Statistical Learning with Python from edX

This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines; neural networks and deep learning; survival models; multiple testing. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).

This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. We focus on what we consider to be the important elements of modern data science. Computing in this course is done in Python. There are lectures devoted to Python, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chatper. We also offer the separate and original version of this course called Statistical Learning with R – the chapter lectures are the same, but the lab lectures and computing are done using R.

The lectures cover all the material in An Introduction to Statistical Learning, with Applications in Python by James, Witten, Hastie, Tibshirani, and Taylor (Springer, 2023. The pdf for this book is available for free on the book website.

What's inside

Learning objectives

Overview of statistical learning
Linear regression
Classificaiton
Resampling methods
Linear model selection and regularization
Moving beyond linearity

Tree-based methods
Support vector machines
Deep learning
Survival modeling
Unsupervised learning
Multiple testing

Overview of statistical learning
Linear regression
Classificaiton
Resampling methods
Linear model selection and regularization
Moving beyond linearity
Tree-based methods
Support vector machines
Deep learning
Survival modeling
Unsupervised learning
Multiple testing

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Introduces students to essential concepts of machine learning, focusing on foundational theories and practical applications

Emphasizes comprehension and application of key statistical learning concepts rather than relying heavily on mathematical formulas

Provides a comprehensive overview of modern data science techniques, including supervised and unsupervised learning methods

Incorporates hands-on Python programming exercises to reinforce concepts and enhance practical skills

Led by renowned instructors with extensive experience and expertise in statistical learning and machine learning

Career center

Learners who complete Statistical Learning with Python will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists combine statistical knowledge, programming skills, and business acumen to extract insights from data. This role demands proficiency in statistical modeling, machine learning, and data analysis. The Statistical Learning with Python course provides a comprehensive foundation for these skills. The course covers topics such as regression, classification, time series analysis, and natural language processing, which are essential for data science. Additionally, the course emphasizes practical implementation using Python, which is a widely used language in data science.

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Machine Learning Engineer

Machine Learning Engineers design, develop, and implement machine learning models to solve real-world problems. This role requires a strong foundation in statistical learning and programming. The Statistical Learning with Python course can equip you with the essential skills for this career. The course covers topics such as supervised and unsupervised learning, model evaluation, and feature engineering, which are fundamental to machine learning. Furthermore, the practical Python implementation of these techniques will provide you with hands-on experience in building and deploying machine learning solutions.

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Statistician

Statisticians collect, analyze, and interpret data to provide insights and solve problems. This role requires a deep understanding of statistical principles and methodologies. The Statistical Learning with Python course can provide you with a solid foundation in statistics. The course covers topics such as probability, inference, hypothesis testing, and regression analysis, which are essential for a Statistician. Additionally, the course emphasizes practical implementation using Python, which is increasingly used in statistical analysis.

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Data Analyst

Data Analysts collect, process, study, and visualize data to help organizations make informed decisions. To be successful in this role, you need to understand statistical techniques and programming languages. The Statistical Learning with Python course can provide you with the necessary skills to excel as a Data Analyst. The course covers topics such as data preprocessing, regression, classification, model selection, and machine learning algorithms, which are crucial for data analysis. By taking this course, you will gain a deep understanding of data analysis principles and practical experience in applying these techniques using Python.

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Risk Analyst

Risk Analysts assess and manage risks for organizations. This role requires expertise in statistical modeling, data analysis, and risk management. The Statistical Learning with Python course provides a strong foundation for these skills. The course covers topics such as probability, regression, and time series analysis, which are crucial for risk analysis. Furthermore, the practical Python implementation of these techniques will give you hands-on experience in building risk models and analyzing risk data.

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Quantitative Analyst

Quantitative Analysts apply mathematical and statistical techniques to financial data to make investment decisions. This role requires expertise in statistical modeling, risk assessment, and financial analysis. The Statistical Learning with Python course provides a strong foundation for these skills. The course covers topics such as regression, time series analysis, and financial modeling, which are crucial for quantitative analysis. Furthermore, the practical Python implementation of these techniques will give you hands-on experience in building financial models and analyzing financial data.

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Actuary

Actuaries use mathematical and statistical techniques to assess and manage financial risks. This role requires expertise in statistical modeling, probability, and financial analysis. The Statistical Learning with Python course provides a strong foundation for these skills. The course covers topics such as regression, time series analysis, and financial modeling, which are crucial for actuarial work. Furthermore, the practical Python implementation of these techniques will give you hands-on experience in building financial models and analyzing financial data.

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Business Analyst

Business Analysts use data and analytics to improve business processes and make informed decisions. This role requires proficiency in data analysis, statistical modeling, and business acumen. The Statistical Learning with Python course can provide you with a strong foundation for these skills. The course covers topics such as data visualization, regression, and decision-making tools, which are essential for business analysis. Additionally, the course emphasizes practical implementation using Python, which is widely used in business analytics.

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Biostatistician

Biostatisticians apply statistical methods to medical and health-related data. This role requires expertise in statistical modeling, data analysis, and medical knowledge. The Statistical Learning with Python course provides a strong foundation for these skills. The course covers topics such as regression, survival analysis, and clinical trials, which are essential for biostatistics. Furthermore, the practical Python implementation of these techniques will give you hands-on experience in analyzing medical data and drawing meaningful conclusions.

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Market Researcher

Market Researchers study market trends and consumer behavior to help businesses make informed decisions. This role requires expertise in data analysis, research methods, and marketing principles. The Statistical Learning with Python course may be useful in this role, as it provides a foundation in statistical modeling and machine learning. The course covers topics such as survey design, data analysis, and segmentation, which are relevant to market research. Additionally, the practical Python implementation of these techniques will give you hands-on experience in analyzing market data and drawing meaningful conclusions.

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Operations Research Analyst

Operations Research Analysts use mathematical and statistical techniques to solve complex business problems. This role requires expertise in optimization, simulation, and decision-making. The Statistical Learning with Python course may be useful in this role, as it provides a foundation in statistical modeling and machine learning. The course covers topics such as linear programming, integer programming, and decision trees, which are relevant to operations research. Additionally, the practical Python implementation of these techniques will give you hands-on experience in building models and analyzing data to solve business problems.

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Financial Analyst

Financial Analysts use financial data and analysis to make investment decisions. This role requires expertise in financial modeling, valuation, and risk assessment. The Statistical Learning with Python course may be useful in this role, as it provides a foundation in statistical modeling and machine learning. The course covers topics such as regression, time series analysis, and financial modeling, which are relevant to financial analysis. Additionally, the practical Python implementation of these techniques will give you hands-on experience in building financial models and analyzing financial data.

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Epidemiologist

Epidemiologists study the distribution and determinants of health-related states or events in specified populations. This role requires expertise in biostatistics, data analysis, and public health principles. The Statistical Learning with Python course may be useful in this role, as it provides a foundation in statistical modeling and machine learning. The course covers topics such as survival analysis, spatial epidemiology, and outbreak investigation, which are relevant to epidemiology. Additionally, the practical Python implementation of these techniques will give you hands-on experience in analyzing epidemiological data and drawing meaningful conclusions.

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Data Engineer

Data Engineers design, build, and maintain data infrastructure and systems. This role requires expertise in data management, database systems, and programming. The Statistical Learning with Python course may be useful in this role, as it provides a foundation in data analysis and statistical modeling. The course covers topics such as data cleaning, feature engineering, and machine learning algorithms, which are relevant to data engineering. Additionally, the practical Python implementation of these techniques will give you hands-on experience in working with data and building data pipelines.

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Software Engineer

Software Engineers design, develop, and maintain software applications. This role requires expertise in programming languages, software design, and algorithms. The Statistical Learning with Python course may be useful in this role, as it provides a foundation in statistical modeling and machine learning. The course covers topics such as data analysis, regression, and classification, which are relevant to software development. Additionally, the practical Python implementation of these techniques will give you hands-on experience in building software applications that leverage data and statistical methods.

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