Python for Data Science
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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.
Request More InfoUpcoming Class Schedule
$1,795.00
Sep 02 - Sep 04, 2026
9:00 AM - 5:00 PM America/New_York- VILT
- Dec 16 - Dec 18, 2026
- 9:00 AM - 5:00 PM America/New_York
- VILT
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.
The Python for Data Science Course
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.
Who should attend Python for Data Science Course
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.
Prerequisites for Python for Data Science Course
Python for Data Science Course Outline
- Overview of Python and its role in data science
- Setting up Python environments (Anaconda, Jupyter Notebooks)
- Writing and running Python scripts
- Introduction to Jupyter Notebooks
- Markdown and code cells
- Running, saving, and sharing notebooks
- Understanding arrays and their advantages
- Creating and manipulating NumPy arrays
- Mathematical operations and broadcasting
- Understanding Series and DataFrames
- Importing and exploring datasets
- Filtering, sorting, and transforming data
- Reading and writing Excel files
- Working with CSV files
- Connecting and querying SQL databases
- Transforming structured and unstructured data
- Importing datasets from APIs and web sources
- Altering specific data using custom functions
- Handling missing data – filling, dropping, and imputing values
- Aggregating data using group operations
- Creating fully customizable plots
- Implementing custom figures and axis
- Adding labels, legends, and annotations
- Creating scatter plots
- Generating distribution plots
- Visualizing summary statistics with box plots
- Data analysis case studies
- End-to-end data science project
- Best practices for working with large datasets
Resources
FAQs on Python for Data Science
Learners should have foundational Python knowledge equivalent to completing an Introduction to Python course before enrolling in this intermediate-level course.
This course is designed for intermediate Python developers who want to explore, analyze, and visualize large or complex data sets. Beginners should first take an Introduction to Python course.
You will work with Pandas, NumPy, Matplotlib, and Seaborn for data analysis and visualization, and use Jupyter Notebook as the primary development environment throughout hands-on projects.
Topics include Python environment setup, Jupyter Notebooks, NumPy arrays, Pandas data manipulation, data I/O with Excel, CSV, and SQL, API data imports, and advanced data handling techniques like managing missing data.
You’ll be able to clean, transform, and analyze real-world datasets, create interactive data visualizations, and apply data science workflows using Python libraries within Jupyter Notebook through hands-on projects.