Statistics for Data Analysis Using R

Learn Programming in R & R Studio • Descriptive, Inferential Statistics • Plots for Data Visualization • Data Science

4.55 (2451 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Learn R programming from the ground up, starting from the basics and progressing to advanced data analysis techniques.

Learn the basic statistical concepts first, followed by practical application using R Studio, combining theory and practice for effective learning.

Master descriptive statistics, including mean, mode, median, skewness, and kurtosis, and how to apply these concepts to your data analysis.

Understand and perform inferential statistics such as one and two-sample z-tests, t-tests, Chi-Square tests, F-tests, ANOVA, and TukeyHSD, and more.

Explore probability distributions, including normal, binomial, and Poisson, and their applications in data analysis.

Develop the skills to perform data manipulation, visualization, and statistical analysis using R.

Apply statistical concepts in real-world scenarios, enhancing your problem-solving abilities and decision-making skills.

Boost your career prospects with a strong foundation in statistics and R programming, valuable skills in today’s data-driven job market.

Equip yourself with the tools to handle large datasets and perform complex statistical analyses with confidence.

Enhance your ability to make data-driven decisions by mastering the use of R for statistical analysis.

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Our Verdict

This Statistics for Data Analysis Using R course by Sandeep Kumar excels at blending statistical theory with practical application via R programming. As a result, students gain valuable skills that can be transferred to real-world data analysis scenarios. Though the course moves quickly and may not provide enough practice opportunities for all learners, its detail-oriented approach is ideal for those looking to grasp key concepts before moving on to advanced studies. This Udemy course is an excellent starting point for students with little to no background in statistics or R programming, setting them up for long-term success.

What We Liked

The course offers a strong foundation in both statistics theory and R programming, enabling effective data analysis.
Instructor's clear, step-by-step approach facilitates learning, making it suitable for beginners and intermediate students alike.
Exercises and example-driven teaching style help solidify understanding of statistical concepts and R coding techniques.
Comprehensive course content covers a wide range of topics, from basic commands to advanced data manipulation and visualization techniques.

Potential Drawbacks

Pacing may be too fast for some learners, particularly those with limited statistical or programming background.
Later sections lack practice assignments, which could affect the overall learning experience for some students.
While explanations are detailed, they might not cover all statistics concepts in depth, leaving advanced learners wanting more.
Limited focus on specific applications of R in social research