noelia borau

matematiker

About

Mathematician, MSc candidate in Statistics and Data Mining | Looking for Master's thesis

Country

sweden

City

sweden

Industry

information technology and services

Skill

r, project management, statistics, data mining, algorithms, research, management, mathematical modeling, mathematical analysis, applied mathematics, latex, matlab, data science, customer service, sql, python, machine learning, optimization, text mining, maple, sas programming, social network analysis, bayesian statistics, python, computational linguistics

Experience

machine learning researcher

nordic e-infraestructure collaboration

2019-5 - Present · 7 yrs 5 mos

I collaborate with the project called iOBS (Improved Observation Usage in Numerical Weather Prediction) developing Machine Learning QC algorithms.

matematiker

smhi

business consultant | tax accountant | auditor

buil polo s.l

2001-10 - 2008-5 · 6 yrs 8 mos

Business consulting, accounting, taxation, audits.

tourism development agent

comarca hoya de huesca

2011-6 - 2012-5 · 1 yr

Management of Tourist Offices and Interpretation Centres in the Hoya de Huesca region. Development and study of tourist products. Development of contents for the corporate website.

Education

universidad nacional de educación a distancia - u.n.e.d

mathematics

2010-1 - 2014-1 · 4 yrs 1 mo

Main courses:  Mathematical Language, Sets and Numbers.  Discrete Mathematics.  Linear and Integer Programming.  Numerical Equation Solving.  Introduction to Differential Equations.  Fourier Analysis and Partial Differential Equations.  Statistical Inference (Mathematics).  Linear Geometry and Differential Geometry.  Complex Variable.  Introduction to Astronomy.

universidad de zaragoza

mathematical science

1995-1 - 1998-1 · 3 yrs 1 mo

Main courses:  Algebra I and II.  Mathematical Analysis I and II.  General Physics.  Computer Science I and II.  Geometry.  Topology.  Scientific English.

linköping university

mining

2014-1 - 2016-1 · 2 yrs 1 mo

Main courses:  Data Mining and Statistical Learning.  Data Mining – Clustering and Association Analysis.  Bayesian Learning.  Computational Statistics.  Time Series Analysis.  Visualization.  Advanced R programming.  Probability Theory.  Multivariate Statistical Methods.