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https://github.com/ishaansathaye/data301-introdatascience

Cal Poly Fall 2023 DATA 301 Introduction to Data Science
https://github.com/ishaansathaye/data301-introdatascience

cal-poly data-science intro

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Cal Poly Fall 2023 DATA 301 Introduction to Data Science

Host: GitHub
URL: https://github.com/ishaansathaye/data301-introdatascience
Owner: ishaansathaye
Created: 2023-09-25T05:06:23.000Z (about 1 year ago)
Default Branch: main
Last Pushed: 2023-11-08T07:48:35.000Z (about 1 year ago)
Last Synced: 2023-11-08T21:55:08.053Z (about 1 year ago)
Topics: cal-poly, data-science, intro
Language: Jupyter Notebook
Homepage:
Size: 11 MB
Stars: 1
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

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README

        # DATA 301 Introduction to Data Science

## Notes

- Intro

  - [Introduction to Notebooks and Google Colab](./notes/intro/Introduction_to_Notebooks_and_Colab.ipynb)

- Quiz 1

  - [Tabular Data](./notes/quiz1/Tabular_Data.ipynb)

  - [Rows and Observational Units](./notes/quiz1/Rows_and_Observational_Units.ipynb)

  - [Columns and Variables](./notes/quiz1/Columns_and_Variables.ipynb)

  - [Lecture 1: Introduction to Tabular Data](./notes/quiz1/Day_01_Introduction_to_Tabular_Data.ipynb)

- Quiz 2

  - [Summarizing One Categorical Variable](./notes/quiz2/Summarizing_One_Categorical_Variable.ipynb)

  - [Summarizing Two Categorical Variables](./notes/quiz2/Summarizing_Two_Categorical_Variables.ipynb)

  - [Lecture 2: Categorical Variables](./notes/quiz2/Day_02_Categorical_Variables.ipynb)

- Quiz 3

  - [Multi Way Tables and Simpson's Paradox](./notes/quiz3/Multi_Way_Tables_and_Simpson's_Paradox.ipynb)

  - [Summarizing a Quantitative Variable](./notes/quiz3/Summarizing_a_Quantitative_Variable.ipynb)

  - [Lecture 3A: Multi-way Tables and Simpson's Paradox](./notes/quiz3/Day_03A_Multi_Way_Tables_and_Simpson's_Paradox.ipynb)

  - [Lecture 3B: Summarizing a Quantitative Variable](./notes/quiz3/Day_03B_Summarizing_a_Quantitative_Variable.ipynb)

- Quiz 4

  - [Transforming Variables](./notes/quiz4/Transforming_Variables.ipynb)

  - [Summarizing Two Quantitative Variables](./notes/quiz4/Summarizing_Two_Quantitative_Variables.ipynb)

  - [Lecture 4: Quantitative Variables](./notes/quiz4/Day_04_Quantitative_Variables.ipynb)

- Quiz 5

  - [Split-Apply-Combine](./notes/quiz5/Split_Apply_Combine.ipynb)

  - [Moving Beyond Two Variables](./notes/quiz5/Beyond_Two_Variables.ipynb)

  - [Lecture 5: Split-Apply-Combine and Summarizing Categorical and Quantitative Variables](./notes/quiz5/Day_05_Relationships_between_Quantitative_and_Categorical_Variables.ipynb)

- Quiz 6

  - [The Grammar of Graphics](./notes/quiz6/Grammar_of_Graphics.ipynb)

  - [Lecture 6: Data Visualization](./notes/quiz6/Day_06_Data_Visualization.ipynb)

- Quiz 7

  - [Distances between Observations](./notes/quiz7/Distances_Between_Observations.ipynb)

  - [Lecture 7: Distances Between Observations](./notes/quiz7/Day_07_Distances_Between_Observations.ipynb)

- Quiz 8

  - [Hierarchical Data and JSON](./notes/quiz8/Hierarchical_Data_and_JSON.ipynb)

  - [Lecture 8: Hierarchical Data and JSON Data Format](./notes/quiz8/Day_08_JSON_Data_Format_and_APIs.ipynb)

- Quiz 9

  - [The XML Data Format](./notes/quiz9/The_XML_Data_Format.ipynb)

  - [HTML and Web Scraping](./notes/quiz9/HTML_and_Web_Scraping.ipynb)

  - [Lecture 09: XML, HTML, and Web Scraping](./notes/quiz9/Day_09_XML,_HTML,_and_Web_Scraping.ipynb)

- Quiz 10

  - [Bag of Words and N-Grams](./notes/quiz10/Bag_of_Words_and_N_Grams.ipynb)

  - [The Vector Space Model](./notes/quiz10/The_Vector_Space_Model.ipynb)

  - [Lecture 10: Intro to Text Data](./notes/quiz10/Day_10_Introduction_to_Text_Data.ipynb)

- Quiz 11

  - [Concatenating and Merging Data](./notes/quiz11/Concatenating_and_Merging_Data.ipynb)

  - [Types of Joins](./notes/quiz11/Types_of_Joins.ipynb)

  - [Lecture 11: Concatenating and Merging (Joining) Data](./notes/quiz11/Day_11_Concatenating_and_Joining_Data.ipynb)

    - [Solutions](https://colab.research.google.com/drive/1Yxs8M9cVzyPz849km5cRSVrPxSQLmkUY?usp=sharing)

- Quiz 12

  - [Linear Regression](./notes/quiz12/Linear_Regression.ipynb)

  - [Categorical Features in Regression Models](./notes/quiz12/Categorical_Features_in_Regression_Models.ipynb)

  - [Lecture 12: Linear Regression](./notes/quiz12/Day_12_Linear_Regression.ipynb)

- Quiz 13

  - [K-Nearest Neighbors for Regression](./notes/quiz13/K_Nearest_Neighbors_for_Regression.ipynb)

  - [Lecture 13: K-Nearest Neighbors for Regression](./notes/quiz13/Day_13_K_Nearest_Neighbors_for_Regression.ipynb)

- Quiz 14

  - [Training and Test Errors](./notes/quiz14/Training_and_Test_Errors.ipynb)

  - [Estimating the Test Error](./notes/quiz14/Estimating_the_Test_Error.ipynb)

  - [Lecture 14: Training and Test Errors](./notes/quiz14/Day_14_Training_and_Test_Errors.ipynb)

- Quiz 15

  - [Model Selection and Hyperparameter Tuning](./notes/quiz15/Model_Selection_and_Hyperparameter_Tuning.ipynb)

  - [Lecture 15: Model Selection and Hyperparameter Tuning](./notes/quiz15/Day_15_Model_Section_and_Hyperparameter_Tuning.ipynb)

- Quiz 16

  - [Ensemble Models for Regression](./notes/quiz16/Ensemble_Methods_for_Regression.ipynb)

  - [Lecture 16: Ensemble Models for Regression](./notes/quiz16/Day_16_Ensemble_Methods_for_Regression.ipynb)

- Quiz 17

  - [K-Nearest Neighbors for Classification](./notes/quiz17/K_Nearest_Neighbors_for_Classification.ipynb)

  - [Evaluating Classification Models](./notes/quiz17/Evaluating_Classification_Models.ipynb)

  - [Lecture 17: K-Nearest Neighbors for Classification](./notes/quiz17/Day_17_K_Nearest_Neighbors_for_Classification.ipynb)

- Quiz 18

  - [Estimating Test Metrics for Classification Models](./notes/quiz18/Estimating_Test_Metrics_for_Classification.ipynb)

  - [Lecture 18: Estimating Test Metrics for Classification](./notes/quiz18/Day_18_Estimating_Test_Metrics_for_Classification.ipynb)

- Quiz 19

  - [Time Series Data](./notes/quiz19/Time_Series_Data.ipynb)

  - [Time Series Modeling and Forecasting](./notes/quiz19/Time_Series_Forecasting.ipynb)

  - [Lecture 19: Time Series](./notes/quiz19/Day_19_Time_Series.ipynb)

- Quiz 20

  - [K Means Clustering](./notes/quiz20/K_Means_Clustering.ipynb)

  - [Lecture 20: Unsupervised Learning - K Means Clustering](./notes/quiz20/Day_20_K_Means_Clustering.ipynb)

- Quiz 21

  - [Hierarchical Clustering](./notes/quiz21/Hierarchical_Clustering.ipynb)

  - [Lecture 21: Hierarchical Clustering](./notes/quiz21/Day_21_Hierarchical_Clustering.ipynb)

## Assignments

- [Assignment 1](./assignments/DATA_301_Assignment_1_Sreshta_Talluri_and_Ishaan_Sathaye.ipynb)

  - [PDF](./assignments/DATA_301_Assignment_1_Sreshta_Talluri_and_Ishaan_Sathaye.pdf)

- [Assignment 2A](./assignments/assignment2/DATA_301_Assignment_02A_Ishaan_Sathaye_and_Sreshta_Talluri.ipynb)

  - [PDF](./assignments/assignment2/DATA_301_Assignment_02A_Ishaan_Sathaye_and_Sreshta_Talluri.pdf)

- [Assignment 2B](./assignments/assignment2/DATA_301_Assignment_02B_Ishaan_Sathaye_and_Sreshta_Talluri.ipynb)

  - [PDF](./assignments/assignment2/DATA_301_Assignment_02B_Ishaan_Sathaye_and_Sreshta_Talluri.pdf)

- [Assignment 3](./assignments/assignment3/DATA_301_Assignment_3_Ishaan_Sathaye_and_Sreshta_Talluri.ipynb)

  - [PDF](./assignments/assignment3/DATA_301_Assignment_3_Ishaan_Sathaye_and_Sreshta_Talluri.pdf)

- [Assignment 4](./assignments/assignment4/DATA_301_Assignment_04_ISHAAN_SATHAYE_SRESHTA_TALLURI.ipynb)

  - [PDF](./assignments/assignment4/DATA_301_Assignment_04_ISHAAN_SATHAYE_SRESHTA_TALLURI.pdf)

- [Assignment 5A](./assignments/assignment5/DATA_301_Assignment_5A_ISHAAN_SATHAYE_SRESHTA_TALLURI.ipynb)

  - [PDF](./assignments/assignment5/DATA_301_Assignment_5A_ISHAAN_SATHAYE_SRESHTA_TALLURI.pdf)

- [Assignment 5B](./assignments/assignment5/DATA_301_Assignment_5B_ISHAAN_SATHAYE_SRESHTA_TALLURI.ipynb)

  - [PDF](./assignments/assignment5/DATA_301_Assignment_5B_ISHAAN_SATHAYE_SRESHTA_TALLURI.pdf)

- Assignment 6

  - [Day 16 PDF](./assignments/assignment6/Day_16_Ensemble_Methods_for_Regression.pdf)

  - [Day 18 PDF](./assignments/assignment6/Day_18_Estimating_Test_Metrics_for_Classification.pdf)

## Project

- Phase 2

  - [Collect and Clean Notebook](./project/phase2/clean.ipynb)

  - [Visualizations](./project/phase2/visualization.ipynb)

  - [Executive Summary Notebook](./project/phase2/summary.ipynb)