learning Applications

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Define the research question and gather relevant data
Choose appropriate cross-validation techniques
Implement the techniques and analyze the results
Draw conclusions and make recommendations
Present your findings in a clear and concise case study

Create a blog post or article on the importance of data analysis in decision-making

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Solidify your understanding by creating a written piece that explores the significance of data analysis in making informed decisions, reinforcing the concepts learned in the course.

Browse courses on Data Analysis

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Research and gather relevant information
Outline the structure of your article or blog post
Write clear and engaging content
Proofread and edit your work
Publish and promote your article or blog post

Career center

Learners who complete Experimental Design for Data Analysis will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists play a crucial role in extracting meaningful insights from data and using them to drive business decisions. This course, Experimental Design for Data Analysis, is a valuable resource for those aspiring to become Data Scientists, as it provides a strong foundation in the principles of experimental design, hypothesis testing, and model evaluation. By understanding how to design and conduct experiments, Data Scientists can ensure that their models are accurate and reliable, leading to more effective decision-making.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers are responsible for building and deploying machine learning models, which require a deep understanding of experimental design and model evaluation techniques. This course provides a comprehensive overview of these concepts, giving Machine Learning Engineers the skills they need to develop and implement effective machine learning solutions. The emphasis on cross-validation and hyperparameter tuning is particularly valuable for Machine Learning Engineers, as it helps them optimize their models for real-world applications.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts are tasked with analyzing data to identify trends, patterns, and insights that can inform business decisions. This course, Experimental Design for Data Analysis, is highly relevant for Data Analysts, as it provides a rigorous framework for designing and conducting experiments, ensuring that their analyses are statistically valid and reliable. The focus on understanding common problems in data modeling and leveraging different validation strategies is also valuable for Data Analysts, as it helps them mitigate potential biases and improve the accuracy of their findings.

See salaries and explore the career path for Data Analyst

Business Analyst

Business Analysts work closely with stakeholders to understand their business needs and develop solutions that meet those needs. This course, Experimental Design for Data Analysis, can be highly beneficial for Business Analysts, as it provides a structured approach to gathering and analyzing data, testing hypotheses, and evaluating the results. By understanding the principles of experimental design, Business Analysts can make more informed decisions and deliver more effective solutions to their clients.

See salaries and explore the career path for Business Analyst

Statistician

Statisticians are experts in the collection, analysis, and interpretation of data, making them essential for a wide range of industries. This course, Experimental Design for Data Analysis, is a valuable resource for Statisticians, as it provides a comprehensive overview of the principles of experimental design, hypothesis testing, and model evaluation. By understanding how to design and conduct experiments, Statisticians can ensure that their analyses are statistically valid and reliable, leading to more accurate and actionable insights.

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Research Scientist

Research Scientists are employed in academia, industry, and government to conduct research and develop new knowledge. This course, Experimental Design for Data Analysis, is a valuable tool for Research Scientists, as it provides a solid foundation in the principles of experimental design, hypothesis testing, and model evaluation. By understanding how to design and conduct experiments, Research Scientists can ensure that their research is rigorous and produces reliable results, contributing to the advancement of knowledge in their field.

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Marketing Analyst

Marketing Analysts play a vital role in understanding customer behavior and developing effective marketing campaigns. This course, Experimental Design for Data Analysis, may be helpful for Marketing Analysts, as it provides a framework for designing and conducting experiments to test the effectiveness of different marketing strategies. By understanding how to analyze data and draw valid conclusions, Marketing Analysts can make more informed decisions and improve the return on investment for their marketing campaigns.

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Product Manager

Product Managers are responsible for the development and management of products, ensuring that they meet the needs of customers. This course, Experimental Design for Data Analysis, may be helpful for Product Managers, as it provides a framework for gathering and analyzing data to understand customer needs and evaluate the success of new products or features. By understanding how to design and conduct experiments, Product Managers can make more informed decisions and deliver products that meet the evolving needs of the market.

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Financial Analyst

Financial Analysts are responsible for analyzing financial data to make investment recommendations and advise clients on financial matters. This course, Experimental Design for Data Analysis, may be helpful for Financial Analysts, as it provides a foundation in statistical analysis and hypothesis testing, which are essential skills for evaluating the risk and return of investments. By understanding how to design and conduct experiments, Financial Analysts can make more informed decisions and provide more valuable advice to their clients.

See salaries and explore the career path for Financial Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to solve complex problems in business and industry. This course, Experimental Design for Data Analysis, may be helpful for Operations Research Analysts, as it provides a framework for designing and conducting experiments to evaluate the effectiveness of different solutions. By understanding how to analyze data and draw valid conclusions, Operations Research Analysts can make more informed decisions and improve the efficiency and effectiveness of their organizations.

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Software Engineer

Software Engineers design, develop, and maintain software systems. This course, Experimental Design for Data Analysis, may be helpful for Software Engineers, as it provides a framework for gathering and analyzing data to evaluate the performance and reliability of software systems. By understanding how to design and conduct experiments, Software Engineers can make more informed decisions and develop more robust and reliable software systems.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical techniques to analyze financial data and make investment decisions. This course, Experimental Design for Data Analysis, may be helpful for Quantitative Analysts, as it provides a strong foundation in statistical analysis and hypothesis testing, which are essential skills for evaluating the risk and return of investments. By understanding how to design and conduct experiments, Quantitative Analysts can make more informed decisions and generate more alpha for their clients.

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Risk Analyst

Risk Analysts identify and assess risks to organizations and develop strategies to mitigate those risks. This course, Experimental Design for Data Analysis, may be helpful for Risk Analysts, as it provides a framework for gathering and analyzing data to evaluate the likelihood and impact of different risks. By understanding how to design and conduct experiments, Risk Analysts can make more informed decisions and develop more effective risk management strategies.

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Data Engineer

Data Engineers design and build the infrastructure that stores and processes data. This course, Experimental Design for Data Analysis, may be helpful for Data Engineers, as it provides a framework for understanding the different types of data and how to design and build systems to store and process that data efficiently. By understanding how to design and conduct experiments, Data Engineers can make more informed decisions and develop more scalable and reliable data systems.

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Auditor

Auditors examine and evaluate financial and operational information to ensure accuracy and compliance with regulations. This course, Experimental Design for Data Analysis, may be helpful for Auditors, as it provides a framework for gathering and analyzing data to identify potential risks and areas for improvement. By understanding how to design and conduct experiments, Auditors can make more informed decisions and provide more valuable insights to their clients.

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