Upcoming dates

All times shown in the class's own timezone. 2 of 3 are guaranteed to run, meaning they go ahead regardless of enrollment. Dates we teach ourselves run once 2 people are booked, so a pair of seats confirms one.

Dates	Starts	Where	Status	Seat
Sep 2-4	9:00 AM to 5:00 PM EDT	Live virtual	GUARANTEED	$1,795
Sep 21-25	9:00 AM to 4:00 PM PDT	Live virtual	RUNS WITH 2+	$2,995
Dec 16-18	9:00 AM to 5:00 PM EST	Live virtual	GUARANTEED	$1,795

About this class

Data analysts are in demand everywhere today! This five-day, instructor-led course shows you how to do data analysis the way the pros do. You’ll master descriptive analysis, using Pandas to analyze the data and Seaborn to create the visualizations that let you present your findings effectively. You’ll get started with predictive analysis, using Scikit-learn with linear regression models. And you’ll be guided right from the start by 4 real-world case studies in political, environmental, social, and sports analytics essential for learning and great perspective for applying your new skills in your own field. See for yourself how quickly and easily this course can turn you into the data analyst that employers are looking for.

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

This course is for anyone who wants to become a data analyst, no matter what the field. It is encouraged to have a background in Python Programming. Course ISI-1481 - Python Programming - is the recommended course to attend prior to ISI-1559B.

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.