Introduction to Data Science (for physics)

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Outline of the course:

1. Statistics and Data Analysis

2. Multivariate Techniques and Machine Learning

3. Physics Modeling, Simulation and Monte Carlo Methods 4. Regression, Classification, Clustering and Retrieval

First three parts will focus on applications in physics (mostly in High Energy Physics)

The last part will discuss more typical „Data Science”

problems and solutions.

Introduction to Data Science (for physics)

Prof. dr hab. Elżbieta Richter-Wąs

Acknowledgement: slides below „borrowed” fron different courses in HEP and Data Science.

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Part 1: Statistics and Data Analysis

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From N. Berger, CERN Summer School, 2019 3

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Part 2: Multivariate Analysis and Machine Learming

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In HEP everything started multivariate.

Below: inteligent „Multivariate Pattern Recognition”

used to identify particles

Nowdays: let computer help you.

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Classifiers and their properties

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Classical Learning

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Machine Learning

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Part 3: Physics modeling, simulation and Monte Carlo methods

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What is the model?

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Part 3: Physics modeling, simulation and and Monte Carlo methods

13 Visualised model of the detector used for simulation Detector

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Part 4: Regression, Classification, Clustering

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• Current view on Machine Learning :

disruptive inteligent applications are used by leading comercial companies

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Part 4: Regresion, Classification, Clustering

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• Data → inteligence pipeline

New kind of analysis which brings inteligence how to solve a problem

Eg. which product to buy which film to chose

connect people and taxi driver

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Regression

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Case study: prediction for the house price

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Classification

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Case study: Score of the restaurant

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Clustering

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Case study: assigning books to groups by topics

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Recommendation

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Case study: personalisation of recommending items

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Deploying inteligence module

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Case studied are about building, evaluating, deploying inteligence in data analysis.

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Regression: Predicting house prices

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Classification: Sentiment analysis

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Clustering: Finding documents

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Getting your ETCs for lectures

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• I foresee written exam on the theory part.

• List of topical questions will be available before Xmass break.

• You will be asked to answer 5 questions out of 25-30 on the list.