Python for Data Science
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 Is Included
- Expert Instruction – Learn from industry professionals with real-world experience in data science.
- 90 Day Access to On-Demand Videos – Reinforce your learning with exclusive reference videos
- Guaranteed to Run – No last-minute cancellations! Your course is guaranteed to take place, so you can plan with confidence.
- Flexible Rescheduling – Need to change your course date? Rescheduling options are available (see conditions).
- FREE Course Retake – Reinforce your skills by retaking the course at no additional cost (see conditions).
- Develop Real-World Data Science Projects – Apply your skills to hands-on projects using Jupyter Notebook and real-world datasets.
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.
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.
To be successful in this course, learners should have the level of knowledge and experience gained from Introduction to Python.
- 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