Materials for the course "Statistical Modeling and Inference with Python" for O'Reilly by Dr. Chester Ismay

Course content

The major files in this repository are

slides.pdf: PDF version of the slides used in this course to motivate the code.
spotify_sample.csv: Data for code walkthroughs
imdb_movie_sample.csv: Data for student exercises.
exercises.ipynb: A Jupyter Notebook with pseudocode/instructions provide to be filled in for code walkthroughs and student exercises
exercises_solutions.ipynb: A Jupyter Notebook with answers to the code walkthroughs and exercises.

Recommended instructions on getting set up with Python and Jupyter Notebook

If you aren't able to do this on your machine, you may want to check out Google Colab. It's a free service that allows you to run Jupyter notebooks in the cloud. Alternatively, I've set up some temporary notebooks on Binder here that you can work with online as well.

Step 1: Install Python

Option 1: Anaconda Installation:
- Download Anaconda: Go to the official Anaconda website and download the latest version of Anaconda for your operating system (Windows, macOS, or Linux). Anaconda conveniently installs Python, Jupyter Notebook, and many other commonly used packages for data science and machine learning.
Option 2: Python Installation:
- Download Python: Alternatively, you can download Python directly from the official Python website and install the latest version for your operating system.

Step 2: Launch Jupyter Notebook

Launch Jupyter Notebook:
- Anaconda: After installing Anaconda, open Anaconda Navigator from your Start Menu (Windows) or using the Anaconda Navigator application (macOS/Linux). In Anaconda Navigator, find Jupyter Notebook in the list of available applications and click on the "Launch" button. This will open Jupyter Notebook in your default web browser.
- Python Installation: Open your command prompt (Windows) or terminal (macOS/Linux), and install Jupyter using pip:
```
pip install notebook
```

Step 3: Install Required Libraries

Open Anaconda Prompt (Windows) or Terminal (macOS/Linux) (if using Anaconda), or open your command prompt (Windows) or terminal (macOS/Linux) (if using Python installation).

Install Required Libraries using conda (Anaconda):

conda install numpy pandas scipy matplotlib seaborn statsmodels scikit-learn

Install Required Libraries using pip (if not using Anaconda):

pip install numpy pandas scipy matplotlib seaborn statsmodels scikit-learn

Step 4: Launch Jupyter Notebook

Open your command prompt or terminal.
Run Jupyter Notebook:
```
jupyter notebook
```
This command will open Jupyter Notebook in your default web browser.

Step 5: Verify Installation

Create a new notebook: In the Jupyter Notebook interface, click on "New" and select "Python 3" to open a new notebook.

Test the installation of the libraries:

Import the libraries in the first cell of your notebook:

 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
 import seaborn as sns
 import statsmodels.api as sm
 from sklearn.model_selection import train_test_split
 from sklearn.linear_model import LinearRegression
 from sklearn.metrics import mean_squared_error, r2_score

Run the cell (Shift + Enter). If no errors appear, the libraries are installed correctly.

Additional Tips

Troubleshooting: If you encounter any errors during installation, make sure that your pip is up to date (pip install --upgrade pip) and try the installation commands again. If issues persist, check for specific error messages online for troubleshooting tips.
Learning Resources: Familiarize yourself with the Jupyter Notebook interface and basic functionality by reading tutorials or watching introductory videos about Jupyter Notebooks.

ai-ml-architect / oreilly-statistical-modeling-and-inference-with-python