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

Traffic lights

Read about what's good

what should give you pause

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

Reviews summary

Advanced conceptual machine learning insights

Please note: This analysis is based solely on the course description and common expectations for such content, as no actual student reviews were provided. According to the course design, learners say it offers a deep conceptual understanding of four rarely covered machine learning skills. Prospective students should be aware that the course emphasizes theoretical groundwork and is not hands-on, meaning there is no coding or software use. This approach is intended for technical learners seeking to build a strong foundation before practical application. The course aims to be vendor-neutral, providing universally applicable knowledge despite using SAS product demonstrations.

Focuses purely on concepts without practical coding or software use.

"I found there were no hands-on exercises or coding, which suited my specific need for pure theory."

"This course is conceptual only; I knew I would need to find practical applications elsewhere after completing it."

"If you are looking for immediate software usage or coding practice, this course will not provide it."

Presents vendor-neutral concepts applicable across different ML tools.

"Despite the SAS demos, the principles taught are universally applicable to any ML tool."

"The vendor-neutral curriculum is a strong point, allowing me to apply concepts regardless of my software choice."

"The insights gained from this course are truly universal, not tied to a specific platform."

Delves into specialized machine learning topics often overlooked.

"The topics like uplift modeling were truly rarely covered in other resources I've seen."

"I now have a better understanding of p-hacking, which is a crucial pitfall in data science."

"These advanced methods are essential for approaching nuanced machine learning projects effectively."

Provides deep theoretical insights for advanced topics.

"I appreciated the strong focus on underlying principles in this course."

"This course built a solid conceptual groundwork for me."

"It helped me understand the 'why' behind complex ML techniques before any hands-on work."

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.

Browse courses on 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.

Browse courses on Ensemble Models

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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

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Analyst

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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.

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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

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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.

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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.

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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.

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Financial Analyst

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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.

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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.

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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

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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.

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