# Python for Data Science Bootcamp

Canonical URL: <https://training-nyc.com/courses/python-data-science-nyc>

## Overview

Python is by far the most popular language used by programmers for data science, and it is also quite popular in web applications and game development. Python is considered a high-level programming language, but with its easy syntax and robust documentation, it is considered one of the easiest languages for beginners to learn.

In this 5-day hands-on Python course, you'll learn the fundamentals of Python, and then you'll transition into more complicated programming tasks. We'll focus heavily on data science using Pandas, Matplotlib, and Sci-Kit learn. With these packages, you'll learn how to input, analyze, and make visual representations of data.

**Related Classes:** You may also be interested in our [Data Science Certificate](/courses/data-science) or our other data analytics classes, including [SQL](/course_groups/sql-classes-nyc), [Excel](/course_groups/excel-classes-nyc), and [Tableau](/course_groups/tableau-training-nyc).

## What you'll learn

- Data types within Python which include strings, integers, list, floats, etc.
- Control flow, looping, and function which will allow you to create powerful programs.
- Object-oriented programming, which allows for the creation of reusable programs.
- Combine these skills into a special project in data science.

## 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 — NYC
- Jul 26, 2026 – Aug 23, 2026 — NYC
- Jul 27, 2026 – Jul 31, 2026 — NYC
- Aug 4, 2026 – Sep 3, 2026 — NYC
- Sep 14, 2026 – Sep 18, 2026 — NYC
- Nov 2, 2026 – Nov 6, 2026 — NYC
- Nov 17, 2026 – Dec 22, 2026 — NYC
- Nov 30, 2026 – Dec 4, 2026 — NYC
- Dec 13, 2026 – Jan 10, 2027 — NYC

## Pricing

**Tuition:** $1495

Payment options: GI Bill accepted.