Teoretyczne Podstawy Informatyki

Introduction to Data Science (Wprowadzenie do analityki danych)

Wydział Fizyki, Astronomii i Informatyki Stosowanej,

Uniwersytet Jagielloński w Krakowie

Rok akademicki 2020/2021

Office hours: Tuesday, 13:00 - 14:00; office G-0-10.
COVID-19: Please use MSTeams system or email if applicable.

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Egzam (theory part, written): Monday 1.02.2021, 9.00-11.00,
details to be announced later

Monday, 22.2.2021, 9.00-11.00
List of topics

Lectures & Assignments:
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Week	Lecture slides	Lab slides	Python scripts with assignments	Datasets	Tutorials
13.10.2020	Introduction Data exploration	Introdution_lab	assignment-0-python assignment-0-numpy assignment-0-numpy-matplotlib assignment-0-pandas assignment-1	kc_house_data.csv.zip info on kc_house Optional datasets for exploration analysis: 1) https://www.mldata.io/dataset-details/school_grades/ 2) https://insights.stackoverflow.com/survey 3) https://stat.gov.pl/ 4) https://www.kaggle.com/nasa/asteroid-impacts 5)https://www.kaggle.com/vik2012kvs/walmart-dataretail-analysis 6)https://www.kaggle.com/dronio/SolarEnergy 7)https://archive.ics.uci.edu/ml/datasets/Heart+failure+clinical+records 8)https://www.kaggle.com/cesartrevisan/scikit-learn-and-gridsearchcv 9)https://www.kaggle.com/osmi/mental-health-in-tech-survey	HowToStartAnaconda link to GoogleColab DS_cheatsheet_numpy.pdf DS_cheatsheet_matplotlib.pdf DS_cheatsheet_jupyter_notebook.pdf DS_cheatsheet_pandas.pdf
20.10.2020	Regression-Primer Regression-Advanced-I		assignment-2		S.Rashki python-machine-learning-book rashka_ch04.ipynb rashka_ch10.ipynb
27.10.2020	Regression-Advanced-II		assignment-3		numpy tutorial matrix algebra tutorial
3.11.2020	Regression-Advanced-III
10.11.2020	Classification-Primer Classification-Advanced-I		assignment-4	data:amazon_baby.csv.zip	scikit-learn: LogisticRegression
17.11.2020	Classification-Advanced-II
24.11.2020	Classification-Advanced-III
1.12.2020	Clustering&Retrieval-Primer Clustering&Retrieval-Advanced-I		assignment-5	data:people_wiki.cvs.zip
8.12.2020	Clustering&Retrieval-Advanced-II
15.12.2020	Clustering&Retrieval-Advanced-III		Its time to start developing your personal Data Science project! Select one from the list below or create your own: Proj_NYcrime.txt Proj_PolandClimateChange.txt Proj_TrollTweets.txt Proj_VoteCast.txt Proj_Classification.txt Proj_Clustering.txt Proj_Regression.txt	lending-club-data.csv.zip http://snap.stanford.ed/data/amazon/ http://mlr.cs.umass.edu/ml/datasets.html https//data.world/	more reading: LDA explained
22.12.2020	Clustering&Retrieval-Advanced-IV Recommending-System
12.01.2021	Modeling, simulation, Monte Carlo methods		assignment-6
19.01.2021	Statistical Inference		assignment-7		OpenIntro Statistics Z. Fan at Standford Uni.
26.01.2021	Multivariate Analyses and Artificial Neural Network				DNN and CNN https://www.youtube.com/watch?v=u4alGiomYP4 RNN https://www.youtube.com/watch?v=fTUwdXUFfI8

Lectures are based on the materials from Coursera:

Dr. Mine Çetinkaya-Rundel "Data Analysis and Statistical Inference"
C. Guestrin and E. Fox, "Machine Learning Specialisation"
        Foundation: link
        Regression: link
        Classification: link
        Clustering and Retrieval: link

Related interesting material from Coursera
D. Peng, J. Leek and B. Caffo, " Exploratory Data Analysis"
J. Leskovec, A. Rajaraman and J. Ullman, "Mining Massive Datasets"
B. Caffo, R. D. Peng and J. Leek, "Regression Models"

Data Science applications in physics:
B. Nachman,
"Advanced Machine Learning for Classification, Regression, and Generation in Jet Physics"
M. Stoye,
"ML applications in CMS"
ML techniques in HEP, Workshop, Berkeley Laboratory, 11 - 13 December 2018
https://indico.physics.lbl.gov/indico/event/546/

Collection of datasets
http://mlr.cs.umass.edu/ml/datasets.html
http://faculty.marshall.usc.edu/gareth-james/ISL/data.html
http://snap.stanford.edu/data/amazon/

Useful links:
https://turi.com/download/install-graphlab-create-aws-coursera.html
https://turi.com/download/academic.html
https://github.com/turi-code/SFrame

Clustering
http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html
http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html

Boosting
https://turi.com/learn/userguide/supervised-learning/boosted_trees_classifier.html
https://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf
http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html
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[1] https://class.coursera.org/statistics
[2] http://www.openintro.org/stat/textbook.php
[3] https://class.coursera.org/exdata-006
[4] https://class.coursera.org/mmds
[5] http://www.mmds.org/
[6] http://www.cs.cmu.edu/~awm/tutorials.html

Additional materials
http://www.stat.cmu.edu/~cshalizi/ADAfaEPoV/ADAfaEPoV.pdf
http://statweb.stanford.edu/~tibs/ElemStatLearn/
http://en.wikipedia.org/wiki/Determining_the_number_of_clusters_in_a_data_set
http://www.youtube.com/watch?v=wQhVWUcXM0A

Link to lectures given in 2017
http://th-www.if.uj.edu.pl/~erichter/dydaktyka/Dydaktyka2017/AiSAD-2017/index.html

Link to lectures given in 2014
http://th-www.if.uj.edu.pl/~erichter/dydaktyka/Dydaktyka2014/AiSAD-2014/index.html

Ostatnia modyfikacja: 7 October 2020

Elzbieta Richter-Was

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