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  • Data Bootcamp

Introduction to Python

  • Installations and Introduction to your Terminal
  • Python Fundamentals 1
  • Data Structures and Introduction to Pandas
  • Intro to pandas
  • Introduction to pandas
  • Missing Values and Split Apply Combine
  • datetime, and matplotlib intro
  • More plotting with matplotlib and seaborn
  • More EDA, Time Series, and Geospatial Data
  • Accessing Data with API’s
  • Extracting Data From HTML

Modeling with Python

  • Inference and Hypothesis Testing
  • Introduction to Linear Regression and Linear Programming
  • Model Complexity and Evaluation
  • Regression Part II
  • Introduction to Classification and K-Nearest Neighbors
  • Classification II: Logistic Regression
  • Evaluating Classification Models
  • Introduction to Decision Trees
  • Random Forests
  • Boosted Ensembles
  • PCA: A Brief Introduction
  • Unsupervised Learning

Artificial Neural Networks

  • Introduction to Artificial Neural Networks
  • Convolutional Nets in PyTorch
  • Text Classification with Neural Networks
  • Using a pretrained model and dataset from huggingface
  • Fine tuning a model with torchvision

Bonus

  • Using a pretrained model and dataset from huggingface
  • Huggingface and gradio
  • Pretrained Models in an app

Library and WRDS

  • 0. Setup WRDS Python API
  • Data Workflow Steps

Projects

  • Midterm Project

Homework

  • Assignment 1
  • Homework 3: Advanced Pandas and Introductory Plotting
  • A Regression Model for Wages
  • Problem 1: Difference in Groups

Class Notebook Solutions

  • Python Fundamentals 1
  • Data Structures and Introduction to Pandas
  • Introduction to pandas
  • Missing Values and Split Apply Combine
  • datetime, and matplotlib intro
  • More plotting with matplotlib and seaborn
  • Accessing Data with API’s
  • Extracting Data From HTML
  • Introduction to Linear Regression and Linear Programming
  • Regression Part II

Extras

  • Climb the Ladder!
  • Plotting with bokeh
  • Repository
  • Open issue

Index

By Lenny

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