Resampling, Selection and Splines from Coursera

"Statistical Learning for Data Science" is an advanced course designed to equip working professionals with the knowledge and skills necessary to excel in the field of data science. Through comprehensive instruction on key topics such as shrink methods, parametric regression analysis, generalized linear models, and general additive models, students will learn how to apply resampling methods to gain additional information about fitted models, optimize fitting procedures to improve prediction accuracy and interpretability, and identify the benefits and approach of non-linear models. This course is the perfect choice for anyone looking to upskill or transition to a career in data science.

This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

What's inside

Syllabus

Welcome and Review

Welcome to our Resampling, Selection, and Splines class! In this course, we will dive deep into these key topics in statistical learning and explore how they can be applied to data science. The module provides an introductory overview of the course and introduces the course instructor.

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Taught by Osita Onyejekwe, who are recognized for their work in data science, statistics, and machine learning

Examines real-world examples and industry best practices in data science and machine learning

Develops students' skills in applying statistical learning techniques to solve complex data science problems

Provides hands-on experience through interactive exercises and projects

Leverages the expertise of the University of Colorado Boulder, a leading institution in data science

Provides opportunities for networking and collaboration with peers and experts in the field

Reviews summary

Advanced statistical learning for data science

According to learners, this course offers an excellent deep dive into key statistical learning methods like resampling, selection, and splines. Students consistently highlight the instructor's clear and knowledgeable explanations, especially for complex topics like GLS and Bootstrapping. While it provides practical R implementations that solidify theoretical concepts, some learners note a divide on the balance between theory and hands-on application, with a few wishing for more real-world examples and diverse datasets. This advanced course is best suited for professionals with a strong foundation in statistics and R, as it assumes significant prior knowledge. Overall, it's considered challenging but highly rewarding, greatly enhancing one's data science toolkit.

Instructor provides clear, knowledgeable explanations.

"The instructor's explanations were incredibly clear, especially for complex topics like GLS and Bootstrapping."

"The way the instructor broke down the theory and then showed the R implementation was superb."

"I found the instructor knowledgeable and felt it was a good follow-up."

"The instructor's dedication to answering forum questions was notable."

Deep and clear coverage of resampling, selection, and splines.

"Excellent dive into resampling and shrinkage. The cross-validation module was outstanding..."

"The depth of coverage on resampling methods was exceptional, and the practical demonstrations in R were vital."

"I got the clarity I was looking for on generalized least squares and principal component analysis from this course."

"The cross-validation and bootstrapping modules were particularly strong and clarified many concepts for me."

Some modules' pacing is uneven, lacking sufficient examples.

"The pacing felt a bit uneven. Some modules rushed through advanced concepts without enough detailed examples..."

"More exercises would be beneficial for consolidating learning."

"I would suggest providing more hints or solutions for the more complex exercises, as I sometimes got stuck."

Opinions vary on the balance between theory and practical application.

"The R implementations were practical and helped solidify the theory."

"The explanations were often too academic and lacked practical, real-world examples I could immediately apply."

"I was hoping for more hands-on projects and case studies; it felt a bit dry at times."

"The labs were useful, but could use more diverse datasets."

Requires prior statistical knowledge and R proficiency.

"It assumes a very high level of prior knowledge in statistics and mathematical notation."

"This course is definitely for those who already have a strong foundation in statistics and R."

"As someone transitioning into data science, I struggled at times to keep up without external resources."

"Great course for those already familiar with foundational statistics."

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 Resampling, Selection and Splines with these activities:

Follow Online Tutorials

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Provides additional support and clarification on specific topics covered in this course.

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Find online tutorials on topics you find challenging.
Follow the tutorials and take notes.
Try out the examples and practice exercises.

Review Statistical Methods

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Refreshes your understanding of statistical methods, making it easier to grasp the advanced concepts covered in this course.

Browse courses on Statistical Methods

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Review your notes or textbooks from previous statistics courses.
Work through practice problems to test your understanding.
Take an online refresher course or watch video tutorials.

Create a Data Science Resource List

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Helps you organize and easily access valuable resources related to this course.

Browse courses on Compilation

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Gather links to helpful websites, articles, and videos.
Organize the resources into categories.
Share your resource list with classmates or online.

Six other activities

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Read Introduction to Statistical Learning

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Provides a comprehensive overview of the key concepts and techniques covered in this course, solidifying your understanding.

View An Introduction to Statistical Learning: with... on Amazon

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Read the book thoroughly.
Take notes and highlight important concepts.
Complete the exercises at the end of each chapter.

Solve Practice Problems

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Helps reinforce your understanding of the concepts and techniques covered in this course.

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Find practice problems in your textbook or online.
Work through the problems and check your answers.
Identify areas where you need additional practice.

Join a Study Group

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Provides opportunities to discuss course material, ask questions, and learn from others.

Browse courses on Collaborative Learning

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Find or create a study group with classmates.
Meet regularly to discuss course content and assignments.
Work together on practice problems and projects.

Build a Statistical Model

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Provides hands-on experience in applying the concepts learned in this course to real-world data.

Browse courses on Statistical Modeling

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Choose a dataset and define your research question.
Explore the data and select appropriate statistical methods.
Build and train your model.
Evaluate the performance of your model.
Write a report summarizing your findings.

Develop a Data Science Portfolio

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Provides a practical way to apply your skills and showcase your learning in this course.

Browse courses on Data Science Projects

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Identify a data science project that interests you.
Gather and clean the data.
Build and train a statistical model.
Evaluate and interpret your results.
Document your project in a portfolio.

Mentor Junior Data Scientists

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Deepens your knowledge by helping others understand the concepts and techniques covered in this course.

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Volunteer to mentor junior data scientists.
Share your knowledge and experience.
Answer questions and provide support.

Career center

Learners who complete Resampling, Selection and Splines will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist designs and builds analytical models and algorithms to extract meaningful insights from data. This course helps build a foundation for the statistical learning methods used by Data Scientists to accomplish this. The course provides an understanding of resampling methods, which are essential for assessing the accuracy and stability of models. Additionally, the course covers topics like shrinkage, penalized regression, and non-linear models, which are all widely used by Data Scientists in practice.

See salaries and explore the career path for Data Scientist

Statistician

A Statistician uses mathematical and statistical methods to collect, analyze, interpret, and present data. This course may be useful to a Statistician as it provides a deep dive into advanced statistical learning techniques, such as resampling, model selection, and non-linear modeling. These techniques are essential for Statisticians working in a variety of fields, including healthcare, finance, and market research.

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Machine Learning Engineer

A Machine Learning Engineer designs, builds, and deploys machine learning models to solve real-world problems. This course may be useful to a Machine Learning Engineer as it provides a solid foundation in the statistical methods that underlie machine learning. The course covers topics such as resampling, shrinkage, and non-linear modeling, which are all essential for developing accurate and reliable machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

A Data Analyst collects, analyzes, and interprets data to help organizations make informed decisions. This course may be useful to a Data Analyst as it provides a strong foundation in statistical learning methods, which are essential for extracting meaningful insights from data. The course covers topics such as resampling, penalized regression, and non-linear models, which are all widely used by Data Analysts in practice.

See salaries and explore the career path for Data Analyst

Business Analyst

A Business Analyst uses data to solve business problems and improve decision-making. This course may be useful to a Business Analyst as it provides a foundation in statistical learning methods, which can be used to analyze data and identify trends and patterns. The course covers topics such as resampling, model selection, and non-linear modeling, which are all valuable skills for a Business Analyst.

See salaries and explore the career path for Business Analyst

Quantitative Analyst

A Quantitative Analyst uses mathematical and statistical models to analyze financial data and make investment decisions. This course may be useful to a Quantitative Analyst as it provides a foundation in statistical learning methods, which are essential for developing and evaluating financial models. The course covers topics such as resampling, shrinkage, and non-linear modeling, which are all used by Quantitative Analysts in practice.

See salaries and explore the career path for Quantitative Analyst

Operations Research Analyst

An Operations Research Analyst uses mathematical and statistical models to optimize business operations and solve complex problems. This course may be useful to an Operations Research Analyst as it provides a foundation in statistical learning methods, which can be used to develop and evaluate optimization models. The course covers topics such as resampling, model selection, and non-linear modeling, which are all used by Operations Research Analysts in practice.

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Risk Analyst

A Risk Analyst identifies, assesses, and manages risks. This course may be useful to a Risk Analyst as it provides a foundation in statistical learning methods, which can be used to analyze data and identify risks. The course covers topics such as resampling, penalized regression, and non-linear modeling, which are all valuable skills for a Risk Analyst.

See salaries and explore the career path for Risk Analyst

Financial Analyst

A Financial Analyst analyzes financial data and makes recommendations for investment decisions. This course may be useful to a Financial Analyst as it provides a foundation in statistical learning methods, which can be used to analyze financial data and identify trends and patterns. The course covers topics such as resampling, model selection, and non-linear modeling, which are all valuable skills for a Financial Analyst.

See salaries and explore the career path for Financial Analyst

Market Researcher

A Market Researcher gathers and analyzes data to understand consumer behavior and market trends. This course may be useful to a Market Researcher as it provides a foundation in statistical learning methods, which can be used to analyze data and identify trends and patterns. The course covers topics such as resampling, penalized regression, and non-linear modeling, which are all valuable skills for a Market Researcher.

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Actuary

An Actuary uses mathematical and statistical methods to assess risk and uncertainty. This course may be useful to an Actuary as it provides a foundation in statistical learning methods, which can be used to develop and evaluate actuarial models. The course covers topics such as resampling, shrinkage, and non-linear modeling, which are all used by Actuaries in practice.

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Data Engineer

A Data Engineer designs and builds data pipelines and infrastructure to support data analysis and machine learning. This course may be useful to a Data Engineer as it provides a foundation in statistical learning methods, which can be used to understand the data being processed and to optimize the performance of data pipelines. The course covers topics such as resampling, penalized regression, and non-linear modeling, which are all valuable skills for a Data Engineer.

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Software Engineer

A Software Engineer designs, develops, and maintains software applications. This course may be useful to a Software Engineer as it provides a foundation in statistical learning methods, which can be used to develop and evaluate software applications. The course covers topics such as resampling, shrinkage, and non-linear modeling, which are all valuable skills for a Software Engineer.

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Product Manager

A Product Manager leads the development and launch of new products. This course may be useful to a Product Manager as it provides a foundation in statistical learning methods, which can be used to understand customer needs and to evaluate the success of new products. The course covers topics such as resampling, penalized regression, and non-linear modeling, which are all valuable skills for a Product Manager.

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Consultant

A Consultant provides advice and expertise to clients on a variety of topics. This course may be useful to a Consultant as it provides a foundation in statistical learning methods, which can be used to analyze data and to solve problems for clients. The course covers topics such as resampling, penalized regression, and non-linear modeling, which are all valuable skills for a Consultant.

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