## Path overview

In this path, you’ll learn the foundations of statistics such as sampling, working with variables, and understanding frequency distribution tables and the fundamentals of probability and how to use them for analysis. You’ll also learn how to create and test hypotheses with significance testing, and how to make forecasts based on patterns and trends with real-world data.

Best of all, you’ll learn by doing — you’ll write code and get feedback directly in the browser. You’ll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.

## Key skills

- Cleaning, preparing and analyzing data with R
- Creating insightful data visualizations
- Using statistics to perform descriptive analytics
- Using probabilities to perform predictive analysis

## Path outline

###
**Part 1: ** Probability and Statistics with R [5 courses]

### Introduction to Statistics in R 5h

Objectives- Sample data using simple random sampling, stratified sampling, and cluster sampling
- Measure variables in statistics
- Build, visualize, and compare frequency distribution tables

### Intermediate Statistics in R 3h

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

### Introduction to Probability in R 1h

Objectives- Estimate theoretical and empirical probabilities
- Define the fundamental rules of probability
- Identify combinations and permutations

### Conditional Probability in R 2h

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

### Hypothesis Testing in R 1h

Objectives- Implement probability density functions
- Create testable hypotheses
- Decide which hypotheses to support based on your data

## The Dataquest guarantee

Dataquest has helped thousands of people start new careers in data. If you put in the work and follow our path, you’ll master data skills and grow your career.

We believe so strongly in our paths that we offer a full satisfaction guarantee. If you complete a career path on Dataquest and aren’t satisfied with your outcome, we’ll give you a refund.

## Master skills faster with Dataquest

### Go from zero to job-ready

Learn exactly what you need to achieve your goal. Don’t waste time on unrelated lessons.

### Build your project portfolio

Build confidence with our in-depth projects, and show off your data skills.

### Challenge yourself with exercises

Work with real data from day one with interactive lessons and hands-on exercises.

### Showcase your path certification

Share the evidence of your hard work with your network and potential employers.

## Projects in this path

### Guided Project: Investigating Fandango Movie Ratings

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.

### Guided Project: 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.

### Guided Project: 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.

### Guided Project: 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.

### Guided Project: 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.