Description
Statistical Modeling for Data Science Applications Specialization is a statistical modeling training series for data science applications published by Coursera Specialized Academy. This educational series consists of three completely separate courses. Mastery of various statistical sciences in the field of data science is very important and without it, it is practically impossible to do anything special. Well-constructed and accurate statistical models allow data scientists to draw different conclusions from information contained in large amounts of data. During this training course, students will be familiar with a set of intermediate and advanced statistical modeling techniques. The theories and practical parts of linear regression analysis are among the most important topics that will be discussed in this training course.
This training course includes a series of different topics, among the most important of which are variance analysis, experimental design, generalized linear model, and generalized collective models. (additive models) pointed out. This tutorial focuses on real data analysis using the R programming language.
What you will learn in the Statistical Modeling for Data Science Applications Specialization training course:
- Linear models
- Programming language R
- Statistical models
- linear regression
- Calculus
- Probability theory
- Linear Algebra
- Modeling and displaying the relationship between different variables
- Predicting the future value of variables by examining the current value of existing variables
- And …
Course details
Publisher: Coursera
teacher: Brian Zaharatos
English language
Providing institution/university: University of Colorado Boulder
Education level: Intermediate
Number of courses: 3
Duration of training: assuming 9 hours of work per week, about 4 months
The courses available in the Statistical Modeling for Data Science Applications Specialization collection
Course 1
Modern Regression Analysis in R
Course 2
ANOVA and Experimental Design
Course 3
Generalized Linear Models and Nonparametric Regression
Course prerequisites
What background knowledge is necessary?
Students should be familiar with differential and integral calculus, basic linear algebra (including matrix operations, properties, and vector norms) and probability theory (including random variables, probability distributions, expectation, variance, covariance, and conditional probability).
Do I need to take the courses in a specific order?
Learners should take the first course (Modern Regression Analysis in R) first. Learners can take the next two courses in either order.
Pictures
Sample video of Statistical Modeling for Data Science Applications Specialization
Installation guide
After extracting, watch with your favorite player.
English subtitle
Quality: 720p
This educational series consists of 3 separate courses.
download link
Course 1 – Modern Regression Analysis in R
Course 2 – ANOVA and Experimental Design
Course 3 – Generalized Linear Models and Nonparametric Regression
Password file(s): www.downloadly.ir
The size of the files
About 2.5 GB in total
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