How much does Python for Data Science cost?

$1,795 per seat. That covers courseware and lab access. Exam vouchers are quoted separately. Volume pricing is available for teams, and a private cohort is usually cheaper per seat past about four people.

How long is Python for Data Science?

3 days of instructor-led training, delivered live online. Classes run during business hours in the timezone shown on each date.

When is the next Python for Data Science class?

The next scheduled date is Dec 16-18. There is 1 upcoming date in total, 1 of which is guaranteed to run regardless of enrollment.

Is Python for Data Science delivered online?

Yes. It runs live online with an instructor, not as a recording, so you can ask questions as you go. It can also be delivered privately at your site.

Can a government agency buy Python for Data Science?

Yes. Purchase orders and government purchase cards are both accepted at checkout, and quotes can be formatted for a solicitation response. Terms are net 30 on invoiced orders.

What happens if I need to cancel Python for Data Science?

Course fees are fully refundable with written notice at least ten business days before the class. Inside that window the fee is forfeited, though one free retake is available within six months of the class date.

Python for Data Science | 3-day class

Upcoming dates

All times shown in the class's own timezone. 1 of 1 are guaranteed to run, meaning they go ahead regardless of enrollment.

Dates	Starts	Where	Status	Seat	Book
Dec 16-18	9:00 AM to 5:00 PM EST	Live virtual	GUARANTEED	$1,795

About this class

We have 1 scheduled date of Python for Data Science, starting Dec 16-18. It is guaranteed to run regardless of enrollment. Seats are $1,795 each over 3 days, courseware included. Book online with a card, or send a purchase order and we will invoice on net 30 terms.

Did you know that data professionals spend up to 80% of their time cleaning and preparing data? Python is the industry’s go-to language for streamlining this process, making it an essential tool for anyone looking to analyze, visualize, and derive insights from data.

Master the complete data science tech stack essential for landing a job at the world’s leading companies. This Python for Data Science course takes a structured, in-depth approach, helping you not only learn how to apply data science but also why it matters. Through a carefully balanced mix of real-world case studies and the mathematical theory behind key data science algorithms, you’ll develop both the practical skills and foundational understanding needed to excel in the field.

Please note, this course is able to be offered in either 3 full day sessions or 5 evening sessions. See the schedule below.

What you'll be able to do

The Python for Data Science course teaches the fundamentals of Python for data analysis and visualization. Participants will work with key libraries like Pandas, NumPy, Matplotlib, and Seaborn to clean, transform, and analyze data. They will create interactive visualizations to communicate insights effectively and apply their skills through hands-on projects using Jupyter Notebook and real-world datasets.

Course outline

1. Introduction to Python for Data Science

Overview of Python and its role in data science
Setting up Python environments (Anaconda, Jupyter Notebooks)
Writing and running Python scripts

2. Working with Jupyter Notebooks

Introduction to Jupyter Notebooks
Markdown and code cells
Running, saving, and sharing notebooks

3. Numerical Computing with NumPy

Understanding arrays and their advantages
Creating and manipulating NumPy arrays
Mathematical operations and broadcasting

4. Data Manipulation with Pandas

Understanding Series and DataFrames
Importing and exploring datasets
Filtering, sorting, and transforming data

5. Data Input and Output (I/O)

Reading and writing Excel files
Working with CSV files
Connecting and querying SQL databases

6. Converting Datasets to Pandas DataFrames

Transforming structured and unstructured data
Importing datasets from APIs and web sources

7. Advanced Data Handling

Altering specific data using custom functions
Handling missing data, filling, dropping, and imputing values
Aggregating data using group operations

8. Data Visualization with Matplotlib

Creating fully customizable plots
Implementing custom figures and axis
Adding labels, legends, and annotations

9. Statistical Data Visualization with Seaborn

Creating scatter plots
Generating distribution plots
Visualizing summary statistics with box plots

10. Hands-on Projects and Real-World Applications

Data analysis case studies
End-to-end data science project
Best practices for working with large datasets

Before you attend

To be successful in this course, learners should have the level of knowledge and experience gained from Introduction to Python

Who this is for

Intermediate Python developers looking to use Python to explore and visualize large or complex data sets. Check out our Introduction to Python course if you’re new to Python.