$1,795
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.
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
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.
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
