Four Rare Machine Learning Skills All Data Scientists Need from Coursera

This course covers the most neglected yet critical skills in machine learning, four vital techniques that are very rarely covered – most courses and books omit them entirely.

1) UPLIFT MODELING (AKA PERSUASION MODELING): When you're modeling, are you even predicting the right thing?

2) THE ACCURACY FALLACY: When evaluating how well a model works, are you even reporting on the right thing?

3) P-HACKING: Are your simplest discoveries from data even real?

4) THE PARADOX OF ENSEMBLE MODELS: Do you understand how they work, even though they seem to defy Occam's Razor?

>> WHY THESE ADVANCED METHODS ARE ESSENTIAL: Each one addresses a question that is fundamental to machine learning (above). For many projects, success hinges on these particular skills.

>> NO HANDS-ON – BUT FOR TECHNICAL LEARNERS: This course has no coding and no use of machine learning software. Instead, it lays the conceptual groundwork before you take on the hands-on practice. When it comes to these state-of-the-art techniques and prevalent pitfalls, there's a foundation of conceptual knowledge to build before going hands-on – and you'll be glad you did.

>> VENDOR-NEUTRAL: This course includes illuminating software demos of machine learning in action using SAS products. However, the curriculum is vendor-neutral and universally-applicable. The contents and learning objectives apply, regardless of which machine learning software tools you end up choosing to work with.

What's inside

Syllabus

Four Rare Machine Learning Skills All Data Scientists Need

This one-week course has only one module, which covers the course's four rare yet vital topics: (1) UPLIFT MODELING: How do you optimize marketing – which is meant to persuade – if we cannot generally establish causal relationships? Put another way, how do you model and predict influence when you cannot measure influence? The special, advanced method uplift modeling (aka persuasion modeling) goes beyond predicting an outcome to actually predicting the influence that a treatment decision would have on that outcome. We'll explore the marketing applications of uplift modeling and see success stories from the likes of US Bank and President Obama's 2012 reelection campaign. (2) THE ACCURACY FALLACY: For many machine learning projects, high accuracy is unattainable – and, besides, accuracy isn't the right metric in the first place. But many projects are falsely advertised as "highly accurate." Learn to identify occurrences of the accuracy fallacy, a common misstep by which researches spread misinformation about predictive model performance. (3) P-HACKING: In what way is bigger data more dangerous? How do we avoid being fooled by random noise and ensure scientific discoveries are trustworthy? This prevalent pitfall is a huge gotcha! (4) THE PARADOX OF ENSEMBLE MODELS: Is there a way to advance model capability and performance that's elegant and simple, without involving the complexity of neural networks? Why yes there is.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides a foundation for conceptualizing modern machine learning techniques and common pitfalls before moving to hands-on practice

Suitable for technical learners seeking to advance their theoretical understanding of machine learning concepts

Addresses advanced topics including uplift modeling and the accuracy fallacy often neglected in machine learning courses

Instructor has industry experience which lends credibility to the course content

This course is vendor-neutral, making it applicable to learners regardless of their preferred machine learning software

No hands-on component, which may not be suitable for learners seeking immediate practical application

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 Four Rare Machine Learning Skills All Data Scientists Need with these activities:

Review Neural Networks and Deep Learning

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Familiarize yourself with the key concepts of neural networks and deep learning before diving into the course material.

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Read the first three chapters of the book
Complete the exercises and code examples in the book

Organize and Review Course Materials

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Maximize knowledge retention by organizing and reviewing course materials regularly.

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Create a system for organizing notes, assignments, and other course materials
Review course materials on a regular basis, even after completing assignments

Join a Study Group for the Course

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Enhance your learning experience by collaborating with peers in a study group.

Browse courses on Machine Learning

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Find a group of students who are also taking the course
Meet regularly to discuss course material, share insights, and work on projects together

Five other activities

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Practice Uplift Modeling Techniques

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Reinforce your understanding of uplift modeling techniques by working through practice problems.

Browse courses on Uplift Modeling

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Solve the uplift modeling problems provided in the course materials
Create your own uplift modeling scenarios and solve them

Identify and Avoid the Accuracy Fallacy

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Develop your ability to identify and avoid the common pitfall of the accuracy fallacy.

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Analyze machine learning models to determine if they are susceptible to the accuracy fallacy
Develop strategies to mitigate the accuracy fallacy in your own machine learning projects

Create a Presentation on a Rare Machine Learning Skill

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Solidify your understanding of a rare machine learning skill by creating a presentation on it.

Browse courses on Machine Learning

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Choose one of the rare machine learning skills covered in the course
Research the skill and gather information from multiple sources
Create a presentation that clearly explains the skill and its applications

Practice Avoiding P-Hacking

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Enhance your data science skills by learning to avoid the pitfalls of p-hacking.

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Review the course materials on p-hacking
Apply p-hacking detection techniques to your own data analysis projects

Explore the Paradox of Ensemble Models

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Deepen your understanding of the paradox of ensemble models and how to leverage them effectively.

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Implement different ensemble modeling techniques in your own projects
Analyze the performance of ensemble models and compare them to single models

Career center

Learners who complete Four Rare Machine Learning Skills All Data Scientists Need will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use their knowledge of statistics, linear algebra, and computer science to bring insights to the decision-making process of a company. This four-skill course can help a Data Scientist bring more accurate and persuasive insights to decision-makers, which can directly impact the rate of success for a business.

See salaries and explore the career path for Data Scientist

Analyst

Analysts research, interpret, and present data to help companies and organizations make informed decisions. They use their analytical skills to uncover trends, patterns, and insights in data. This course will help Analysts optimize their impact on businesses through more accurate and persuasive analyses.

See salaries and explore the career path for Analyst

Statistician

Statisticians collect, analyze, and interpret data to help organizations make informed decisions. They design and conduct surveys, experiments, and other studies to collect data. This course will help Statisticians understand how to identify and avoid pitfalls and fallacies in their work, leading to more accurate and reliable results.

See salaries and explore the career path for Statistician

Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning models to solve real-world problems. They work with data scientists to identify and gather the necessary data, and then use their knowledge of machine learning algorithms to build and train models. This course will help Machine Learning Engineers build more accurate and effective models, leading to better results for their organizations.

See salaries and explore the career path for Machine Learning Engineer

Market Researcher

Market Researchers conduct surveys, focus groups, and other studies to collect data about consumer behavior. They use this data to help businesses understand their target market and develop marketing strategies. This course will help Market Researchers collect and analyze data more effectively, leading to better insights and more successful marketing campaigns.

See salaries and explore the career path for Market Researcher

Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to solve business problems. They work with businesses to improve efficiency, productivity, and profitability. This course will help Operations Research Analysts understand and apply the latest machine learning techniques to their work, leading to better solutions and improved outcomes.

See salaries and explore the career path for Operations Research Analyst

Risk Analyst

Risk Analysts assess and manage risks for organizations. They work with businesses to identify, evaluate, and mitigate risks that could impact the organization's financial performance or reputation. This course will help Risk Analysts understand and apply the latest machine learning techniques to their work, leading to more accurate and reliable risk assessments.

See salaries and explore the career path for Risk Analyst

Consultant

Consultants provide advice and guidance to businesses on a variety of topics, including strategy, operations, and technology. They work with businesses to identify and solve problems, and to develop and implement solutions. This course will help Consultants understand and apply the latest machine learning techniques to their work, leading to more innovative and effective solutions for their clients.

See salaries and explore the career path for Consultant

Financial Analyst

Financial Analysts provide advice and guidance to businesses on financial matters. They work with businesses to assess financial performance, make investment decisions, and manage risk. This course will help Financial Analysts understand and apply the latest machine learning techniques to their work, leading to more accurate and reliable financial analysis.

See salaries and explore the career path for Financial Analyst

Business Analyst

Business Analysts work with businesses to improve processes, systems, and strategies. They use their analytical skills to identify and solve business problems, and to develop and implement solutions. This course will help Business Analysts understand and apply the latest machine learning techniques to their work, leading to more effective and efficient solutions for their clients.

See salaries and explore the career path for Business Analyst

Policy Analyst

Policy Analysts research, analyze, and evaluate public policies. They work with governments and other organizations to develop and implement policies that address social and economic issues. This course will help Policy Analysts understand and apply the latest machine learning techniques to their work, leading to more accurate and reliable policy analysis.

See salaries and explore the career path for Policy Analyst

Software Engineer

Software Engineers design, develop, and maintain software systems. They work with businesses to identify and solve technical problems, and to develop and implement software solutions. This course will help Software Engineers understand and apply the latest machine learning techniques to their work, leading to more efficient and effective software development.

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Data Engineer

Data Engineers design, build, and maintain data systems. They work with businesses to collect, store, and process data, and to develop and implement data solutions. This course will help Data Engineers understand and apply the latest machine learning techniques to their work, leading to more efficient and effective data management.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical techniques to analyze financial data. They work with investment firms and other financial institutions to develop and implement investment strategies. This course will help Quantitative Analysts understand and apply the latest machine learning techniques to their work, leading to more accurate and reliable financial analysis.

See salaries and explore the career path for Quantitative Analyst

Actuary

Actuaries use mathematical and statistical techniques to assess risk and uncertainty. They work with insurance companies and other financial institutions to develop and implement risk management strategies. This course will help Actuaries understand and apply the latest machine learning techniques to their work, leading to more accurate and reliable risk assessment.

See salaries and explore the career path for Actuary