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Coursera 4.3 (9)
Overview
Statistical Learning is a crucial specialization for those pursuing a career in data science or seeking to enhance their expertise in the field. This Statistical Learning for Data Science course offered by Coursera in partnership with University of Colorado Boulder builds upon your foundational knowledge of statistics and equips you with advanced techniques for model selection, including regression, classification, trees, SVM, unsupervised learning, splines, and resampling methods. Additionally, you will gain an in-depth understanding of coefficient estimation and interpretation, which will be valuable in explaining and justifying your models to clients and companies. Through this specialization, you will acquire conceptual knowledge and communication skills to effectively convey the rationale behind your model choices and coefficient interpretations.
This specialization 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.
Applied Learning Project
Throughout the specialization, learners will complete many programming assignments designed to help learners master statistical learning concepts, including regression, classification, trees, SVM, unsupervised learning, splines, and resampling methods.
What You Will Learn:
- Express why Statistical Learning is important and how it can be used.
- Apply many regression and classification techniques.
- Explain the pros and cons of certain models in certain situations.
Skills You Will Gain:
- Unsupervised Learning
- Resampling
- Regression
- R Programming
- Splines
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Visit programme websiteProgramme Structure
Courses include:
- Regression and Classification
- Resampling, Selection and Splines
- Trees, SVM and Unsupervised Learning
Check out the full curriculum
Visit programme websiteKey information
Duration
- Part-time
- 4 months
- Flexible
Start dates & application deadlines
You can apply for and start this programme anytime.
Language
English
Delivered
Online
Disciplines
Statistics Data Science & Big Data View 592 other Short Courses in Data Science & Big Data in United StatesExplore more key information
Visit programme websiteAcademic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
Intermediate level
- Recommended experience: Intro Statistics and Foundational Math
Make sure you meet all requirements
Visit programme websiteTuition Fee
To always see correct tuition fees
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International
FreeTuition FeeBased on the tuition of 0 USD for the full programme during 4 months. -
National
FreeTuition FeeBased on the tuition of 0 USD for the full programme during 4 months.
You can choose from hundreds of free courses, or get a degree or certificate at a breakthrough price. You can now select Coursera Plus, an annual subscription that provides unlimited access.
Funding
Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project.
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