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Resampling, Selection and Splines
  • Level Professional
  • Duration 16 hours
  • Course by University of Colorado Boulder
  • Offered by

About

"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.

Modules

Course Introduction

    • 1 Videos
    • 2 Readings
    • 1 Discussion
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1 Discussions

  • Introduce Yourself!

1 Videos

  • Course Introduction

2 Readings

  • Earn Academic Credit for your Work!
  • Course Support

Generalized Linear Models (GLM) Review

    • 2 Videos
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2 Videos

  • Generalized Linear Models: Part 1
  • Generalized Linear Models: Part 2

Non-Parametric Regression Review

    • 1 Videos
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1 Videos

  • Parametric vs Non-Parametric Regression

General Additive Models (GAM) Review

    • 2 Videos
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2 Videos

  • General Additive Models Part 1
  • General Additive Models Part 2

Introduction to Generalized Least Squares

    • 1 Videos
    • 1 Readings
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1 Videos

  • Generalized Least Squares

1 Readings

  • Generalized Least Squares (GLS): Relations to OLS & WLS

Assignments

    • 1 Lab
    • 1 Programming
Show info about module content

1 Programming

  • Generalized Least Squares

1 Labs

  • Generalized Least Squares Practice

Ridge Regression

    • 4 Videos
    • 1 Readings
    • 1 Programming
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1 Programming

  • Ridge Regression

4 Videos

  • L1 and L2 Norms
  • Ridge Regression: Part 1
  • Ridge Regression: Part 2
  • Ridge Regression: Part 3

1 Readings

  • Ridge Regression

LASSO

    • 1 Videos
    • 1 Readings
    • 1 Programming
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1 Programming

  • LASSO

1 Videos

  • LASSO

1 Readings

  • LASSO

Principle Component Analysis (PCA)

    • 2 Videos
    • 1 Readings
    • 1 Programming
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1 Programming

  • Principle Component Analysis

2 Videos

  • Principle Component Analysis (PCA) Overview
  • PCA in Terms of SVD

1 Readings

  • Principle Component Analysis

Cross-Validation

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Cross-Validation

1 Readings

  • Summary

Module Assignment

    • 1 Programming
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1 Programming

  • Cross-Validation

Bootstrapping

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Bootstrapping

1 Readings

  • Summary

Module Assignment

    • 1 Programming
Show info about module content

1 Programming

  • Bootstrapping

Auto Summary

Unlock advanced data science techniques with the "Statistical Learning for Data Science" course, tailored for professionals eager to enhance their expertise. This comprehensive program delves into crucial topics such as shrink methods, parametric regression analysis, generalized linear models, and general additive models. You'll gain hands-on experience with resampling methods to refine model accuracy and interpretability, optimizing fitting procedures, and exploring the advantages of non-linear models. Ideal for those aiming to upskill or pivot to a data science career, this course also forms part of the University of Colorado Boulder's Master of Science in Data Science (MS-DS) degree on Coursera. The interdisciplinary MS-DS program features faculty from diverse departments, including Applied Mathematics, Computer Science, and Information Science, making it accessible to individuals with varied academic and professional backgrounds. With no application process and performance-based admissions, this degree is within reach for many aspiring data scientists. Enroll in the "Statistical Learning for Data Science" course offered by Coursera and benefit from flexible subscription options, including Starter and Professional plans. With a total duration of 960 hours, this professional-level course provides the depth and breadth needed to master statistical learning and excel in the data science domain.

Osita Onyejekwe
Instructor

Osita Onyejekwe