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Coursera 4.3 (9)
Overview
Statistical modeling lies at the heart of data science. Well crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In this Statistical Modeling for Data Science Applications course offered by Coursera in partnership with University of Colorado Boulder, learners will add some intermediate and advanced statistical modeling techniques to their data science toolkit. In particular, learners will become proficient in the theory and application of linear regression analysis; ANOVA and experimental design; and generalized linear and additive models. Emphasis will be placed on analyzing real data using the R programming language.
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
Learners will master the application and implementation of statistical models through auto-graded and peer reviewed Jupyter Notebook assignments. In these assignments, learners will use real-world data and advanced statistical modeling techniques to answer important scientific and business questions.
What You Will Learn:
- Correctly analyze and apply tools of regression analysis to model relationship between variables and make predictions given a set of input variables.
- Use advanced statistical modeling techniques, such as generalized linear and additive models, to model wide range of real-world relationships.
- Successfully conduct experiments based on best practices in experimental design.
Skills You Will Gain:
- Linear Model
- Regression
- R Programming
- Statistical Model
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Visit programme websiteProgramme Structure
Courses include:
- Modern Regression Analysis in R
- ANOVA and Experimental Design
- Generalized Linear Models and Nonparametric Regression
Check out the full curriculum
Visit programme websiteKey information
Duration
- Part-time
- 3 months
- Flexible
Start dates & application deadlines
You can apply for and start this programme anytime.
Language
English
Delivered
Online
Disciplines
Statistics Data Analytics View 86 other Short Courses in Statistics 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
- Calculus, linear algebra, and probability theory.
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 3 months. -
National
FreeTuition FeeBased on the tuition of 0 USD for the full programme during 3 months.
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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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