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.

Generalized Least Squares

In this module, we will turn our attention to generalized least squares (GLS). GLS is a statistical method that extends the ordinary least squares (OLS) method to account for heteroscedasticity and serial correlation in the error terms. Heteroscedasticity is the condition where the variance of the errors is not constant across all levels of the predictor variables, while serial correlation is the condition where the errors are correlated across time or space. GLS has many practical applications, such as in finance for modeling asset returns, in econometrics for modeling time series data, and in spatial analysis for modeling spatially correlated data. By the end of this module, you will have a good understanding of how GLS works and when it is appropriate to use it. You will also be able to implement GLS in R using the gls() function in the nlme package.

Shrink Methods

In this module, we will explore ridge regression, LASSO, and principal component analysis (PCA). These techniques are widely used for regression and dimensionality reduction tasks in machine learning and statistics.

Cross-Validation

This week, we will be exploring the concept of cross-validation, a crucial technique used to evaluate and compare the performance of different statistical learning models. We will explore different types of cross-validation techniques, including k-fold cross-validation, leave-one-out cross-validation, and stratified cross-validation. We will discuss their strengths, weaknesses, and best practices for implementation. Additionally, we will examine how cross-validation can be used for model selection and hyperparameter tuning.

Bootstrapping

For our final module, we will explore bootstrapping. Bootstrapping is a resampling technique that allows us to gain insights into the variability of statistical estimators and quantify uncertainty in our models. By creating multiple simulated datasets through resampling, we can explore the distribution of sample statistics, construct confidence intervals, and perform hypothesis testing. Bootstrapping is particularly useful when parametric assumptions are hard to meet or when we have limited data. By the end of this week, you will have an understanding of bootstrapping and its practical applications in statistical learning.

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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

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.

See salaries and explore the career path for Operations Research Analyst

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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Resampling, Selection and Splines

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