Просмотр кода страницы Data analysis (Software Engineering) 2020

'''[Scores coming soon]''' <br />
'''[Slack Invite Link coming soon]  <br />
'''Anonymous feedback form:''' [https://goo.gl/forms/xTfnM328m8ulT4FF2 here]<br />
'''[[ Data_analysis_(Software_Engineering)_2019 | Previous Course Page ]]''' <br />
'''[https://github.com/shestakoff/hse_se_ml Course repo]<br/>


<br />

== Course description ==
In this class we consider the main problems of data mining and machine learning: classification, clustering, regression, dimensionality reduction, ranking, collaborative filtering. We will also study mathematical methods and concepts which data analysis is based on as well as formal assumptions behind them and various aspects of their implementation.

A significant attention is given to practical skills of data analysis that will be developed on seminars by studying the Python programming language and relevant libraries for scientific computing.

The knowledge of linear algebra, real analysis and probability theory is required.

'''The class consists of:'''
# Lectures and seminars
# Practical and theoretical homework assignments
# A machine learning competition (more information will be available later)
# Midterm theoretical colloquium
# Final exam

== Course Schedule (3rd module)==
===Seminars===
'''Dates: Tuesdays (..)'''
* Group BPI-xxx, xx:xx-xx:xx, Room xxx

===Lectures===
'''Dates: Mondays (..)'''
* xx:xx-xx:xx, Room xxx

[Complete Schedule of Software Engineering]

== Lecture materials ==

'''Lecture 1. Introduction to data science and machine learning ''' <br/>
[https://shestakoff.github.io/hse_se_ml/2019/l1-intro/lecture-intro.slides#/ Slides] <br/>

== Seminars ==

'''Seminar 1. Introduction to Data Analysis in Python '''<br/>
[https://github.com/shestakoff/hse_se_ml/tree/master/2019/s1-intro Practice in class] <br/>

== Evaluation criteria ==
The course lasts during the 3rd and 4th modules. Knowledge of students is assessed by evaluation of their home assignments and exams. There are two exams during the course – after the 3rd module and after the 4th module respectively. Each of the exams evaluates theoretical knowledge and understanding of the material studied during the respective module.

Grade takes values 4,5,…10. Grades, corresponding to 1,2,3 are assumed unsatisfactory. Exact grades are calculated using the following rule:

* '''score''' ≥ 35% => 4,
* '''score''' ≥ 45% => 5,
* ...
* '''score''' ≥ 95% => 10,

where '''score''' is calculated  using the following rule:

'''score''' = 0.7 * S<sub>cumulative</sub> + 0.3 * S<sub>exam2</sub> <br \>
'''cumulative score''' = 0.8 * S<sub>homework</sub> + 0.2 * S<sub>exam1</sub> + 0.2 * S<sub>competition</sub>

* S<sub>homework</sub> – proportion of correctly solved homework,
* S<sub>exam1</sub> – proportion of successfully answered theoretical questions during exam after module 3,
* S<sub>exam2</sub> – proportion of successfully answered theoretical questions during exam after module 4,
* S<sub>competition</sub> – score for the competition in machine learning (it's also from 0 to 1).

Participation in machine learning competition is optional and can give students extra points. <br \>
"Automative" passing of the course based on '''cumulative score''' ''may'' be issued.

== Plagiarism ==
In case of discovered plagiarism zero points will be set for the home assignemets - for both works, which were found to be identical. In case of repeated plagiarism by one and the same person a report to the dean will be made.

== Deadlines ==

Assignments sent after late deadlines will not be scored (assigned with zero score) in the absence of legitimate reasons for late submission which do not include high load on other classes. 

== Structure of emails and homework submissions ==
Practical assignments must be implemented in Jupyter Notebook format, theoretical ones in pdf. Practical assignments must use '''Python 3''' (or Python 3 compatible). Use your surname as a filename for assignments (e.g. Ivanov.ipynb). Do not archive your assignments.

Assignments can be performed in either Russian or English.

'''Assignments can be submitted only once!'''

== Useful links ==
=== Machine learning, Stats, Maths ===
* [https://github.com/esokolov/ml-course-hse Machine learning course from Evgeny Sokolov on Github]
* [http://www.machinelearning.ru/wiki/index.php?title=Заглавная_страница machinelearning.ru]
* [https://yandexdataschool.ru/edu-process/courses/machine-learning Video-lectures of K. Vorontsov on machine learning]
* [https://www.dataoptimal.com/wp-content/uploads/Data-Science-Books-for-2018.pdf Some books for ML1]
* [https://anvaka.github.io/greview/hands-on-ml/1/ Some books for ML2]
* [https://mml-book.github.io/ Math for ML]
* One of classic ML books. [https://web.stanford.edu/~hastie/Papers/ESLII.pdf Elements of Statistical Learning (Trevor Hastie, Robert Tibshirani, Jerome Friedman)]
* [http://immersivemath.com/ila/learnmore.html Linear Algebra Immersive book]

=== Python ===
* [http://python.org Official website]
* Libraries: [http://www.numpy.org/ NumPy], [http://pandas.pydata.org/ Pandas], [http://scikit-learn.org/stable/ SciKit-Learn], [http://matplotlib.org/ Matplotlib].
* A little example for the begginers: [http://nbviewer.ipython.org/gist/voron13e02/83a86f2e0fc5e7f8424d краткое руководство с примерами по Python 2]
* Python from scratch: [http://nbviewer.ipython.org/gist/rpmuller/5920182 A Crash Course in Python for Scientists]
* Lectures [https://github.com/jrjohansson/scientific-python-lectures#online-read-only-versions Scientific Python]
* A book: [http://www.cin.ufpe.br/~embat/Python%20for%20Data%20Analysis.pdf Wes McKinney «Python for Data Analysis»]
* [https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks Коллекция интересных IPython ноутбуков]

=== Python installation and configuration ===

[https://www.continuum.io/downloads anaconda]