Coursera - Statistical Modeling for Data Science Applications Specialization

[LeeAndro]

Last updated 12/2022MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 ChGenre: eLearning | Language: English + srt | Duration: 102 Lessons (20h 27m) | Size: 2.59 GB

Correctly analyze and apply tools of regression analysis to model relationship between variables and make predictions given a set of input variables.​

Successfully conduct expents based on best practices in expental design.

Use advanced statistical modeling techniques, such as generalized linear and additive models, to model wide range of real-world relationships.

Linear Model
R Programming
Statistical Model
regression
Calculus
and probability theory.
Linear Algebra

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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 three credit sequence, 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 expental 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 acad 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 athttps://www.coursera.org/degrees/master-of-science-data-science-boulder.

Logo adapted from photo by Vincent Ledvina on Unsplash

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.

HomePage:

https://www.coursera.org/specializations/statistical-modeling-for-data-science-applications

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