Skip to content

fdac22/news

BranchesTags

Repository files navigation

Final Project Reports (Due Dec 10)

Similar to proposals, but note additional sections:

  • Objective (research question)
  • Data that was used: how obtained, how processed, integrated, and validated
  • What models or algorithms were used
  • Results: A description of the results
  • Primary issues encountered during the project
  • Future work: ideas generated, improvements that would make sense, etc
  • Org chart: rough timeline and responsibilities for each member

Presentations on Dec 6: (6m+q/a)

  • Animal Detection
  • Terrain Mapping
  • Clickbait Detection
  • Rating Day Trading
  • Cardiac Health
  • Travel Recommendations
  • Steam Reviews Analysis
  • Camera analysis
  • league_winrate_calculator
  • AI Book
  • SPAM
  • DDOS
  • Whale locations Salary Prediction
  • Car stats
  • NFL Game Prediction

Presentations on Dec 1: (8m+q/a)

  • Password Analysis
  • NFL Upset Prediction
  • Social Media Sentiment
  • Premire Leage Prediction
  • NBA Analytics
  • Science Fiction Analysis
  • Solar Panel Readings Analysis and Prediction
  • Ages and Attitudes Towards Terror
  • Federated Learning
  • Baseball Over the Years
  • Genre Popularity
  • Driving Stress Detection
  • League of Legends Build Calculator

Presentations on Nov 29:

  • UTK Research Computing Job Trace and Storage Analysis
  • Reddit Analysis

Class on 15, 17, 22

  • Work on presentations/reports

Class on Nov 10

  • Q/A
  • MP3D due Nov 15

Class on Nov 8

  • MP3D
  • Q/A

Class on Nov 3

Class on Nov 1

  • Intro to MP3C
  • Q/A

Class on Oct 25, 27

  • work on class projects

Class on Oct 20

  • Intro to MP3 part B
  • Q/A on MP3A
  • Q/A gcp

Class on Oct 18

  • no need to participate if done with MP3 and have no questions on GCP
  • Q/A on MP3A
  • Q/A gcp

Class on Oct 13

  • Intro to gcp (fixed)
  • Q/A on MP3A

Class on Oct 11

  • MP2 due
  • Intro to MP3

Class on Oct 6

  • Fall break

Class on Oct 4

Class on Sep 29

Class on Sep 29

  • Data Dicovery
  • Introducing MP2 and GCP
  • Final questions about the proposal
  • The proposal pdf will be committed by the end of day on Sep 29 to fdac22/ProjectName/proposal.pdf.

Class on Sep 27

  • Work on your proposals! Class zoom is only to answer questions you may have about the proposal.
  • The group needs to submit a project proposal (1.5-2 pages in IEEE format (see https://www.overleaf.com/latex/templates/preparation-of-papers-for-ieee-sponsored-conferences-and-symposia/zfnqfzzzxghk) with the following content
  • an objective
  • a brief motivation for the project,
  • detailed discussion of the data that will be obtained or used in the project,
  • responsibilities of each member, along with
  • a time-line of milestones, and
  • the expected outcome
  • The proposal pdf will be committed by the end of day on Sep 29 to fdac22/ProjectName/proposal.pdf.

Class on Sep 22

- if you already have a team please
   - ask to create your own discord channel for this course
   - work on the project proposal (see Sep 27)
- if you need team members or have no project 
   - please join class zoom call 
   - find your project-mates

Class on Sep 20

- Finish boasters, form most teams
- Finish MP1 presentations planned for Sep 15
    - groups 13, 15, 14, 6, 11

Class on Sep 15

- Presenting MP1 results by the representatives of each group
  - be ready to share it from your zoom session;
  - the presentations will go in group order (the representative from the first group, the second group...)
- Class(final) project boasters

Class on Sep 13

- Presenting MP1 rsults in the assigned groups

Class on Sep 8

  • Unfinished parts from Sep 6

  • Make sure you have

    a. Forked fdac22/Miniproject1
b. Posted the idea for your analysis on your peer's fork
c. Responded to the idea that was posted by your peer

  • Boasters for class project

  • Question regarding MP1

Class on Sep 6

  • Magic of Internet
  • See the simple text analysis of your descriptions
  • Introducing the MiniProject1 process and template
  • Think about selecting the course project (see course projects for the last six years at fdac21, fdac20, fdac19, fdac18, fdac17, fdac16, fdac for inspiration)
  • Boasters for class project (if you have an idea for the class project, please commit to fdac22/FinalProjectPitches)

Class on Sep 1

Class on Aug 30

  • Please accept your invitation to fdac22 organization while logged in to GH via handle you used to submit pull request
  • If you have not done so yet, please accept github fdac22 invitation (48 accepted 17 pending as of 15:44PM on Aug 30. Also,the class has 107 students, so many of you have not completed instructions for Aug 25th class!)
  • then
    • fork repo students
    • create your utid.md file providing your name and interests: see per fdac22/students/README.md and upload your ssh key to github. Once done, please
    • submit a pull request to fdac22/students
  • Introductory lecture

Class on Aug 25

  • Create your github account
    • fork repo students
    • create your utid.md file providing your name and interests and what you want to get out of the course (at least a full paragraph, see example): see per fdac22/students/README.md, and also upload your your public ssh key to your account on github. Once done, please
    • submit a pull request to fdac22/students
  • Make sure you do it a day before the next class so we can start ready

Information for remote participation via Zoom / Discord

Syllabus for "Fundamentals of Digital Archeology"

Simple rules:

  1. There are no stupid questions. However, it may be worth going over the following steps:
  2. Think of what the right answer may be.
  3. Search online: stack overflow, etc.
  4. Look through issues
  5. Post the question as an issue.
  6. Ask instructor: email for 1-on-1 help, or to set up a time to meet

Objectives

The course will combine theoretical underpinning of big data with intense practice. In particular, approaches to ethical concerns, reproducibility of the results, absence of context, missing data, and incorrect data will be both discussed and practiced by writing programs to discover the data in the cloud, to retrieve it by scraping the deep web, and by structuring, storing, and sampling it in a way suitable for subsequent decision making. At the end of the course students will be able to discover, collect, and clean digital traces, to use such traces to construct meaningful measures, and to create tools that help with decision making.

Expected Outcomes

Upon completion, students will be able to discover, gather, and analyze digital traces, will learn how to avoid mistakes common in the analysis of low-quality data, and will have produced a working analytics application.

In particular, in addition to practicing critical thinking, students will acquire the following skills:

  • Use Python and other tools to discover, retrieve, and process data.

  • Use data management techniques to store data locally and in the cloud.

  • Use data analysis methods to explore data and to make predictions.

Course Description

A great volume of complex data is generated as a result of human activities, including both work and play. To exploit that data for decision making it is necessary to create software that discovers, collects, and integrates the data.

Digital archeology relies on traces that are left over in the course of ordinary activities, for example the logs generated by sensors in mobile phones, the commits in version control systems, or the email sent and the documents edited by a knowledge worker. Understanding such traces is complicated in contrast to data collected using traditional measurement approaches.

Traditional approaches rely on a highly controlled and well-designed measurement system. In meteorology, for example, the temperature is taken in specially designed and carefully selected locations to avoid direct sunlight and to be at a fixed distance from the ground. Such measurement can then be trusted to represent these controlled conditions and the analysis of such data is, consequently, fairly straightforward.

The measurements from geolocation or other sensors in mobile phones are affected by numerous (yet not recorded) factors: was the phone kept in the pocket, was it indoors or outside? The devices are not calibrated or may not work properly, so the corresponding measurements would be inaccurate. Locations (without mobile phones) may not have any measurement, yet may be of the greatest interest. This lack of context and inaccurate or missing data necessitates fundamentally new approaches that rely on patterns of behavior to correct the data, to fill in missing observations, and to elucidate unrecorded context factors. These steps are needed to obtain meaningful results from a subsequent analysis.

The course will cover basic principles and effective practices to increase the integrity of the results obtained from voluminous but highly unreliable sources.

  • Ethics: legal aspects, privacy, confidentiality, governance

  • Reproducibility: version control, ipython notebook

  • Fundamentals of big data analysis: extreme distributions, transformations, quantiles, sampling strategies, and logistic regression

  • The nature of digital traces: lack of context, missing values, and incorrect data

Prerequisites

Students are expected to have basic programming skills, in particular, be able to use regular expressions, programming concepts such as variables, functions, loops, and data structures like lists and dictionaries (for example, COSC 365)

Being familiar with version control systems (e.g., COSC 340), Python (e.g., COSC 370), and introductory level probability (e.g., ECE 313) and statistics, such as, random variables, distributions and regression would be beneficial but is not expected. Everyone is expected, however, to be willing and highly motivated to catch up in the areas where they have gaps in the relevant skills.

All the assignments and projects for this class will use github and Python. Knowledge of Python is not a prerequisite for this course, provided you are comfortable learning on your own as needed. While we have strived to make the programming component of this course straightforward, we will not devote much time to teaching programming, Python syntax, or any of the libraries and APIs. You should feel comfortable with:

  1. How to look up Python syntax on Google and StackOverflow.
  2. Basic programming concepts like functions, loops, arrays, dictionaries, strings, and if statements.
  3. How to learn new libraries by reading documentation and reusing examples
  4. Asking questions on StackOverflow or as a GitHub issue.

Requirements

These apply to real life, as well.

  • Must apply "good programming style" learned in class
    • Optimize for readability
  • Bonus points for:
    • Creativity (as long as requirements are fulfilled)

Teaming Tips

  • Agree on an editor and environment that you're comfortable with
  • The person who's less experienced/comfortable should have more keyboard time
  • Switch who's "driving" regularly
  • Make sure to save the code and send it to others on the team

Evaluation

  • Class Participation – 15%: students are expected to read all material covered in a week and come to class prepared to take part in the classroom discussions (online). Asking and responding to other student questions (issues) counts as a key factor for classroom participation. With online format and collaborative nature of the projects, this should not be hard to accomplish.

  • Assignments - 40%: Each assignment will involve writing (or modifying a template of) a small Python program.

  • Project - 45%: one original project done alone or in a group of 2 or 3 students. The project will explore one or more of the themes covered in the course that students find particularly compelling. The group needs to submit a project proposal (2 pages IEEE format) approximately 1.5 months before the end of term. The proposal should provide a brief motivation of the project, detailed discussion of the data that will be obtained or used in the project, along with a time-line of milestones, and expected outcome.

Other considerations

As a programmer you will never write anything from scratch, but will reuse code, frameworks, or ideas. You are encouraged to learn from the work of your peers. However, if you don't try to do it yourself, you will not learn. deliberate-practice (activities designed for the sole purpose of effectively improving specific aspects of an individual's performance) is the only way to reach perfection.

Please respect the terms of use and/or license of any code you find, and if you re-implement or duplicate an algorithm or code from elsewhere, credit the original source with an inline comment.

Resources

Materials

This class assumes you are confident with this material, but in case you need a brush-up...

Other

Databases
  • A MongoDB Schema Analyzer. One JavaScript file that you run with the mongo shell command on a database collection and it attempts to come up with a generalized schema of the datastore. It was also written about on the official MongoDB blog.
R and data analysis
  • Modern Applied Statistics with S (4th Edition) by William N. Venables, Brian D. Ripley. ISBN0387954570
  • R
  • Code School
  • Quick-R
Tutorials written as ipython-notebooks

GitHub

About

All news related to the class

Resources

Readme
Activity
Custom properties

Stars

2 stars

Watchers

4 watching

Forks

2 forks
Report repository

Releases

No releases published

Packages

No packages published

Contributors 3

  •  
  •  
  •