Teoretyczne Podstawy Informatyki

Introduction to Data Science (Wprowadzenie do analityki danych)

Wydział Fizyki, Astronomii i Informatyki Stosowanej,

Uniwersytet Jagielloński w Krakowie

Rok akademicki 2026/2027

konsultacje:
wtorek, godz 10.00-11.00, pokoj G-0-10
sroda, godz 9.00-10.00, pokoj G-0-10

Lectures & Assignments:
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Week	Lecture slides	Lab slides	Python scripts with "primer" assignments	Datasets for "primer" and "advanced" assignments.	Tutorials and additional materials
8.10.2026	Introduction Data exploration Questions for exam		Data exploration - primer Assignment_DataExploration	kc_house_data.csv.zip info on kc_house Example datasets for "advanced" assignments: 1) https://insights.stackoverflow.com/survey 2) https://stat.gov.pl/ 3) https://www.kaggle.com/nasa/asteroid-impacts 4)https://www.kaggle.com/dronio/SolarEnergy 5)https://www.kaggle.com/cesartrevisan/scikit-learn-and-gridsearchcv 6)https://www.kaggle.com/osmi/mental-health-in-tech-survey 7)https://zenodo.org/records/4603412 8)https://archive.ics.uci.edu/dataset/492/metro+interstate+traffic+volume lending-club-data.csv.zip http://snap.stanford.ed/data/amazon/ http://mlr.cs.umass.edu/ml/datasets.html https//data.world/	HowToStartAnaconda Link to GoogleColab DS_cheatsheet_numpy.pdf DS_cheatsheet_matplotlib.pdf DS_cheatsheet_jupyter_notebook.pdf DS_cheatsheet_pandas.pdf Exploratory Data Analysis Fundamentals of Analysis Box plot Introduction to Modern Statistics https://openintro-ims.netlify.app/
15.10.2026 22.10.2026 29.10.2026	Regression-Primer Regression-Advanced Regression- StatInference Questions for exam		Regression - primer Assignment_Regression	kc_house_data.csv.zip info on kc_house	S.Rashki python-machine-learning-book rashka_ch04.ipynb rashka_ch10.ipynb numpy tutorial.ipynb matrix algebra tutorial ElasticNet Regression
5.11.2026 12.11.2026 19.11.2026	Classification-Primer Classification-Advanced Questions for exam		Classification - primer Assignment_Classification	data:amazon_baby.csv.zip	scikit-learn: LogisticRegression
26.11.2026 3.12.2026 10.12.2026	Clustering&Retrieval-Primer Clustering&Retrieval-Advanced Questions for exam		Clustering - primer Assignment_Clustering	data:people_wiki.cvs.zip	more reading: LDA explained LDA paper
17.12.2026	Recommender-System Questions for exam		Example https://www.geeksforgeeks.org/recommendation-system-in-python/		Recovering Eigenfaces Matrix Factorisation https://developers.google.com/machine-learning/recommendation/ https://alex.smola.org/teaching/berkeley2012/recommender.html https://www.cs.uic.edu/~liub/teach/cs583-fall-24/ https://www.cs.cmu.edu/~mgormley/courses/10601-s17/slides/lecture25-mf.pdf https://www.ugr.es/~essir2013/slides/ESSIR_2013_Recsys_final.pdf
7.01.2027 14.01.2027	Statistical Inference Questions for exam		Stat. inference - primer Assignment_StatInference		https://openintro-ims.netlify.app/
21.01.2027	Deep Learning Multivariate Analyses and Artificial Neural Network Modeling, simulation, Monte Carlo methods				VisualizingAndConvolutionalNN Understanding Deep Learning Machine Learning in Physics Computational Thinking and Data Science Introduction to Computational Modeling (lectures by Ilya Nemenman)

Recomended books (.pdf available for download) :
https://hastie.su.domains/ElemStatLearn/
https://hastie.su.domains/ISLP/ISLP_website.pdf
https://probml.github.io/pml-book/
https://www.deeplearningbook.org/

Lectures are based on the materials from Coursera:

C. Guestrin and E. Fox, "Machine Learning Specialisation"

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
-----------------
[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

Ostatnia modyfikacja: 17 September 2026

Elzbieta Richter-Was

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