Просмотр исходного текста страницы Data Analysis in Journalism and Political Science 2020-2021

==About the course==

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

'''Abstract:'''
In this intermediate Python course, you will learn how to apply data science methods and techniques to political science and journalism. This course will provide you with knowledge and skills in data mining, exploratory data analysis, and visualization. The practical classes are project-oriented and cover the basic topics of data science applications used in the media: topic modeling, sentiment analysis, social network analysis, and classical machine learning approaches. By the end of the course, you will be able to perform your own projects in political science and journalism in Python.

Syllabus: [https://drive.google.com/file/d/1SSK05QXB12jdaORTdASaMz8_lxp_Hc0W/view?usp=sharing open]

==Required Software==

* Anaconda 
or separately installed:
* Python version >= 3.6
* Jupyter Notebook
* pip3 for installing Python libraries during the course

==Materials==

Presentations and all materials will be available immediately after each practice class. Additional materials will be used in quizzes at each next seminar.

Github with the materials from our practical classes:
https://github.com/anamarina/Data_Analysis_in_Journalism_and_Political_Science

{| class="wikitable"
|-
 ! Week !! Topic !! Slides !! Tutorial !! Additional Materials !! Assignment !! Deadline 
|-
| style="background:#eaecf0;" | '''1''' || Introduction || [https://docs.google.com/presentation/d/1sz_IHa8t1wnU-piEko6fi12heS8dagl1O3QEAsumovk/edit?usp=sharing Intro] || [https://github.com/anamarina/Data_Analysis_in_Journalism_and_Political_Science/blob/master/week1_intro/Refresh_Python_basics.Part%201.ipynb Part1], [https://github.com/anamarina/Data_Analysis_in_Journalism_and_Political_Science/blob/master/week1_intro/Refresh_Python_basics.Part_2.ipynb Part 2]||  || || 
|-
| style="background:#eaecf0;" | '''2''' || Parsing || - || [https://github.com/anamarina/Data_Analysis_in_Journalism_and_Political_Science/blob/master/week2_parsing/sem2_parsing.ipynb sem2_parsing.ipynb] ||  || ||
|-
| style="background:#eaecf0;" | '''3''' || Exploratory Data Analysis & Visualization || - || [https://github.com/anamarina/Data_Analysis_in_Journalism_and_Political_Science/blob/master/week3_eda/sem3_eda.ipynb sem3_eda.ipynb] ||  || ||
|-
| style="background:#eaecf0;" | '''4''' || Social network analysis || - || [https://github.com/anamarina/Data_Analysis_in_Journalism_and_Political_Science/blob/master/week4_graphs/sem4_graphs.ipynb sem4_graphs.ipynb] || 1. [https://towardsdatascience.com/getting-started-with-graph-analysis-in-python-with-pandas-and-networkx-5e2d2f82f18e Getting started with graph analysis in pandas and networks (Fraud detection example)] <br> 2. [https://www.coursera.org/learn/python-social-network-analysis#syllabus Coursera.Applied Social Network Analysis in Python] || || 
|-
| style="background:#eaecf0;" | '''5''' || ||  ||  ||  || ||
|-
| style="background:#eaecf0;" | '''6''' || ||  ||  ||  || ||
|-
| style="background:#eaecf0;" | '''7''' || ||  ||  ||  || ||
|-
|}

==Assignments==

The course consists of two home assignments, each of them performed individually. 
Assignment 1 will be published after Week 3. It is based on Week 1 - Week 3. 
Assignment 2 - after Week 5.  It is based on Week 2 - Week 5. 

Each task is checked for plagiarism. 
Matching more than 25% of the code will be considered plagiarism and will result in 1 point out of 10 with the right to appeal. 
If the code matches more than 40%, the job will be canceled (0 points) without the right to appeal.
After the deadline for each assignment, during the next week, each student will be offered a convenient time for her/him for participating in a conference in Zoom with a lecturer and TA to answer questions on code and explanations of solutions.

'''Assignment title standard:'''
Please, name your files with solutions in this format: Assignment # _ # Number # _ # Group number # _ # Name # _ # Surname #.
Example: Assignment_1_BMOL182_Morty_Smith

Github with assignments: [https://github.com/anamarina/Data_Analysis_in_Journalism_and_Political_Science https://github.com/anamarina/Data_Analysis_in_Journalism_and_Political_Science]

Links for '''submitting''' your assignments (Dropbox links):
{| class="wikitable"
|-
! Group !! Assignment 1 !! Assignment 2 !! Final Project
|-
| style="background:#eaecf0;" | '''BMOL182''' || [https://www.dropbox.com/request/9N8NiR6ADYUWpLo6X0em Link] || [https://www.dropbox.com/request/jCDoL60FT7in07FLzOBs Link] || [https://www.dropbox.com/request/UcXunGYinfQDfOKO1AWi Link]
|-
| style="background:#eaecf0;" | '''BMOL183''' || [https://www.dropbox.com/request/9N8NiR6ADYUWpLo6X0em Link] || [https://www.dropbox.com/request/jCDoL60FT7in07FLzOBs Link] || [https://www.dropbox.com/request/UcXunGYinfQDfOKO1AWi Link]
|}

==Communication==

All course materials, assignments, deadlines will be published on this page. 

Important announcements will be sent in Telegram channel and sometimes duplicated to group emails: [https://t.me/joinchat/AAAAAER0YXnyxzVHeH0Eaw https://t.me/joinchat/AAAAAER0YXnyxzVHeH0Eaw] 

The group with 24/7 online support in Telegram for Q&A, discussions, technical issues, and moral support: https://t.me/joinchat/Eb8LXxFniDA5jaVbLRW5Cg

{| class="wikitable"
|-
! Group !! Teacher !! Teaching Assistant !! Schedule 
|-
| style="background:#eaecf0;" | '''BMOL182''' || Marina Ananyeva [mailto:ananyeva.me@gmail.com Email] [https://t.me/ananyevame Telegram] || Nikita Budilovskiy [mailto:nvbudilovskiy@edu.hse.ru Email] [https://t.me/nvbuddy Telegram] || Saturday 13:00 - 14:20 (September), Saturday 13:00 - 16:00 (October)
|-
| style="background:#eaecf0;" | '''BMOL183''' || Marina Ananyeva  || Nikita Budilovskiy [mailto:nvbudilovskiy@edu.hse.ru Email] [https://t.me/nvbuddy Telegram] || Saturday 14:40 - 16:00 (September), Saturday 16:20 - 19:30 (October)
|}

==Feedback==

We’ll much appreciate it if you help us to make this course better by sharing your ideas and feedback. Feel free to do it!

Anonymous feedback form: [https://docs.google.com/forms/d/e/1FAIpQLSe5e9p5OM-aTuFhw4y-xYEgrvap6VtfzzRUpH7PVBwdzT7Jng/viewform click_here]

==Grading==

'''Final Grade = 0.2*(in-class quizzes + extra points) + 0.2*(home assignment 1) + 0.2* (home assignment 2) + 0.4*(group project)'''

In-class quizzes. At the beginning of each practical class, students are asked to pass a test to check their knowledge based on the additional reading materials and previous seminars. Each quiz task is evaluated out of 1 or 2 points for correct answers. All points for 6 quizzes will be summed up and normalized to 10 points grade. A student can get the extra points in case if he/she showed some good activity during the seminar (e.g., answered the teacher's questions, programmed a small task using a projector for demonstration instead of the teacher, submitted a bonus task). 

Home assignments will be provided during the course with detailed instructions and assignment evaluation criteria. Deadlines cannot be violated. If so, the assignment will not be evaluated by your TA. All assignments are checked for plagiarism. Please, see Assignments above this page.

[https://docs.google.com/spreadsheets/d/1ochOhi7pacEwOuJ6F_MofoTlhosqPoF7YVV0gHDjpOs/edit?usp=sharing Link for your grades]

==Cheating and honor==

You must abide by the [https://docs.google.com/document/d/1y4JHIdxCriFIrSHO1ZmeWi9Tjx01o4umqnoFxQtVSrI/mobilebasic 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, except for the final group project. If you warn us, the worst thing that can happen to you after a good-faith mistake is to ask you to complete another version of the task, without disciplinary action and without notifying the HSE administration.