Difference between revisions of "User:Alexanderr"

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Information gained from data analysis will be compiled into a Landscape Report for the state of Wisconsin, and will serve as a reference for elected officials to determine statewide educational policy and funding. Also, this new TABot functionality will allow student programmers to learn from their mistakes and avoid potentially dangerous bugs before deploying code, as well as reduce the amount of time professors need to laboriously examine code.
 
Information gained from data analysis will be compiled into a Landscape Report for the state of Wisconsin, and will serve as a reference for elected officials to determine statewide educational policy and funding. Also, this new TABot functionality will allow student programmers to learn from their mistakes and avoid potentially dangerous bugs before deploying code, as well as reduce the amount of time professors need to laboriously examine code.
  
=='''Milestones:'''==
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=='''Work Log:'''==
 
{| class="wikitable"
 
{| class="wikitable"
 
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|'''Description'''
 
|'''Description'''
 
|-
 
|-
|Week 1: Orientation
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|Week 1
 
|
 
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*Meet with REU students and mentors
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*Initial meetings with mentor
*Learn about introductory data science topics
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*Data science crash course
*Meet with my mentor to talk about the project
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*Outlined possible project directions
 
|-
 
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|Week 2: Android/iOS programming
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|Week 2
 
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*Continue programming feedback activity (Android)
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*Re-familiarized myself with the C language
*Continue programming serverside feedback activity (Php, mysql)
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*Completed first few OS projects on Dr. Brylow's website
*Start programming feedback activity (iOS)
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*Watches machine learning courses
*TBD: Collect data from apps
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*Met with Sudjeeth to review goals and available data
 
|-
 
|-
|Week 3: Group Messaging
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|Week 3
 
|
 
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*Read articles about implementing a group messaging system
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*Read papers concerning static analysis of C code
*Find best practices for implementing one
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*Implemented Python script to scrape DPI website
*TBD: Collect data from the system
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*Used pandas and scikit-learn to clean data
|-
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*Compiled enrollment statistics and discuss with Sudjeeth
|Week 4: Natural Language Processing
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|
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*Read articles about NLP
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*Find best practices for implementing one
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*Practice implementing NLP with a kaggle dataset
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|-
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|Week 5: TBD - Collect data
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|
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*Hopefully have veterans use apps to get data
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*Will update wiki when I know we'll have veterans
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|-
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|Week 6: TBD - Extract data
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|
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*Implement PHP/python script to export data into Excel or Csv file
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|-
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|Week 7: NLP on Data
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|
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*Using the data, create a ML alg that uses NLP
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*Use survey data, feedback data, and group message data
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|-
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|Week 8: Research Paper
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|
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*Plan to work on the paper throughout the whole summer, but this week will be used for writing the majority of my paper and refining it since I will have my data and ML alg output
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|-
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|Week 9: Poster
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|
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*Create poster by taking info from paper
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|-
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|Week 10: Finale
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*Finish up critiquing paper/poster
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*Present poster
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|}
 
|}

Latest revision as of 16:52, 18 June 2020

About Me

My name is Alex Rambasek. I'm a rising junior at Case Western Reserve University in Cleveland, Ohio, studying Computer Science and Mathematics. My areas of concentration in CS is artificial intelligence.


Project Description

I'm working alongside Dr. Dennis Brylow and Sudjeeth Ramagoni in the Summer of 2020 to help clean, analyze, interpret, and predict data about high school CS education in the state of Wisconsin. Furthermore, I am helping to integrate static code analysis into a series of bash scripts known as TABot, used at Marquette to grade student code against instructor provided test cases in courses such as Operating Systems.


Why Should I Care?

Information gained from data analysis will be compiled into a Landscape Report for the state of Wisconsin, and will serve as a reference for elected officials to determine statewide educational policy and funding. Also, this new TABot functionality will allow student programmers to learn from their mistakes and avoid potentially dangerous bugs before deploying code, as well as reduce the amount of time professors need to laboriously examine code.

Work Log:

Week Description
Week 1
  • Initial meetings with mentor
  • Data science crash course
  • Outlined possible project directions
Week 2
  • Re-familiarized myself with the C language
  • Completed first few OS projects on Dr. Brylow's website
  • Watches machine learning courses
  • Met with Sudjeeth to review goals and available data
Week 3
  • Read papers concerning static analysis of C code
  • Implemented Python script to scrape DPI website
  • Used pandas and scikit-learn to clean data
  • Compiled enrollment statistics and discuss with Sudjeeth