Análisis correlacional y descriptivo
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Project for David C.
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Project for Top Academic Tutors
“David is a professional person who put the best effort to help me revising my methodolgy chapter and run SEM for my data. He responds on time and never delay a [login to view URL] David for the excellant job!”shgrawe 6 个月前
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“Did a great a job and knows his stuff! Would definitely recommend him.”Miaz24 6 个月前
Presencia TIC en los centros educativos españoles (parte A)
“genial como siempre”sergiyun 10 个月前
SPSS Data extraction and data analysis (1)
“This is my second time working with this freelancer, and I will work with him again for sure.”Ald22 10 个月前
Data AnalystApr 2014 - Apr 2015 (1 year)
I managed and analyzed data and redacted reports on different levels (students, careers, Faculties and University). I also carried out specific analyzes for the elaboration of scientific papers and presentations, that contribute to the knowledge and diffusion of the experience of different programs in the University.
Research AssistantJun 2012 - Dec 2013 (1 year)
I was responsible for the analysis of social representations in a nation-wide research project (FONDECYT No. 1120904, "Media and Power: Discourses of the Press and Adult Subjects of the Region of Araucanía on Justice / Injustice Around the State-Nation and Mapuche People Conflict") .
Research AssociateMar 2010
Assistant researcher in the FONDECYT Project No. 1090179 (2009-2010): "Economic Literacy and Patterns of Consumption and Indebtedness in Pedagogy Students: Toward an Explanatory Model", and in Project FONDECYT No. 110711 (2011-2013): "Design, Validation and Evaluation of A pedagogical model of Economic Literacy in the Initial Formation of Teachers". I also briefly collaborated in the design of a financial education model for the Superintendency of Banks and Financial Institutions (2015).
Psychologist2006 - 2011 (5 years)
Master in Psychology2011 - 2013 (2 years)
PhD in Psychology of Communication and Change2015 - 2016 (1 year)
Aplicaciones de la Teoría de Grafos a la vida real (2016)Universitat Politécnica de Valencia (EdX)
Modeling real-world problems through their representation with graphs and solving them through their associated algorithms.
Learn to Program and Analyze Data with Python (2015)University of Michigan (Coursera)
This Specialization builds on the success of the Python for Everybody course and will introduce fundamental programming concepts including data structures, networked application program interfaces, and databases, using the Python programming language. In the Capstone Project, you’ll use the technologies learned throughout the Specialization to design and create your own applications for data retrieval, processing, and visualization.
Statistical Thinking for Data Science and Analytics (2016)Columbia University (EdX)
This course is part of the Microsoft Professional Program Certificate in Data Science. This statistics and data analysis course will pave the statistical foundation for our discussion on data science. You will learn how data scientists exercise statistical thinking in designing data collection, derive insights from visualizing data, obtain supporting evidence for data-based decisions and construct models for predicting future trends from data.
R Programming (2016)Johns Hopkins University (Coursera)
Basic on how to program in R, how to use R for effective data analysis, how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code.
Social and Economic Networks: Models and Analysis (2016)Stanford University (Coursera)
Learn how to model social and economic networks and their impact on human behavior. The course uses some empirical background on social and economic networks, an overview of concepts used to describe and measure networks, a set of models of how networks form, including random network models and strategic formation models, some hybrids models and how networks impact behavio (contagion, diffusion, learning, peer influences).
Quantitative Methods (2016)Universiteit van Amsterdam (Coursera)
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.
US English Level 195%
Preferred Freelancer Program SLA92%
UK English 185%
Academic Writing 283%
US English Level 283%
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