# Python for Data Science Bootcamp

Canonical URL: <https://www.graduateschool.edu/courses/python-data-science-bootcamp>

## Overview

This bootcamp takes you from Python basics to machine learning foundations, covering programming concepts, data analysis with NumPy and Pandas, and visualization with Matplotlib. You’ll finish by building introductory models like logistic regression, k-nearest neighbors, and decision trees, gaining the skills to continue into advanced data science and machine learning.

## What you'll learn

- Learn Python fundamentals, including variables, data types, functions, loops, and control flow, for building robust programs
- Work with complex data structures such as dictionaries and lists to efficiently organize and access data
- Use NumPy and Pandas to import, clean, and manipulate datasets for analysis and exploration
- Generate descriptive statistics and apply filtering, grouping, and pivoting techniques to gain deeper insights
- Visualize data using Matplotlib and create clear, customized charts, including bar graphs, histograms, and scatter plots
- Gain the practical skills needed to transition into machine learning with a solid understanding of data science workflows

## Curriculum

### Python Fundamentals

#### Python Fundamentals: Variables & Data Types

- Declare variables of basic types: integers, floats, strings, booleans
- Perform input/output with print() and input()
- Apply arithmetic, relational, and logical operators

#### Control Flow I: Conditional Logic

- Use Boolean operators ==, !=, \<, \>, \<=, \>=
- Write if/else and nested conditionals
- Combine conditions with and/or for complex logic

#### Control Flow II: Loops & Iteration

- Implement for loops over ranges and lists; understand iterables
- Understand map and filter operations.
- Use list comprehensions to simplify operations.

#### DataFrames & Data Manipulation with Pandas

- Construct DataFrames from various data formats via pd.DataFrame()
- Concatenate multiple DataFrames using pd.concat()
- Inspect DataFrame shape and handle missing values (NaN)
- Perform Panda data analysis operations to glean insight

#### Data Visualization: Charting Basics

- Plot time series with plt.plot() for line charts
- Create scatter plots using plt.scatter() to reveal correlations
- Decide between line vs. scatter based on data context and purpose

#### Trend Analysis with Regression Lines

- Understand least-squares regression concept and its interpretation
- Compute a best-fit line via numpy.polyfit()
- Overlay regression lines on scatter plots and make predictions

#### Advanced Plot Customization

- Annotate charts with titles, axis labels, and legends
- Highlight key data points (e.g., min/max) directly on plots
- Use stacked bar charts, pie charts, and animated charts to visualize data

## Schedule
- Jun 8, 2026 – Jun 12, 2026 — Live Online
- Jul 26, 2026 – Aug 23, 2026 — Live Online
- Jul 27, 2026 – Jul 31, 2026 — Live Online
- Aug 4, 2026 – Sep 3, 2026 — Live Online
- Sep 14, 2026 – Sep 18, 2026 — Live Online
- Nov 1, 2026 – Nov 15, 2026 — Live Online
- Nov 2, 2026 – Nov 6, 2026 — Live Online
- Nov 17, 2026 – Dec 22, 2026 — Live Online
- Dec 13, 2026 – Jan 10, 2027 — Live Online

## Pricing

**Tuition:** $1495

Payment options: GI Bill accepted.