Python Data Science & Machine Learning High School Program NYC

Introduction to Programming

• History of Python
• Understanding Hardware
• Anaconda Distribution
• Jupyter Notebook Fundementals
• Writing First Program (“Hello World”)

Terminal Commands

• Navigate & Manipulate Directory Strcutres
• Edit Files
• Basic Scripting

Python Fundamentals

• Data Types
• Operators
• Expression
• Indexing & Slicing
• Strings
• Conditionals
• Functions
• Control Flow
• Nested Loops
• Sets & Dictionaries

Data Science Fundementals

• Import Data
• Functions
• Basic Data Tool

Advanced Python Fundementals

• Lists
• Mutating Operations
• Tuples, Sets, Dictionaries
• Loops
• Control Flow
• List Comprehension
• Error Handeling

Processing

• String Methods
• Read & Write to Text Files
• Natrual Language Processing
• Mini Project

Object Oriented Programming

• Classes
• Constrcutors
• Object Methods
• Writing Modules
• Advanced Scripting
• Terminal & Socket Connection

Numerical Python

• Arrays
• Universal Functions
• Concatenating, Indexing, Slicing
• Arithmetic & Boolean Operations

Python Data Analysis:Pandas 1

• Data Series
• Data Frames
• Import CSV & Excel Files
• Organize Data Frames
• Data Manipulation
• Descriptive Statstics

Advanced Python

• File Input
• User Input
• List Comprehension
• Packages

Data Analysis

• Cleaning Data
• Filtering Data
• Advanced Grouping
• Pivot Tables

Data Visualization

• Plotting with Matplotlib
• Scatter Plots
• Histograms & Bar Plots
• Custom Visualizations

Machine Learning Fundamentals

Basic Regression Analysis

• Linear Regression
• Mean squared error
• Training set vs Test set
• Cross validation

Advanced Regression Analysis

• Multi-linear regression
• Feature engineering
• Overfitting

Classification

Logistic Regression

• Regression vs Classification
• Logistic Regression
• Sigmoid function

K-nearest Neighbors

• K-nearest neighbors
• Model-based vs memory-based
• Parametric vs non-parametric
• Evaluating performance

Final Project

Details

• Curate Data
• Import, Clean, and Merge Data
• Analyze Data
• Visualize Data
• Present Results