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Data-Centric Statistical Inference Using R and Tidyverse
Gain insights into data visualization with ggplot2, data wrangling, and modeling in R. Delve into statistical inference using the infer package to enhance your data science skills.
99 Lessons
2 Projects
17h
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- Working knowledge of R, ModernDive, and tidyverse to process data
- Hands-on experience of data preprocessing, wrangling, and visualization
- The ability to apply data-centric concepts for regression analysis, sampling, confidence intervals, and hypothesis testing and interpretation
- The ability to tell a story using data of different case studies
- The ability to infer insights and conclusions from data using different packages
Learning Roadmap
1.
Getting Started with Data in R
Getting Started with Data in R
Get familiar with data analysis in R, from visualization to coding and practical projects.
2.
Data Visualization
Data Visualization
Get started with visualizing data relationships using ggplot2 in R's Tidyverse framework.
3.
Data Wrangling
Data Wrangling
9 Lessons
9 Lessons
Examine data transformation techniques for efficient analysis using dplyr and Tidyverse.
4.
Data Importing and “Tidy” Data
Data Importing and “Tidy” Data
6 Lessons
6 Lessons
Grasp the fundamentals of importing and organizing tidy data using Tidyverse in R.
5.
Basic Regression
Basic Regression
13 Lessons
13 Lessons
Map out the steps for basic regression principles using R and Tidyverse functionalities.
6.
Multiple Regression
Multiple Regression
9 Lessons
9 Lessons
Tackle multiple regression, interaction models, residuals, and model selection using visualizations.
7.
Statistical Inference with the infer Package
Statistical Inference with the infer Package
8 Lessons
8 Lessons
Master the steps to statistical inference using tactile and virtual sampling techniques, emphasizing randomness and sample sizes.
8.
Bootstrapping and Confidence Intervals
Bootstrapping and Confidence Intervals
13 Lessons
13 Lessons
Sharpen your skills in bootstrapping, resampling, and constructing confidence intervals using the infer package.
9.
Hypothesis Testing
Hypothesis Testing
9 Lessons
9 Lessons
Unpack the core of hypothesis testing, evaluating gender discrimination, movie ratings, and theory-based methods.
10.
Inference for Regression
Inference for Regression
9 Lessons
9 Lessons
Master the steps to analyze, interpret, and infer regression relationships using theory and simulations.
11.
Tell a Story with Data
Tell a Story with Data
4 Lessons
4 Lessons
Grasp the fundamentals of effective data storytelling with R, focusing on analysis and visualization.
12.
Appendix
Appendix
2 Lessons
2 Lessons
Dig deeper into R, RStudio, and essential R packages for enhanced data analysis.
Certificate of Completion
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Developed by MAANG Engineers
ABOUT THIS COURSE
The world is full of data and it can be quite challenging to infer the knowledge based on statistical reasoning. This course aims to equip you with the skills you need to play with your data, wrangle it, and visualize it.
Starting with data visualization, the course gets learners building ggplot2 graphs early on and then continues to reinforce important concepts graphically throughout the course. After moving through data wrangling and data importing, you’ll be introduced to modeling, which plays a prominent role, with a focus on building regression models and inference for regression. Lastly, statistical inference is presented through a computational lens with calculations done via the infer package.
By the end of this course, you’ll develop your data science toolbox, equipping yourself with tools such as data visualization, data formatting, data wrangling, and data modeling using regression.
ABOUT THE AUTHOR
Dr. Chester Ismay
Experienced data science leader and mentor, co-author of 'Statistical Inference via Data Science.' Expert in R, Python. Developed R packages, holding a PhD in Statistics. Skilled in machine learning, consulting, and software development.
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