PEER REVIEWER TRAINING 

Content: This event will provide detailed training covering all aspects of how to be a peer reviewer. This workshop will cover all the essentials of how to be an effective peer reviewer, including: Overview of different types of peer review; The responsibilities of a peer reviewer; How to critically assess an article and write an effective report; What to do if you have ethical concerns about an article you have been asked to review.

Speaker: Dr Diana Marshall, Head of Reviewer Programmes at Taylor & Francis Group. She was Publisher for the BMC series Journals at BioMed Central and has worked in a variety of editorial roles. Before making the move into publishing, Diana gained a PhD in Plant Biology from the University of Cambridge.

Fecha: martes 28 de septiembre de 2021, de las 9 a las 10:30 horas / Modalidad: en línea / Sin costo.

Regístrate en el siguiente enlace: Peer reviewer training

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«EVALUACIÓN Y DISEÑO DE INSTRUMENTOS» 

Cursos y recursos para la investigación

Objetivo: Elaborar instrumentos de medición psicológica, desde el manejo y la interpretación de las pruebas estadísticas de discriminación, dificultad, confiabilidad y validez.

Temas: Medición en Psicología / Etapas en la elaboración de instrumentos de medición / Etapas en el análisis de instrumentos de medición.

Recursos:

• Programa en Otoño 2021. Para descargarlo: OTOÑO 2021 Programa  
• Echeverría et al. (2013). Diseño de instrumentos de medición en Psicología. Para descargarlo: EVALUACIÓN – VALIDACIÓN
• Meyer et al. (2001). Prueba y evaluación psicológicas. Para descargarlo: EVIDENCIA
• Organización Panamericana de la Salud (2013). Herramientas de evaluación. Para descargarlo: COMPENDIO
• Weiner (2013). Llegó para quedarse. Para descargarlo: EVALUACIÓN PSICOLÓGICA
• Zotero: asistente personal en investigación. Para descargarlo: ZOTERO 5.0
• Instrumento tamiz sobre depresión del Centro de Estudios Epidemiológicos. Para descargarlo: CES-D-R
• Protocolo para calcular el Índice de Discriminación (IDis). Para descargarlo: IDis
• Protocolo para calcular el Índice de Dificultad (IDif). Para descargarlo: IDif
• Dorantes (2010). Base de datos. Para descargarla: BASE DE DATOS
• Protocolo para verificar los supuestos estadísticos. Para descargarlo: SUPUESTOS ESTADÍSTICOS
• Protocolo para análisis factorial y prueba de confiabilidad. Para descargarlo: FACTORIAL Y CONFIABILIDAD

 

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«CUANTIFICAR: NO TENGAS MIEDO» 

Cursos y recursos para la investigación

Objetivo: Conocer y asumir la perspectiva cuantitativa en investigación, para mirar la vida con una lente diferente y ampliar el propio aporte, personal y profesional.

Temas:  Instrumentos para la recolección y bases de datos cuantitativos / Técnicas de análisis de datos cuantitativos con el SPSS (Statistical Package for the Social Sciences) / Formato APA (American Psychological Association) para citas y referencias.

Recursos:

• Un instrumento para la recolección de datos: Escala revisada de depresión del Centro de Estudios Epidemiológicos, CESD-R (Eaton, Muntaner, Smith, Tien, & Ybarra, 2004). Para descargarlo: CESD-R
• Una base de datos: Estudio sobre la temperatura corporal normal en las personas (Allen, 1996). Para descargarla: Allen (1996) 
• Otra base de datos: Bases históricas de datos abiertos sobre los casos de la COVID-19 en México, del 1° de enero al 31 de agosto de 2021 (Secretaría de Salud de México, 2021). Para descargarla: COVID-19 en México (2021)
• Bases de datos (desde Ibero investiga). Para visitar el sitio: Ibero investiga
• El protocolo SPSS de la prueba de normalidad. Para descargarlo: KS-SW
• El protocolo SPSS de las pruebas de homocedasticidad y linealidad. Para descargalo: Levene – Linealidad
• El protocolo SPSS de la prueba de correlación. Para descargarlo: Correlación
• El protocolo SPSS de la prueba de regresión. Para descargarlo: Regresión
• El manual de la Pontificia Universidad Javeriana. Para descargarlo: Manual PUJ

 

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EXPLORATORY DATA ANALYSIS

This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.

Duración: ocho semanas / Modalidad: en línea / Sin costo.

El programa está abierto al registro de nuevos alumnos.

Revísalo y regístrate en el siguiente enlace: Exploratory Data Analysis

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GETTING AND CLEANING DATA

Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data.

Duración: cuatro semanas / Modalidad: en línea / Sin costo.

El programa está abierto al registro de nuevos alumnos.

Revísalo y regístrate en el siguiente enlace: Getting and Cleaning Data

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REGRESSION MODELS

Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing.

Duración: ocho semanas / Modalidad: en línea / Sin costo.

El programa está abierto al registro de nuevos alumnos.

Revísalo y regístrate en el siguiente enlace: Regression Models

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STATISTICAL INFERENCE

Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.

Duración: ocho semanas / Modalidad: en línea / Sin costo.

El programa está abierto al registro de nuevos alumnos.

Revísalo y regístrate en el siguiente enlace: Statistical Inference

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THE DATA SCIENTIST’S TOOLBOX

In this course you will get an introduction to the main tools and ideas in the data scientist’s toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.

Duración: cuatro semanas / Modalidad: en línea / Sin costo.

El programa está abierto al registro de nuevos alumnos.

Revísalo y regístrate en el siguiente enlace: The Data Scientist’s Toolbox

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INTRODUCCIÓN A LA MINERÍA DE DATOS

En este curso, aprenderás de manera gradual y práctica los conceptos básicos de Minería de Datos, junto a los algoritmos más utilizados hoy en día. Al finalizar el curso, serás capaz de entender la importancia de manejar la información y de explorar por ti mismo distintas bases de datos reales. 

Duración: cinco semanas / Modalidad: en línea / Sin costo.

El programa está abierto al registro de nuevos alumnos.

Revísalo y regístrate en el siguiente enlace: Minería de Datos

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GAME THEORY

Popularized by movies such as «A Beautiful Mind,» game theory is the mathematical modeling of strategic interaction among rational (and irrational) agents. Beyond what we call `games’ in common language, such as chess, poker, soccer, etc., it includes the modeling of conflict among nations, political campaigns, competition among firms, and trading behavior in markets such as the NYSE. How could you begin to model keyword auctions, and peer to peer file-sharing networks, without accounting for the incentives of the people using them? The course will provide the basics: representing games and strategies, the extensive form (which computer scientists call game trees), Bayesian games (modeling things like auctions), repeated and stochastic games, and more. We’ll include a variety of examples including classic games and a few applications.

Duración: ocho semanas / Modalidad: en línea / Sin costo.

El programa está abierto al registro de nuevos alumnos.

Revísalo y regístrate en el siguiente enlace: Game Theory

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ESTADÍSTICA Y PROBABILIDAD

En este curso se presentarán las nociones básicas para construir la representación tabular y gráfica de la información estadística, utilizar e interpretar dos pruebas estadísticas (regresión lineal y correlación), y reconocer el papel del azar en la modelación y toma de decisiones.

Duración: cuatro semanas / Modalidad: en línea / Sin costo.

El programa está abierto al registro de nuevos alumnos.

Revísalo y regístrate en el siguiente enlace: Estadística y probabilidad

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QUALITATIVE RESEARCH METHODS

In this course you will be introduced to the basic ideas behind the qualitative research in social science. You will learn about data collection, description, analysis and interpretation in qualitative research. Qualitative research often involves an iterative process. We will focus on the ingredients required for this process: data collection and analysis.

You won’t learn how to use qualitative methods by just watching video’s, so we put much stress on collecting data through observation and interviewing and on analysing and interpreting the collected data in other assignments. Obviously, the most important concepts in qualitative research will be discussed, just as we will discuss quality criteria, good practices, ethics, writing some methods of analysis, and mixing methods. We hope to take away some prejudice, and enthuse many students for qualitative research.

Duración: ocho semanas / Modalidad: en línea / Sin costo.

El programa está abierto al registro de nuevos alumnos.

Revísalo y regístrate en el siguiente enlace: Qualitative Research Methods

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QUANTITATIVE RESEARCH METHODS

Discover the principles of solid scientific methods in the behavioral and social sciences. Join us and learn to separate sloppy science from solid research! This course will cover the fundamental principles of science, some history and philosophy of science, research designs, measurement, sampling and ethics. The course is comparable to a university level introductory course on quantitative research methods in the social sciences, but has a strong focus on research integrity. We will use examples from sociology, political sciences, educational sciences, communication sciences and psychology.

Duración: ocho semanas / Modalidad: en línea / Sin costo.

El programa está abierto al registro de nuevos alumnos.

Revísalo y regístrate en el siguiente enlace: Quantitative Research Methods

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APPLYING DATA ANALYTICS IN FINANCE

This course introduces an overview of financial analytics. You will learn why, when, and how to apply financial analytics in real-world situations. You will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risk of corporate stocks, the analytical techniques can be leverages in other domains. Finally, a short introduction to algorithmic trading concludes the course.

After completing this course, you should be able to understand time series data, create forecasts, and determine the efficacy of the estimates. Also, you will be able to create a portfolio of assets using actual stock price data while optimizing risk and reward. Understanding financial data is an important skill as an analyst, manager, or consultant.

Duración: seis semanas / Modalidad: en línea / Sin costo.

El programa está abierto al registro de nuevos alumnos.

Revísalo y regístrate en el siguiente enlace: Applying Data Analytics in Finance

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UNDERSTANDING RESEARCH METHODS

This course will outline the fundamentals of doing research, aimed primarily, but not exclusively, at the postgraduate level. It places the student experience at the centre of our endeavours by engaging learners in a range of robust and challenging discussions and exercises befitting SOAS, University of London’s status as a research-intensive university and its rich research heritage.

The course will appeal to those of you who require an understanding of research approaches and skills, and importantly an ability to deploy them in your studies or in your professional lives.

Duración: cuatro semanas / Modalidad: en línea / Sin costo.

El programa está abierto al registro de nuevos alumnos.

Revísalo y regístrate en el siguiente enlace: Research Methods

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DATA SCIENCE: LINEAR REGRESSION 

Linear regression is commonly used to quantify the relationship between two or more variables. It is also used to adjust for confounding, where extraneous variables affect the relationship between two or more other variables, leading to spurious associations. Linear regression is a powerful technique for removing confounders, but it is not a magical process. 

This course covers how to implement linear regression and adjust for confounding in practice using R.

Duración: ocho semanas / Modalidad: en línea / Sin costo.

El programa está abierto al registro de nuevos alumnos.

Revísalo y regístrate en el siguiente enlace: Linear Regression

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DATA SCIENCE: R BASICS 

This course will introduce you to the basics of R programming. We’ll cover R’s functions and data types, then tackle how to operate on vectors and when to use advanced functions like sorting. You’ll learn how to apply general programming features like “if-else,” and “for loop” commands, and how to wrangle, analyze and visualize data.

Duración: ocho semanas / Modalidad: en línea / Sin costo.

El programa está abierto al registro de nuevos alumnos.

Revísalo y regístrate en el siguiente enlace: R Basics

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INTRODUCTION TO DIGITAL HUMANITIES 

As museums, libraries, archives and other institutions have digitized collections and artifacts, new tools and standards have been developed that turn those materials into machine-readable data. Optical Character Recognition (OCR) and the Text Encoding Initiative (TEI), for example, have enabled humanities researchers to process vast amounts of textual data. However, these advances are not limited just to text. Sound, images, and video have all been subject to these new forms of research.

This course will show you how to manage the many aspects of digital humanities research and scholarship. Whether you are a student or scholar, librarian or archivist, museum curator or public historian — or just plain curious — this course will help you bring your area of study or interest to new life using digital tools.

Duración: siete semanas / Modalidad: en línea / Sin costo.

El programa está abierto al registro de nuevos alumnos.

Revísalo y regístrate en el siguiente enlace: Digital Humanities

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PRINCIPLES, STATISTICAL AND COMPUTATIONAL TOOLS FOR REPRODUCIBLE DATA SCIENCE 

Learn skills and tools that support data science and reproducible research, to ensure you can trust your own research results, reproduce them yourself, and communicate them to others. This course is for anyone who is doing any intensive data research.

This course will appeal to students and professionals in biostatistics, computational biology, bioinformatics,and data science.

Duración: ocho semanas / Modalidad: en línea / Sin costo.

El programa está abierto al registro de nuevos alumnos.

Revísalo y regístrate en el siguiente enlace: Reproducible data science

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DATA ANALYSIS FOR SOCIAL SCIENTISTS

This statistics and data analysis course will introduce you to the essential notions of probability and statistics. We will cover techniques in modern data analysis: estimation, regression and econometrics, prediction, experimental design, randomized control trials (and A/B testing), machine learning, and data visualization. We will illustrate these concepts with applications drawn from real world examples and frontier research. Finally, we will provide instruction for how to use the statistical package R and opportunities for students to perform self-directed empirical analyses.

Duración: 12 semanas / Modalidad: en línea / Sin costo.

El programa está abierto al registro de nuevos alumnos.

Revísalo y regístrate en el siguiente enlace: Data analysis for social scientists

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FUNDAMENTALS OF STATISTICS

Statistics is the science of turning data into insights and ultimately decisions. Behind recent advances in machine learning, data science and artificial intelligence are fundamental statistical principles. The purpose of this class is to develop and understand these core ideas on firm mathematical grounds starting from the construction of estimators and tests, as well as an analysis of their asymptotic performance.

Develop a deep understanding of the principles that underpin statistical inference: estimation, hypothesis testing and prediction.

Duración: 18 semanas / Modalidad: en línea / Sin costo.

El programa está abierto al registro de nuevos alumnos.

Revísalo y regístrate en el siguiente enlace: Fundamentals of Statistics

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MATHEMATICAL METHODS FOR QUANTITATIVE FINANCE

Modern finance is the science of decision making in an uncertain world, and its language is Mathematics. This course develops the tools needed to describe financial markets, make predictions in the face of uncertainty, and find optimal solutions to business and investment decisions.

This course will help anyone seeking to confidently model risky or uncertain outcomes. Its topics are essential knowledge for applying the theory of modern finance to real-world settings. Quants, traders, risk managers, investment managers, investment advisors, developers, and engineers will all be able to apply these tools and techniques.

Duración: 12 semanas / Modalidad: en línea / Sin costo.

El programa está abierto al registro de nuevos alumnos.

Revísalo y regístrate en el siguiente enlace: Quantitative Finance

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PROBABILITY – THE SCIENCE OF UNCERTAINTY AND DATA

The world is full of uncertainty: accidents, storms, unruly financial markets, noisy communications. The world is also full of data. Probabilistic modeling and the related field of statistical inference are the keys to analyzing data and making scientifically sound predictions.

Build foundational knowledge of data science with this introduction to probabilistic models, including random processes and the basic elements of statistical inference.

Duración: 16 semanas / Modalidad: en línea / Sin costo.

El programa está abierto al registro de nuevos alumnos.

Revísalo y regístrate en el siguiente enlace: Probability

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DATA SCIENCE FOR CONSTRUCTION, ARCHITECTURE AND ENGINEERING 

This course focuses on the development of data science skills for professionals specifically in the built environment sector. It targets architects, engineers, construction and facilities managers with little or no previous programming experience. An introduction to data science skills is given in the context of the building life cycle phases. Participants will use large, open data sets from the design, construction, and operations of buildings to learn and practice data science techniques.

Duración: siete semanas / Modalidad: en línea / Sin costo.

El programa está abierto al registro de nuevos alumnos.

Revísalo y regístrate en el siguiente enlace: Data Science for Construction, Architecture and Engineering

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 TÉCNICAS CUALITATIVAS Y CUANTITATIVAS PARA LA INVESTIGACIÓN

Construye una base inicial en los principios estadísticos, econométricos y metodológicos generales, que permita a los alumnos desarrollar un análisis completo, desde el diseño de la investigación, la preparación de los datos, su segmentación y codificación, hasta la obtención de resultados y respuestas a las preguntas de investigación.

Duración: seis semanas / Modalidad: en línea / Sin costo.

El programa está abierto al registro de nuevos alumnos.

Revísalo y regístrate en el siguiente enlace: Técnicas para la investigación

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RESEARCH METHODS: AN ENGINEERING APPROACH

The objective of the course is to translate current research methods, which are mostly from a social science perspective, into something more relatable and understandable to engineers. Our hope for this course is to go beyond the concepts to understand the actual reasons for doing research in a certain way. While engineers are the main target audience, non-engineers will find this information useful as well.

The methods taught in this course will equip you with the knowledge needed to design, plan and construct your own research process.

Duración: seis semanas / Modalidad: en línea / Sin costo.

El programa está abierto al registro de nuevos alumnos.

Revísalo y regístrate en el siguiente enlace: Research Methods

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