Assignments

Homework, Projects, Vitamins, Labs, Discussions

Contents

Homework

Homework 6: Housing Price Prediction

Homework 5: Modeling and Gradient Descent

Homework 4: SQL, FEC Data, and Small Donors

Homework 3: EDA of Bike Sharing

Homework 2: Food Safety Data Cleaning and EDA

Homework 1: Setup, Prerequisites, and Image Classification

Projects

Project 2: Spam vs. Ham Classification

Project 1: Twitter Analysis

Vitamins

Vitamin 10

Vitamin 9

Vitamin 8

Vitamin 7

Vitamin 6

Vitamin 5

Vitamin 4

Vitamin 3

Vitamin 2

Vitamin 1

Labs

Lab 12: Hypothesis Testing, Baby Weights

Lab 11: Logistic regression

Lab 10: Feature Engineering and Cross-Validation

Lab 9: Bootstrap

Lab 8: Modeling and Estimation

Lab 6: Regular Expressions, SQL

Lab 4: Plotting, Smoothing, Transformation

Lab 3: Plotting

Lab 2: Pandas Overview

Lab 1: Setup and Notebook

How to submit lab or homework

Working locally

  1. Download the assignment zip file from https://github.com/DS-100/sp18/tree/master/zipfiles

  2. Unzip the assignment, navigate to the assignment directory and open the jupyter notebook.

  3. Complete the notebook assignment.

  4. Go to datahub: http://data100.datahub.berkeley.edu

  5. Create a new folder, rename it to the assignment name. (lab01 for example).

  6. Go to assignment folder, and upload everything you created in assignment folder. (For example, ‘lab01.ipynb’ and ‘figure1.pdf’)

  7. Run the notebook (‘lab01.ipynb’ for example), click ‘validate’. Make sure it passes all tests. Save it.

  8. Go to “Assignments” tab

  9. Click ‘submit’. You can do so multiple times.

  10. Done!

Working on datahub

  1. Go to datahub: http://data100.datahub.berkeley.edu

  2. Go to “Assignments” tab, fetch the assignment (for example, lab01)

  3. Go to “Files” tab, start working on the assignment.

  4. After you are done, be sure to click ‘validate’ and save.

  5. Go to “Assignments” tab.

  6. Click ‘submit’. You can do so multiple times.

  7. Done!

Previous Semester’s Materials

You can take a look at materials from the Spring 2017 github repository.

You can also obtain all the original slides from last semester here google drive and all datasets are available here.