{ "cells": [ { "cell_type": "markdown", "id": "c88bbad0-7979-4dda-b499-391682c733ab", "metadata": {}, "source": [ "###
San Jose State University
Department of Applied Data Science

**DATA 200
Computational Programming for Data Analytics**

Spring 2024
Instructor: Ron Mak

**Assignment #8
Simple Data Analysis**

Assigned: March 21, 2024
Due: March 28 at 5:30 PM

100 points maximum
with maximum 10 points extra credit
Individual work only!
" ] }, { "cell_type": "markdown", "id": "1255637c-c9d1-4220-9612-fe2b72f8af4d", "metadata": {}, "source": [ "#### **Use the analysis of the Titanic survival data as a model.**" ] }, { "cell_type": "markdown", "id": "4621868e-fee2-4794-aff0-327930b50239", "metadata": {}, "source": [ "#### **PROBLEM 1.** [20 points] Find and download from the web a CSV file containing data that you find interesting. Use the `csv` module to read the file. Enter the data into a suitable data structure. Print at most about 25 records of the file as a nicely formatted table.\n", "#### **TIP:** Google \"csv files for data analysis\" to find data." ] }, { "cell_type": "markdown", "id": "c6837199-8819-4c4f-a145-97e902c3fab1", "metadata": {}, "source": [ "#### **PROBLEM 2.** [20 points each] Analyze the data and calculate, print, and graph four statistics of your choosing." ] }, { "cell_type": "markdown", "id": "03aee398-3714-4540-8fb3-811977ca21e4", "metadata": { "tags": [] }, "source": [ "#### **Grading rubric**\n", "| Criteria | Max points |\n", "| --- | :-: |\n", "| CSV file downloaded, read, and printed in a table. | 20 |\n", "| Statistic #1 calculated and printed. | 10 |\n", "| Statistic #1 graphed. | 10 |\n", "| Statistic #2 calculated and printed. | 10 |\n", "| Statistic #2 graphed. | 10 |\n", "| Statistic #3 calculated and printed. | 10 |\n", "| Statistic #3 graphed. | 10 |\n", "| Statistic #4 calculated and printed. | 10 |\n", "| Statistic #4 graphed. | 10 |" ] }, { "cell_type": "markdown", "id": "672b2fa5-3097-4d89-b8eb-b04ae0d4ef06", "metadata": {}, "source": [ "#### **Additional credit**" ] }, { "cell_type": "markdown", "id": "f1a77e3f-43ec-4a88-8db5-fed7fc144483", "metadata": {}, "source": [ "| Criteria | Max points |\n", "| --- | :-: |\n", "| `TitanicERROR.ipynb` error identified and corrected. | 10 |" ] }, { "cell_type": "code", "execution_count": null, "id": "c1577ccc-74f5-4587-b0f9-729a690c736a", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 5 }