Python For Data Science
Training for Your Group
- Private class for your team
- Online or on-location
- Onsite testing available
Training for Individuals
$1695
- Live, Instructor-led training
- Expert instructors
- Hands-on instruction
Course Overview
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.
Course Length: 3 Days
Audience: This course is ideal for both new programmers and experienced developers seeking to add Python to their skillset.
Prerequisites: Some experience in working with data from Excel, databases, or text files.
What You're Going To Learn
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.
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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
What to Expect at LeapFox
Knowledgeable Instructors
Our instructors are certified professionals. They are trained on the latest features and how to get the most out of software programs.
Hands-on Labs
No boring lectures! Our courses are designed to give students lots of time to practice what they are learning with hands-on exercises and projects.
Certificate of Completion
Receive a certificate of completion at the end of every course.
Up-to-date Curriculum
Each course comes with a helpful and up-to-date ebook which will contain instruction and practice exercises.
Time Saving Tips N Tricks
In each course, your instructor will show you tips and tricks that will save you time and make you more efficient.
Friendly and Helpful Staff
Our staff is dedicated to your success. Each team member is trained to provide the absolute best customer service possible.
Satisfaction Guarantee
If you aren't 100% satisfied with your experience at LeapFox, simply let us know, and we will make it right.
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