Statistical Modeling for Data Science Applications, Short Course | Part time online | Coursera | United States
3 months
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Build Your Statistical Skills for Data Science. Master the Statistics Necessary for Data Science with this Statistical Modeling for Data Science Applications course offered by Coursera in partnership with University of Colorado Boulder.

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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

Programme Structure

Courses include:

  • Modern Regression Analysis in R
  • ANOVA and Experimental Design
  • Generalized Linear Models and Nonparametric Regression

Key information


  • Part-time
    • 3 months
    • Flexible

Start dates & application deadlines

You can apply for and start this programme anytime.





Academic 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.

Tuition Fee

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  • International

    Tuition Fee
    Based on the tuition of 0 USD for the full programme during 3 months.
  • National

    Tuition Fee
    Based on the tuition of 0 USD for the full programme during 3 months.

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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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