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Noble Desktop

Python Data Science & Machine Learning Program NYC (High School & College)

via Noble Desktop

Overview

Learn one of the most powerful and versatile programming languages this summer. In this 2-week course, high school students will explore Python for data science and machine learning.

Syllabus

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

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