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Statistical Learning

Shahrzad Jamshidi

This course offers a deep dive into the world of statistical analysis, equipping learners with cutting-edge techniques to understand and interpret data effectively. We explore a range of methodologies, from regression and classification to advanced approaches like kernel methods and support vector machines, all designed to enhance your data analysis skills.

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This course offers a deep dive into the world of statistical analysis, equipping learners with cutting-edge techniques to understand and interpret data effectively. We explore a range of methodologies, from regression and classification to advanced approaches like kernel methods and support vector machines, all designed to enhance your data analysis skills.

Our journey is guided by the well-known textbook "The Elements of Statistical Learning" by T. Hastie, R. Tibshirani, and J. Friedman. This course provides examples written in Python. Your system should have Python 3.8 or higher, as well as essential libraries such as NumPy, pandas, matplotlib, seaborn, scikit-learn, SciPy, and PyTorch. These tools not only support the learning process but also prepare you for real-world data analysis challenges.

Whether you're aiming to refine your expertise or just starting out in the field of data science, this course provides the knowledge and tools to transform your understanding and application of statistical learning. It's a perfect blend of theory and practice, ideal for anyone looking to enhance their skills in data interpretation and analysis.

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What's inside

Syllabus

Module 1: Statistical Learning - Terminology and Ideas
Welcome to Statistical Learning! In this course, we will cover the topics: Statistical Learning: Terminology and Ideas, Linear Regression Methods, Linear Classification Methods, Basis Expansion Methods, Kernel Smoothing Methods, Model Assessment and Selection, Maximum Likelihood Inference, and Advanced Topics. Module 1 offers an in-depth exploration of statistical learning, beginning with the rationale behind choosing a pre-defined family of functions and optimizing the expected prediction error (EPE). It covers the essentials of statistical learning, including the loss function, the bias-variance tradeoff in model selection, and the significance of model evaluation. This module also distinguishes between supervised and unsupervised learning, discusses various types of statistical learning models and data representation, and delves into the three core elements of a statistical learning problem, providing a comprehensive introduction to this field.
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Examines statistical learning core methodologies for a deeper understanding of data analysis
Applies real-world examples and exercises in Python, preparing learners for practical data analysis
Covers advanced topics like Kernel Smoothing Methods and Support Vector Machines, extending learners' skillsets
Guided by the acclaimed textbook 'The Elements of Statistical Learning', providing a solid theoretical foundation
Teaches methods and techniques applicable to both supervised and unsupervised learning scenarios
Requires knowledge of essential Python libraries, potentially limiting accessibility for beginners in Python

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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 Statistical Learning with these activities:
Introduction to Statistical Learning
Reading the textbook used in the course will provide you with a comprehensive understanding of the course material and a valuable reference for future use.
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Show steps
  • Read the assigned chapters in the textbook
  • Take notes and highlight important concepts
Review Python Libraries
Familiarizing yourself with the Python libraries used in the course will give you a head start in understanding the concepts and techniques covered.
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Show steps
  • Go through the Python libraries documentation
  • Practice using the libraries in a Jupyter Notebook
Attend Machine Learning Meetups
Attending machine learning meetups will provide you with opportunities to connect with other professionals in the field and learn about the latest trends and developments.
Browse courses on Machine Learning
Show steps
  • Find machine learning meetups in your area
  • Attend the meetups and participate in discussions
Four other activities
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Study Group
Participating in a study group will provide you with opportunities to discuss the course material with your peers and reinforce your understanding.
Show steps
  • Form a study group with other students in the course
  • Meet regularly to discuss the course material and work on assignments together
Statistical Inference Problems
Solving statistical inference problems will reinforce your understanding of the concepts and improve your ability to apply them in practice.
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Show steps
  • Go through the textbook examples and exercises
  • Attempt the practice problems at the end of each chapter
Machine Learning Algorithms Tutorials
Following tutorials on machine learning algorithms will reinforce your understanding of the concepts and provide practical examples of their implementation.
Browse courses on Machine Learning Algorithms
Show steps
  • Find tutorials on different machine learning algorithms
  • Follow the tutorials and implement the algorithms in your own projects
Data Visualization Project
Creating data visualizations will help you develop a deeper understanding of the data and its implications, as well as improve your communication skills.
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Show steps
  • Choose a dataset and explore it
  • Create a variety of visualizations to represent the data
  • Write a report summarizing your findings and insights

Career center

Learners who complete Statistical Learning will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists use statistical learning to find patterns and insights in data. This course provides a comprehensive introduction to statistical learning, covering a wide range of topics from linear regression to advanced machine learning techniques. The course also emphasizes the practical aspects of data science, such as data cleaning, feature engineering, and model evaluation. This course can help you build a strong foundation for a career as a Data Scientist.
See salaries and explore the career path for Data Scientist
Machine Learning Engineer
Machine Learning Engineers use statistical learning to develop and deploy machine learning models. This course provides a comprehensive introduction to statistical learning, covering a wide range of topics from linear regression to advanced machine learning techniques. The course also emphasizes the practical aspects of machine learning, such as model training, evaluation, and deployment. This course can help you build a strong foundation for a career as a Machine Learning Engineer.
See salaries and explore the career path for Machine Learning Engineer
Data Analyst
Data Analysts use statistical learning to analyze data and communicate insights to stakeholders. This course provides a comprehensive introduction to statistical learning, covering a wide range of topics from linear regression to advanced machine learning techniques. The course also emphasizes the practical aspects of data analysis, such as data cleaning, feature engineering, and data visualization. This course can help you build a strong foundation for a career as a Data Analyst.
See salaries and explore the career path for Data Analyst
Quantitative Analyst
Quantitative Analysts use statistical learning to develop and implement trading strategies. This course provides a comprehensive introduction to statistical learning, covering a wide range of topics from linear regression to advanced machine learning techniques. The course also emphasizes the practical aspects of quantitative finance, such as risk management and portfolio optimization. This course can help you build a strong foundation for a career as a Quantitative Analyst.
See salaries and explore the career path for Quantitative Analyst
Financial Analyst
Financial Analysts use statistical learning to analyze financial data and make investment recommendations. This course provides a comprehensive introduction to statistical learning, covering a wide range of topics from linear regression to advanced machine learning techniques. The course also emphasizes the practical aspects of financial analysis, such as financial statement analysis and valuation. This course can help you build a strong foundation for a career as a Financial Analyst.
See salaries and explore the career path for Financial Analyst
Market Researcher
Market Researchers use statistical learning to analyze market data and consumer behavior. This course provides a comprehensive introduction to statistical learning, covering a wide range of topics from linear regression to advanced machine learning techniques. The course also emphasizes the practical aspects of market research, such as survey design and data analysis. This course can help you build a strong foundation for a career as a Market Researcher.
See salaries and explore the career path for Market Researcher
Business Analyst
Business Analysts use statistical learning to analyze business data and identify opportunities for improvement. This course provides a comprehensive introduction to statistical learning, covering a wide range of topics from linear regression to advanced machine learning techniques. The course also emphasizes the practical aspects of business analysis, such as data mining and optimization. This course can help you build a strong foundation for a career as a Business Analyst.
See salaries and explore the career path for Business Analyst
Statistician
Statisticians use statistical learning to analyze data and draw conclusions. This course provides a comprehensive introduction to statistical learning, covering a wide range of topics from linear regression to advanced machine learning techniques. The course also emphasizes the theoretical foundations of statistics, such as probability and inference. This course can help you build a strong foundation for a career as a Statistician.
See salaries and explore the career path for Statistician
Actuary
Actuaries use statistical learning to assess risk and develop insurance products. This course provides a comprehensive introduction to statistical learning, covering a wide range of topics from linear regression to advanced machine learning techniques. The course also emphasizes the practical aspects of actuarial science, such as risk management and pricing. This course can help you build a strong foundation for a career as an Actuary.
See salaries and explore the career path for Actuary
Epidemiologist
Epidemiologists use statistical learning to study the distribution and determinants of health-related states or events in specified populations. This course provides a comprehensive introduction to statistical learning, covering a wide range of topics from linear regression to advanced machine learning techniques. The course also emphasizes the practical aspects of epidemiology, such as study design and data analysis. This course can help you build a strong foundation for a career as an Epidemiologist.
See salaries and explore the career path for Epidemiologist
Biostatistician
Biostatisticians use statistical learning to analyze biological and health data. This course provides a comprehensive introduction to statistical learning, covering a wide range of topics from linear regression to advanced machine learning techniques. The course also emphasizes the practical aspects of biostatistics, such as clinical trial design and data analysis. This course can help you build a strong foundation for a career as a Biostatistician.
See salaries and explore the career path for Biostatistician
Data Engineer
Data Engineers use statistical learning to build and maintain data pipelines. This course provides a comprehensive introduction to statistical learning, covering a wide range of topics from linear regression to advanced machine learning techniques. The course also emphasizes the practical aspects of data engineering, such as data integration and data quality. This course can help you build a strong foundation for a career as a Data Engineer.
See salaries and explore the career path for Data Engineer
Software Engineer
Software Engineers use statistical learning to develop and implement software applications. This course provides a comprehensive introduction to statistical learning, covering a wide range of topics from linear regression to advanced machine learning techniques. The course also emphasizes the practical aspects of software engineering, such as software design and development. This course can help you build a strong foundation for a career as a Software Engineer.
See salaries and explore the career path for Software Engineer
Computer Scientist
Computer Scientists use statistical learning to develop and implement computer systems. This course provides a comprehensive introduction to statistical learning, covering a wide range of topics from linear regression to advanced machine learning techniques. The course also emphasizes the theoretical foundations of computer science, such as algorithms and data structures. This course can help you build a strong foundation for a career as a Computer Scientist.
See salaries and explore the career path for Computer Scientist
Operations Research Analyst
Operations Research Analysts use statistical learning to develop and implement operations research models. This course provides a comprehensive introduction to statistical learning, covering a wide range of topics from linear regression to advanced machine learning techniques. The course also emphasizes the practical aspects of operations research, such as optimization and simulation. This course can help you build a strong foundation for a career as an Operations Research Analyst.
See salaries and explore the career path for Operations Research Analyst

Reading list

We've selected 12 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Statistical Learning.
Cover image
The Elements of Statistical Learning
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Is an essential reference for anyone interested in statistical learning. It provides a comprehensive overview of the field, covering a wide range of topics from linear regression to deep learning. The authors are leading experts in the field, and the book is written in a clear and concise style.
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Kindle Edition
$$$
Cover image
An Introduction to Statistical Learning
Save
Popular textbook for introductory courses in statistical learning. It covers a similar range of topics as The Elements of Statistical Learning, but it is written in a more accessible style. The authors provide many examples and exercises, which makes the book ideal for self-study.
An Introduction to Statistical Learning
Paperback
Check
An Introduction to Statistical Learning
Kindle Edition
Check
Pattern Recognition and Machine Learning
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Provides a comprehensive treatment of pattern recognition and machine learning. It covers a wide range of topics, from supervised learning to unsupervised learning to reinforcement learning. The author leading expert in the field, and the book is written in a clear and concise style.
Pattern Recognition and Machine Learning...
Hardcover
$$$
Pattern Recognition and Machine Learning...
Paperback
$$$
Pattern Recognition and Machine Learning (text...
Unknown Binding
$$$$
Cover image
Deep Learning
Save
Provides a comprehensive treatment of deep learning. Deep learning subfield of machine learning that uses artificial neural networks to learn from data. The authors are leading experts in the field, and the book is written in a clear and concise style.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$
Statistical Learning with Sparsity
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Provides a comprehensive treatment of sparsity methods in statistical learning. Sparsity methods are techniques for finding models with a small number of non-zero coefficients. This makes them particularly useful for high-dimensional data, where traditional methods can be computationally expensive or unstable.
Statistical Learning with Sparsity
Paperback
Check
Statistical Learning with Sparsity
Kindle Edition
Check
Cover image
Bayesian Data Analysis, Third Edition
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Provides a comprehensive introduction to Bayesian data analysis. Bayesian data analysis statistical approach that uses Bayes' theorem to update beliefs in the light of new evidence. This makes it particularly useful for problems where there is uncertainty about the underlying model or parameters.
Bayesian Data Analysis (Chapman & Hall/CRC Texts in...
Kindle Edition
$$$$
Cover image
Reinforcement Learning, second edition
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Provides a comprehensive introduction to reinforcement learning. Reinforcement learning subfield of machine learning that deals with learning from interactions with an environment. The authors are leading experts in the field, and the book is written in a clear and concise style.
Reinforcement Learning, second edition: An...
Hardcover
$$$
Reinforcement Learning, second edition: An...
Kindle Edition
$$$
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
Provides a practical introduction to machine learning with Python. It covers a wide range of topics, from data preprocessing to model evaluation. The author leading expert in the field, and the book is written in a clear and concise style.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
Cover image
Data Mining
Save
Delivers a top-down approach to machine learning with a blend of theory and practical guidance.
Data Mining: Practical Machine Learning Tools and...
Paperback
$$$
DATA MINING, 4TH EDITION
Paperback
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Data Mining: Practical Machine Learning Tools and...
Kindle Edition
$$
Cover image
The Book of Why
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Challenges traditional views on causality and proposes causal reasoning that can be grounded in probability.
The Book of Why: The New Science of Cause and Effect
Kindle Edition
$$
Cover image
Introductory Econometrics
Save
While focusing on econometrics, this book is especially helpful in understanding panel data and time series analysis.
Introductory Econometrics
Paperback
Check
Introductory Econometrics
Kindle Edition
Check
Cover image
Statistical Analysis with Missing Data
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Provides a comprehensive examination of missing data imputation to enhance the reliability of statistical modeling.
Statistical Analysis with Missing Data (Wiley...
Hardcover
$$$
By Roderick J. A. Little - Statistical Analysis...
Hardcover
$$
Statistical Analysis with Missing Data (Wiley...
Hardcover
$$$
Statistical Analysis with Missing Data (Wiley...
Kindle Edition
$$$

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Level
Intermediate
Via
Coursera
Institution
Illinois Tech
Instructor
Shahrzad Jamshidi
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Examines statistical learning core methodologies for a deeper understanding of data analysis
Applies real-world examples and exercises in Python, preparing learners for practical data analysis
Covers advanced topics like Kernel Smoothing Methods and Support Vector Machines, extending learners' skillsets
Guided by the acclaimed textbook 'The Elements of Statistical Learning', providing a solid theoretical foundation
Teaches methods and techniques applicable to both supervised and unsupervised learning scenarios
Requires knowledge of essential Python libraries, potentially limiting accessibility for beginners in Python
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