Applied Statistical Modeling for Data Analysis in R

Your Complete Guide to Statistical Data Analysis and Visualization For Practical Applications in R

4.49 (1921 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Analyze their own data by applying appropriate statistical techniques

Interpret the results of their statistical analysis

Identify which statistical techniques are best suited to their data and questions

Have a strong foundation in fundamental statistical concepts

Implement different statistical analysis in R and interpret the results

Build intuitive data visualizations

Carry out formalized hypothesis testing

Implement linear modelling techniques such multiple regressions and GLMs

Implement advanced regression analysis and multivariate analysis

Course Gallery

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Comidoc Review

Our Verdict

Applied Statistical Modeling for Data Analysis in R offers an outstanding course for those eager to deepen their understanding of statistical techniques and develop hands-on experience implementing these methods in R. Though it demands a certain level of comfort with the programming language and occasionally stumbles with errors, Minerva Singh's engaging delivery and clear explanations make this challenging yet rewarding learning journey an excellent use of time for both beginners and advanced learners seeking to enhance their data analysis skills.

What We Liked

The course offers a comprehensive survey of statistical concepts and their implementation in R, making it a one-stop-shop for those looking to analyze data and implement statistical learning.
Minerva Singh's clear and concise explanations of mathematical theories behind the methods ensure that learners not only know how to apply these techniques but also understand why they work.
A wide range of practical examples, including real-world datasets, offer learners ample opportunity to practice implementing statistical analyses in R, ensuring a strong foundation in both theory and practice.
The course's extensive coverage includes hypothesis testing, linear modeling techniques, advanced regression analysis, multivariate analysis, and data visualization.

Potential Drawbacks

Lack of focus on implementation or math may deter some learners, as the course only introduces the necessary packages and functions but does not delve into specific syntax.
For those with minimal understanding of R, the extensive amount of content may be overwhelming, making it difficult to grasp concepts and implement them effectively.
The course's strong focus on environmental science datasets might limit its appeal for learners from other disciplines who struggle to relate statistical techniques to their business domain or area of interest.
Recorded lectures with occasional errors in code or language may confuse learners, as these mistakes require the learner to pause and work through problems rather than following along efficiently.