Introduction to Data Culture 2019-2020

Materials

Your grades are here

№	Topic	Slides	Tutorial and Assignment	Additional Materials
1	Introduction to Data Science	Slides1	Practice1	Rules for plotting graphs
2	Data Characteristics	Slides2	Practice2
3
4
5-6
7

Teamwork

First two homeworks are considered to be made individually. Later, you can work on the practice assignments in teams, you are free to choose and organise. Teams can contain two or three people.

Communication

All course materials, assignments, deadlines will be published on very wiki.

All practical announcements concerning the whole course (such as lecture cancellation) will appear on the Telegram channel https://t.me/joinchat/Du3HEUStPxVGVwnEvkzokw

Also, please name your tasks in this format: Practice#PracticeNumber#_#Group number#_#Team number#_#Names of participants#, where the Team number is the number of the team in which you work during the seminar.

For example: Practice1_BMOL181_Ivanov

Group	Teacher	Teaching Assistant	Schedule
BMOL191	Marina Ananyeva Email Telegram	Egor Chernousov	Thursday 09:00 - 10:20
BMOL192	Marina Ananyeva	Valeriya Rusina Email Telegram	Thursday 10:30 - 11:50
BMOL193	Marina Ananyeva	Liana Gainutdinova Email Telegram	Monday 09:00 - 10:20
BMOL194	Marina Ananyeva	Polina Semenova Email Telegram	Tuesday 09:00 - 10:20
BMOL195	Ilya Abroskin Email Telegram	Andrey Chernikov Email Telegram	Monday 13:40-15:00
BMOL196	Ilya Abroskin Email Telegram	Andrey Chernikov Email Telegram	Monday 12:10-13:30
BMOL197	Elena Kantonistova	Mikhail Belkin	Monday 10:30-11:50
BMOL198	Elena Kantonistova	Mikhail Belkin	Monday 09:00-10:20

Links for submitting homeworks

for group BMOL191: Practice 1

for group BMOL192: Practice 1 Practice 2

for group BMOL193: Practice 1

for group BMOL194: Practice 1

for group BMOL195: Practice 1 Practice 2 Practice 3 Practice 4 Practice 5-6 Practice 7

for group BMOL196: Practice 1 Practice 2 Practice 3 Practice 4 Practice 5-6 Practice 7

for group BMOL197: Practice 1

for group BMOL198: Practice 1

Feedback

We’ll much appreciate if you help us to make it better by telling your ideas and suggestions. You are free to do it!

Anonymous feedback form: click_here

About the course

The course is for students of Bachelor’s Programme 'HSE and University of London Parallel Degree Programme in International Relations'.

Your seminar teachers are: Elena Kantonistova and Marina Ananyeva.

Grading

Final Grade = 0.5*(Homework + Extra Points*) + 0.2*Control Work + 0.3*Examination Assessment

• In the beginning of each seminar students are asked to perform tests to check knowledge based on the materials of previous seminars. Each 100% correctly completed test provides the student with extra points that go towards ongoing assessment.

Deadlines

You must submit each seminar assignment by the beginning of the next one - otherwise you get 0 for it.

Attendance

It is better to attend all lectures to be aware of your material. However, there are two caveats though:

You are responsible for catching up with the material and timely submission of all the assignments. We reserve the right to respond “RTFM” to questions from people who missed the corresponding class for no good reason. HSE bureaucracy has asked us to submit the roll-call of each seminar - they might apply some punishment for truancy.

Force majeure

We use the standard HSE rules to determine what counts as a force majeure - illness and stuff. If you encounter one, please email the practice teacher ASAP and then provide the proof documents to the student's office (учебный офис). Your deadlines will be extended by the duration of the illness, and help with the missed material will be offered. Also since you won't be working in a team, you need to complete only half of the obligatory tasks to get a 10, and half of the bonus tasks to get the full grade for the bonus tasks.

Fast line

If you find yourself offended by a proposition to do data analysis in Excel because you are a Tensorflow contributor, or for any other reason you already know everything this course has to offer - please come forward. Prove yourself a competent data scientist (something line achieving a reasonable place on a Kaggle competition, and passing an oral examination - the precise way will be negotiated and publicized). You’ll get a full grade on Ongoing Assessment and a free pass on all the ongoing assignments in exchange for attending the practical lessons in the role of teaching assistant - helping you classmates and answering their questions.

Hail Mary

If by the end of the course you find yourself with 5 non-zero grades for tests, but not enough Ongoing Assessment credits to earn a passing grade, there will be a way to make it up with a few extra assignments, for up to the lowest “Satisfactory”. There will be deadlines, they’ll be announced. Please don’t let the situation to deteriorate to that.

Retaking the final exam is governed by the standard HSE rules.

Cheating and honor

You must abide by the Honor Code.

Please don’t cheat - the rumor has it HSE has quite severe penalties.

To avoid being accused of plagiarism in “grey cases” please disclose with whom and how you have collaborated on each assignment outside of the assigned group. This way the worst thing that can happen to you after a good faith mistake is being asked to do a different variant of the task in question, with no disciplinary penalty and no notification of the HSE bureaucracy.

Rules status

We did our best to lay out what’s expected from students in the Data Culture course and don’t expect to make substantial alteration to this document. However, should a loophole arise and be abused, we reserve the right to alter the rules. This of course will be announced on the course channel.

MS Excel

The official and supported version of Microsoft Excel for this course is 2016. It is readily available to HSE students - just login (and possibly register with your @edu.hse.ru email) on the website. In case you have trouble downloading, there is a manual.