## 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 Python
- Creating insightful data visualizations
- Using statistics to perform descriptive analytics
- Using probabilities to perform predictive analysis

## Path outline

###
**Part 1: ** Python Introduction [4 courses]

### Introduction to Python Programming 3h

Objectives- Write computer programs using Python
- Save values using variables
- Process numerical data and text data
- Create lists using Python

### Basic Operators and Data Structures in Python 5h

Objectives- Use for loops to repeat processes and conduct data analysis
- Implement if, else, and elif statements in programming logic
- Employ logical and comparison operators in Python
- Develop and update Python dictionaries for data manipulation
- Construct frequency tables using dictionaries for data analytics

### Python Functions and Jupyter Notebook 5h

Objectives- Write Python functions
- Debug functions
- Define function arguments
- Write functions that return multiple variables
- Employ Jupyter notebook
- Build a portfolio project

### Intermediate Python for Data Science 8h

Objectives- Clean and analyze text data
- Define object-oriented programming in Python
- Process dates and times

###
**Part 2: ** Data Analysis and Visualization [2 courses]

### Introduction to Pandas and NumPy for Data Analysis 13h

Objectives- Improve your workflow using vectorized operations
- Select data by value using Boolean indexing
- Analyze data using pandas and NumPy

### Introduction to Data Visualization in Python 8h

Objectives- Visualize time series data with line plots
- Define correlations and visualize them with scatter plots
- Visualize frequency distributions with bar plots and histograms
- Improve your exploratory data visualization workflow using pandas
- Visualize multiple variables using Seaborn's relational plots

###
**Part 3: ** Data Cleaning with Python [1 course]

### Data Cleaning and Analysis in Python 11h

Objectives- Employ data aggregation techniques
- Combine datasets
- Transform and reshape data
- Clean strings and resolve missing data

###
**Part 4: ** Probability and Statistics with Python [5 courses]

### Introduction to Statistics in Python 9h

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

### Intermediate Statistics in Python 8h

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 Python 4h

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

### Introduction to Conditional Probability in Python 6h

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 Python 4h

Objectives- Perform a permutation test
- Perform significance testing to understand an outcome's importance
- Define regular and multi-category chi-squared tests

## 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

Impress employers by completing a capstone project and certifying it with an expert review.

## Projects in this path

### Project: Learn and Install Jupyter Notebook

For this project, you’ll take on the role of a Jupyter Notebook beginner. You’ll learn the essentials of running code, adding explanatory text, and installing Jupyter locally to prepare for real-world data projects.

### Guided Project: Profitable App Profiles for the App Store and Google Play Markets

For this project, you’ll be a data analyst at a company that builds free, ad-supported Android and iOS apps. To drive revenue, you’ll analyze real app market data to find app profiles that attract the most users.

### Guided Project: Exploring Hacker News Posts

For this project, we’ll be data analysts exploring Hacker News posts. We’ll use Python string manipulation, OOP, and date handling to analyze trends driving post popularity. Check out our Jupyter Notebook Guided Project if needed.

### Guided Project: Exploring eBay Car Sales Data

For this project, we’ll assume the role of data analysts for a used car classifieds service to explore and clean a dataset of car listings from eBay Kleinanzeigen, a section of the German eBay website.

### Guided Project: Finding Heavy Traffic Indicators on I-94

For this project, you’ll assume the role of a data analyst exploring a dataset on westbound traffic on the I-94 Interstate highway. You’ll apply exploratory data visualization techniques to determine indicators of heavy traffic.