- Will finish data discovery lecture and will give a new assignment
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Six groups presenting in parallel: presentation is 5min+2m questions
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Agree on a person to send to next round
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Groups were obtained using a complicated text analysis to get representation of different groups, similarities and dissimilarities of text in commits and issues
- jtyler7 joseph346 chumekaboom kylebshr nwilder0 gsimpson723
- rerwin21 ryanwagn kdunn13 teaguejt awachte tjonesster
- matsuobasho stonecoldhughes justa-ghost w4d3 sbradfo5 tapjdey
- ryancaldwell1 jlong49 codyjae jaredmichaelsmith davpcunn spicychckn
- beamad12 almasaeed2010 mbenkhayal milanjpatel rroper1 inthesunset cwilker
- millermoore nateige jalomas7 rhoque-icl mtwe curtis017 alexklibisz
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Here is what the presentation should touch upon
- What is the question?
- What was the approach?
- What problems did I encounter?
- What results did I get?
- What new ideas did this generate?
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The list of problems encountered will be collected and summarized
- The current stats on commits/issues are at https://github.com/fdac15/Assignment1/Astats.ipnb
- The lecture material is at https://github.com/fdac15/presentations dd.pdf
- Example of using the stemmer at the end of https://github.com/fdac15/Assignment1/Astats.ipnb
- How to enable R in ipython notebook: will restart docker containers Sunday at 5AM, if it is an issue for you (are running a long script) please let me know. The home directory/files will be preserved.
- Look forward to presentations on Friday!
- Great progress on Assignment1! A few notes:
- Please crete an issue commonting on your peer's project (above you in the dendrogram) if you have not done so yet.
- Please write at least a paragraph interpreting the observed differences, e.g., a. Do differences between authors appear to be larger or smaller (or the same) as differences within book of the same author? b. Why?
- Guttenberg is already blocked
- Download to your laptop then scp to da2:Assignment1
- Follow example at the end of the latest version of Assignment1.ipnb to red data from file
- Text is public domain (sowhere on the web) but not on PG
- Use appropriate url to the text
- Text is spread over multiple URLs
- Follow example at the end of the latest version of Assignment1.ipnb
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How to copy files from/to docker container?
- In Mac/Linux (da2 is the host entry in ~/.ssh/config as described below)
scp -P YOURPORT fileToBeCopiedTo da2:Assignment1 scp -P YOURPORT da2:Assignment1/fileToBeCopiedFrom .- Using windows: putty has pscp command line that is similar to scp, but winscp provides more "windows-like" experience. Change the netid from audris to your netid and also change the port from 9001 to your port (to be found in students/ports.md).
- Please fork and clone: let me know if you have any issues
- Please make sure you go to settings -> enable issues after the fork
- To your peer's fork is at https://github.com/PEERsGITHUBID/Assignment1
- Will be discussing questions issues during class of [Aug 28]
- Pay attention to the schedule of activities (Social workflow) in fdac15/Assignment1
- Some of you already submitted it :-) fdac15/Homework0: this is an exercise of:
- using git command line in your docker container
- playing with ipython notebook
- If you were not in the class Aug 21, please take a look at the presentation tools.pdf in https://github.com/fdac15/presentations
- The lecture slides for today are in prelim.pdf at https://github.com/fdac15/presentations
- Please let me know if the ssh/notebook are still not working for you
- Please make sure your ghid.md file is not empty and contains meaningful info: check if your .md file is there and not empty and, if not, please submit a pull request.
- I am still waiting for pull requests with .md file and ssh (in lists.txt) from several of you, please submit your ghid.md file and public ssh key if you have not done so yet. I need it to add you to the class organization on github and key to enable paswordless login
- Please accept github group membership: I am adding you to the group once you submit a pull request, but you still need to accept it. Some of the homeworks and slides will be in private repositories for which you will need group membership.
- Please try to ssh to da2 using windows (or mac/linux) instructions: if you are able to do that, then on your laptop browser please enter http://localhost:8888 to access your personal ipython notebook server. You can create python2, python3, and R notebooks. We will primarily use python3 and, later in the course, R
- If you'd prefer to run the ipython notebooks on your own laptop (not from the sever) please install docker infrastructure per https://www.docker.com/toolbox.
- The docker image used for the class is audris/ipython-pymongo:v15
- You will need to forward the port 888 to port 8888 on that docker container on your laptop
- You will also need to start 'ipython notebook --no-browser' in the container
- The slides for the lecture today (tools.pdf) are in https://github.com/fdac15/presentations
[Aug 19] As noted in the initial assignement please create github id and ssh key, then fork and create a PR
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On linux/mac either
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create .ssh/config
- create ~/.ssh/config
host da2 hosthostname da2.eecs.utk.edu port YOURPORT from students/ports.md username YOURNETID- place your private key in ~/.ssh/id_rsa
- Make sure permissions are right
chmod -R og-rwx ~/.ssh- ssh da2
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Or ssh directly
ssh -pYOURPORT -L8888:localhost:8888 -i id_rsa [email protected]
-
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Putty is a common ssh client for windows
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Instructions on how to generate ssh key running windows
- Save the private key and use it in your putty ssh session
- Copy the public key (highlited in the image) to add to the list.txt
- Now work on creating and saving session: start putty and go to connection/ssh/tunnels, enter source and destination and click add
- Go to go to connection/ssh/Auth and browse for your private key
- Go to go to session enter hostname and YOUR PORT from ports.md in fdac15/students
- Don't forget to save the session before clicking open
- Course: COSCS-445/COSCS-545
- ** MK405 2:30-3:20 MWF**
- Instructor: Audris Mockus, [email protected]
- TA: Tapajit Dey, [email protected]
- Need help?
Simple rules for questions:
- There are no stupid questions.
- Think of what the right answer may be.
- Search online: stack overflow, etc.
- code snippets: gist.github.com
- answers to questions: Stack Overflow
- Look through issues
- Post the question as an issue
- Office hours: MON: 1 pm - 2:20 pm, TUE: 9:45 am - 11 am. Location: MK620 -- TA himself is a student in the class, so he might not be able to assist with everything. However, feel free to contact him if you are having any problems or need any resources to help with anything. Even if he is not able to solve everything, he can bring the problem to the instructor's attention.
- Ask instructor: email for 1-on-1 help, or to set up a time to meet
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.
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:
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Use Python and other tools to discover, retrieve, and process data.
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Use data management techniques to store data locally and in the cloud.
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Use data analysis methods to explore data and to make predictions.
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.
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Ethics: legal aspects, privacy, confidentiality, governance
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Reproducibility: version control, ipython notebook
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Fundamentals of big data analysis: extreme distributions, transformations, quantiles, sampling strategies, and logistic regression
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The nature of digital traces: lack of context, missing values, and incorrect data
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:
- How to look up Python syntax on Google and StackOverflow.
- Basic programming concepts like functions, loops, arrays, dictionaries, strings, and if statements.
- How to learn new libraries by reading documentation and reusing examples
- Asking questions on StackOverflow or as a GitHub issue.
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)
- 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
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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. Responding to other student questions (issues) counts as classroom participation.
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Assignments - 40%: Each assignment will involve writing (or modifying a template of) a small Python program.
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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.
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.
- GitHub
- Sign up for GitHub if not already signed up. Pick default (free plan).
- Create ssh key
- Do steps 1, 2, 4, and 5
- Do Not Share Your Private Key in id_rsa
- Fork and create a pull request on
students repository so I
can add you to the to the GitHub group for the course.
- Start by forking the students repository
- Add your GitHub username as USERNAME.md (click on '+' - add
new file next to the https//github.com/fdac15/students/+ link)
- Add your netid and publickey key (in id_rsa.pub) to list.txt
- Click on Create Pull Request
- Familiarize yourself with GitHub workflow
- Walk through workflow
- To start, fork the repository for the exercise/project (found under github.com/fdac15)
- Clone the repository to your computer.
- View, create, and edit your ipython notebooks or other files
- commit changes to complete your solution.
- Push/sync the changes up to GitHub.
- Create a pull request on the original repository by the due time (generally within a week)
- You can continue to push fixes and improvements until the close date – just add a comment in the pull request to let me know it's been updated.
Feedback will be given in the pull request, so please respond with
your thoughts and questions! You are welcome to open the pull
request as the work is still in-progress if you are stuck and want
to ask a question – just mention @audris with the question to make
sure I know to look at it sooner.
This class assumes you are confident with this material, but in case you need a brush-up...
- Codecademy – Python and Python Dictionaries
- See also – Other
- Modern Applied Statistics with S (4th Edition) by William N. Venables, Brian D. Ripley. ISBN0387954570
- R
- Code School
- Quick-R
- Git and GitHub
- GitHub Pages