Online Physics 2022-2023

In this course we will introduce Python and R for the MSc Data Science. This will include common packages used for data science and we will discuss typical programming constructs used in data science.

In this course we will introduce Python and R for the MSc Data Science. This will include common packages used for data science and we will discuss typical programming constructs used in data science.

Data Science is an interdisciplinary field that seeks to identify and understand phenomena captured in structured or unstructured data, extract insights, and add value by generating predictions that aid optimization of processes and equipment. These techniques show considerable promise for bringing about a revolution, increasing the significance and value of owning and collecting data of all types. This course introduces the common techniques and considers the implications for data managers.

Visualising the outcome of a data analysis is critical to communicate the results. In this course we will study standard and cutting edge visualisation techniques to make sense of data, and present it in a compelling, narrative-focused story.

Presenting and visualising data and reporting on the result of an analysis are a crucial skill when making sense of data.

In this module we will discuss advanced and cutting-edge statistical tools and techniques.

Some of the topics covered are likelihood, advanced hypothesis testing, outlier detection, data imputation, bootstrap, nonparametric regression and mixed effect models.

This course teaches programming in high level languages and in particular the Wolfram Language (Mathematica). It will introduce all areas of this powerful language, including symbolic and numerical calculations and simulations, links to other high level languages such as R and Python, links to database languages mySQL and Mongo.

We will show how Wolfram Language allows computation to be applied to many areas of data analysis, and modelling. This allows us to gain deep insight into systems.

The goal of this course is to introduce the student into the field of data science. You will improve your data literacy, understanding the different types of existing data and data structures, and the kind of problems that can be solved using it. You will learn the tools and techniques necessary to obtain the data, store it and manipulate it. You will learn tools and techniques to preprocess it and prepare it for analysis, statistical characterization and visualization. Then, you will be introduced to simple modelling techniques aimed at providing answers for the problems you want to solve. The final lectures are dedicated to introduce the MySQL and Mongo relational and non-relational databases, respectively.

In this course we will discuss modern methods of machine learning, such as decision trees, regression, Markov models, Bayesian approaches, Nearest Neighbours, random forests, support vector machines and neural networks.

Great emphasis will be given to the actual application of all these methods to small and large data sets.

This is an introductory course in statistics and statistical methods for data analysis.

We will introduce descriptive statistics, ANOVA, GLMs, correlations, spectra, wavelets, etc.

This will allow us to perform typical analysis that underlie most modern data science questions.

Nowadays a large volume of data is stored in form of images. This course introduces the tools needed to analyse images and extract information from them, including aspects of image enhancement, filtering, segmentation, morphological analysis and image classification based on convolutional neural networks.

Visualising the outcome of a data analysis is critical to communicate the results. In this course we will study standard and cutting edge visualisation techniques to make sense of data, and present it in a compelling, narrative-focused story.

Presenting and visualising data and reporting on the result of an analysis are a crucial skill when making sense of data.

In this module we will discuss advanced and cutting-edge statistical tools and techniques.

Some of the topics covered are likelihood, advanced hypothesis testing, outlier detection, data imputation, bootstrap, nonparametric regression and mixed effect models.

This course teaches programming in high level languages and in particular the Wolfram Language (Mathematica). It will introduce all areas of this powerful language, including symbolic and numerical calculations and simulations, links to other high level languages such as R and Python, links to database languages mySQL and Mongo.

We will show how Wolfram Language allows computation to be applied to many areas of data analysis, and modelling. This allows us to gain deep insight into systems.

In this course we will discuss modern methods of machine learning, such as decision trees, regression, Markov models, Bayesian approaches, Nearest Neighbours, random forests, support vector machines and neural networks.

Great emphasis will be given to the actual application of all these methods to small and large data sets.

This is an introductory course in statistics and statistical methods for data analysis.

We will introduce descriptive statistics, ANOVA, GLMs, correlations, spectra, wavelets, etc.

This will allow us to perform typical analysis that underlie most modern data science questions.

The goal of this course is to introduce the student into the field of data science. You will improve your data literacy, understanding the different types of existing data and data structures, and the kind of problems that can be solved using it. You will learn the tools and techniques necessary to obtain the data, store it and manipulate it. You will learn tools and techniques to preprocess it and prepare it for analysis, statistical characterization and visualization. Then, you will be introduced to simple modelling techniques aimed at providing answers for the problems you want to solve. The final lectures are dedicated to introduce the MySQL and Mongo relational and non-relational databases, respectively.

Nowadays a large volume of data is stored in form of images. This course introduces the tools needed to analyse images and extract information from them, including aspects of image enhancement, filtering, segmentation, morphological analysis and image classification based on convolutional neural networks.

This is a project course for the MSc in Data Science. Students will be given a data science project, which will be supervised by one member of staff. Students will conduct research on that topic in an independent manner. 

Students will have to deliver a presentation halfway through the project and hand in a report about the results at the end of the project. This will be followed by an oral examination of the submitted report.

This is a project course for the MSc in Data Science. Students will be given a data science project, which will be supervised by one member of staff. Students will conduct research on that topic in an independent manner. 

Students will have to deliver a presentation halfway through the project and hand in a report about the results at the end of the project. This will be followed by an oral examination of the submitted report.