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CSE 111 Plotting and reasoning
创建日期：2024-05-11 17:10:18
标签： CSE 111
Plotting and reasoning

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CSE 111 Project 1

Plotting and reasoning
Description:
In this mini project, we will be using python/jupyter notebook to explore public datasets.
Here is our standard data analysis pipeline:
Standard data analysis contains 5 steps. Due to limited time, the step 2 “Get data” and step 4
“Build a model” are considered conditional extra points in our project. 
You have two options:
1. Work on the preset topic and use the dataset provided. (No bonus points)
2. Work on a custom topic and get your own dataset. (Bonus points if extra work other than
just downloading the dataset)
Instructions:
Step 0:
Install jupyter notebook and create an empty .ipynb file.
Step 1:
Think about what you would like to know first. Ask an interesting question based on the topic.
You need to write a brief summary on why the answer to the question is important.
For example, an interesting question can be “How does the number of tests per day affect the
curve?” the reason why this question is important can be that we want to know the tradeoff
between the cost of tests and the accuracy of the curve so we can make decisions to reduce the
cost of tests potentially.
You have to pick your own question. However, if your question is too trivial, you will get a poor
score.
Step 2:
Perform data acquisition. Think about what data is required to answer the question. If you go
with our covid 19 topic then you can just download the csv however you will still need to perform
data cleaning steps if necessary. Based on the data, you might need to refine your question in
the previous step.
Save the cleaned dataframe to a new .csv.
(Extra points) If you have your own topic then you have to show how you acquire the data, for
instance, webpage scraping, rss, api etc..
Step 3:
Perform Exploratory Data Analysis. You will need to explore the data and your exploration
should answer your question. You may extract the information from the dataframe. You may
need to perform data aggregation. You may need to perform multiple graph drawings etc..
Step 4: (Extra points)
Train-test-validate a model to represent the data. Make predictions based on the model if your
question is about predictions. The model can be a very simple model.
Step 5:
Summarize your findings.
Submission is a zip which contains the .ipynb file,
the raw dataset and the cleaned dataset.
案例分类
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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 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