Probability and Statistics with R
Skill Path
Gain the probability and statistics skills you need to build solid foundations for your data career. You'll learn the basic statistical analysis and probability techniques as well as the fundamentals of R. By the end, you'll be able to gather insights, perform data analysis from start to finish and make educated assumptions for the future.
- Intermediate friendly
- 1 month (5 hrs/week)
- Self paced
- 5 Courses
- 5 projects
Overview of R courses
R skills you'll learn
- ✓ Cleaning, preparing and analyzing data with R
- ✓ Creating insightful data visualizations
- ✓ Using statistics to perform descriptive analytics
- ✓ Using probabilities to perform predictive analysis
Outline of R courses
1 steps · 5 courses
Probability and Statistics with R [5 courses]
Learn probability and statistics for more robust data analysis using R.
- Course 1
Introduction to Statistics in R
5hApply core statistical sampling techniques in R—including random, stratified, and cluster sampling—using hands-on analysis scenarios.
Course Objectives ▾
- Sample data using simple random sampling, stratified sampling, and cluster sampling
- Measure variables in statistics
- Build, visualize, and compare frequency distribution tables
- Course 2
Intermediate Statistics in R
2hApply measures of central tendency and variability in R, using means, medians, standard deviation, and z-scores to compare data.
Course Objectives ▾
- Summarize a distribution using the mean, the weighted mean, the median, or the mode
- Measure the variability of a distribution using the variance and the standard deviation
- Compare values using z-scores
- Course 3
Introduction to Probability in R
1hCompare theoretical and experimental probability in R while calculating event likelihoods using permutations, combinations, and real examples.
Course Objectives ▾
- Estimate theoretical and empirical probabilities
- Define the fundamental rules of probability
- Identify combinations and permutations
- Course 4
Conditional Probability in R
1hApply conditional probability and Bayes’ theorem in R to model dependent events, reason under uncertainty, and build practical Naive Bayes classifiers.
Course Objectives ▾
- Assign probabilities based on conditions
- Assign probabilities based on event independence
- Assign probabilities based on prior knowledge
- Create spam filters using multinomial Naive Bayes
- Course 5
Hypothesis Testing in R
1hUse hypothesis testing in R to assess real-world data with chi-square tests, probability distributions, and statistical significance.
Course Objectives ▾
- Implement probability density functions
- Create testable hypotheses
- Decide which hypotheses to support based on your data
R projects you'll build
5 hands-on projects across the path
Mobile App for Lottery Addiction
For this project, you'll take on the role of a data analyst at a medical institute, using probability and combinatorics in R to develop a mobile app that helps lottery addicts better estimate their chances of winning.
Investigative Statistical Analysis - Analyzing Accuracy in Data Presentation
For this project, you'll be a data journalist analyzing Fandango's movie ratings to determine if there was any change after a 2015 analysis found evidence of bias. You'll use R and statistics skills to compare movie ratings data from 2015 and 2016.
Winning Jeopardy
For this project, we'll assume the role of a Jeopardy contestant analyzing a dataset of past questions, using chi-squared tests and text analysis in R to identify common categories and develop optimal strategies.
Finding the Best Markets to Advertise In
For this project, we'll assume the role of analysts at an e-learning company to determine the two best markets to advertise our programming courses in.
Building a Spam Filter with Naive Bayes
For this project, we'll step into the role of data scientists to build a spam filter for SMS messages. We'll apply conditional probability concepts and use the Naive Bayes algorithm in R.
Earn your Probability and Statistics with R Certificate
Add this R certificate to your resume or LinkedIn to showcase your skills and stand out in job applications.
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