- #Basic data of science how to#
- #Basic data of science code#
Given that the master's degree is taught entirely in English, applicants must certify that they have at least level B2 English.
Graduates in other engineering subjects or equivalent qualifications from outside the EHEA, who have official authorisation from the Master's Committee (bridging courses will be required). Holders of bachelor's degrees in Computer Engineering, Mathematics, Physics, Statistics or related qualifications who hold official qualifications from outside of the EHEA (bridging courses will be required). Holders of bachelor's degrees in other engineering subjects or equivalent qualifications, with the authorisation of the Master's Committee (bridging courses will be required). Holders of bachelor's degrees in Computer Engineering, Mathematics, Physics, Statistics or related qualifications (no bridging courses are required). Admission shall not, in any case, imply that prior qualifications have been recognized as equivalent to a Spanish master's degree and does not confer recognition for any purposes other than that of admission to the master's degree course.Īpplicants with the following qualifications may be admitted: If it is not recognized, the University of Barcelona shall verify that it corresponds to a level of education that is equivalent to official Spanish degrees and that it authorizes the holder to access university master's degree courses in the country of issue. 
In this case, the qualification should be recognized as equivalent to an official Spanish degree. A qualification from outside the framework of the European Higher Education Area.A degree issued by a higher education institution within the European Higher Education Area framework that authorizes the holder to access university master's degree courses in the country of issue.In accordance with Article 16 of Royal Decree 1393/29 October 2007, students must hold one of the following qualifications to access university master's degree courses: Capacity to use effective development methods for data science projects.
#Basic data of science code#
Knowledge of legislation on data protection and privacy, and on the ethical code in professional practice. Capacity to communicate results using appropriate communication and display techniques. Capacity to verify and quantify the validity of a hypothesis, using data analysis. Capacity to understand, develop and modify analytical and exploratory algorithms for a dataset. Capacity to effectively use analytical and predictive tools for automatic learning. #Basic data of science how to#
Capacity to learn how to propose hypothesis and develop intuition about a dataset using exploratory analysis techniques. Capacity to use technologies for the storage, recovery and processing of large volumes of data. Capacity to clean and correct data, in order to create datasets that are easy to manipulate and informative. Capacity to gather and extract information from structured and unstructured data sources. Capacity to understand the process of analysing data, and the role of data in decision-making. The course will focus specifically on the following competences: In addition, it provides tools to face the challenges in the discipline with analytical, critical and creative capacity. 3.The master's degree covers basic and general competences for managing time, resources and projects, and for working in teams. Size versus Quality: When Does Size Matter?. Hexagonal Binning and Contours (Plotting Numeric versus Numeric Data). 
Example: Variability Estimates of State Population. 
Standard Deviation and Related Estimates.Example: Location Estimates of Population and Murder Rates.Unsupervised learning methods for extracting meaning from unlabeled data.Statistical machine learning methods that “learn” from data.Key classification techniques for predicting which categories a record belongs to.How to use regression to estimate outcomes and detect anomalies.
How the principles of experimental design yield definitive answers to questions. How random sampling can reduce bias and yield a higher quality dataset, even with big data. Why exploratory data analysis is a key preliminary step in data science. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Courses and books on basic statistics rarely cover the topic from a data science perspective. Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training.
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