Hi, i am looking for any papers discussing the impact of Big Data & Machine Learning analytics on research methodology both quants and qual!
Below are some applications of Big Data:
- Big data analytics enable us to find new cures and better understand and predict the spread of diseases.
- Police use big data tools to catch criminals and even predict criminal activity.
- Credit card companies use big data analytics it to detect fraudulent transactions.
- A number of cities are even using big data analytics with the aim of turning themselves into Smart Cities, where a bus would know to wait for a delayed train and where traffic signals predict traffic volumes and operate to minimize jams.
The biggest reason big data is important to everyone is due to its application in almost every field. It is affecting everyone’s life in one way or the other.
Thank you @Venkatesh i am looking for applications in the domain of research methods and academic inquiry.
Something along the line of this paper Ref. Article Big Data, New Epistemologies and Paradigm Shift
The following two sources may serve as a starting point:
Bit by Bit. Social Resarch in the Digital Age by Matthew Salganik, http://www.bitbybitbook.com/
The Data Science Handbook (Wiley), edited by Field Cady, https://www.wiley.com/en-us/The+Data+Science+Handbook-p-9781119092940
However, the focus of current discussions is very much on quantitative methods. There is of course some literature on digital qualitative research (e.g. online ethnography), but as far as I know very little dealing specifically with Big Data and its implications.
Thank you @kenneth for the valuable resources... i am still collecting resources on the matter from both quantitative and qualitative methods.
I doubt that you are going to find anything useful for qualitative research because it, by definition, deals with issues of meaning and interpretation. Big data can tell us what patterns are in the data, but not why or how they occur.
La medicina esta incorporando progresivamente análisis con modelos de bigdata, precisamente porque es imprescindible acumular información y procesarla en diseños operacionales por ejemplo para analizar accesos dinámicos en la oferta publica (listas de espera, dimensionamiento de flujos, operacinalización de información en comportamientos de dispositivos on line , como pueden ser uso de ekg en celulares , llamado medicina remota, etc . Al otro extremo de tipos de requerimientos están la que relaciona genómica y medicina personalizada..
I think we need to consider the fundamentals and the major trends. The time of data-driven science has come and this is going to coexist with the other fundamental approaches. Due to this, secondary data-based research will play a bigger role, and the computational research methods supporting data-to-knowledge conversion will receive more attention, no matter if they are traditional statistical, data science, semiotics, artificial intelligence, etc. methods. Anyway, any discovered pattern, trend, phenomenon, etc. needs interpretation ...
First, things are changing so rapidly, don't expect published articles to be up to date. Look at conference presentations,
Second, for qualitative, you now have the opportunity to look at thousands of posts and tweets for common themes.
Third, for quantitative, you will need new statistical tools (e.g., Bayesian sequential analysis).
Frické, M. (2015), Big data and its epistemology. J Assn Inf Sci Tec, 66: 651–661. doi:10.1002/asi.23212
iNTERESANTE articulo de epistemologia sobre bigdata, sugiero leer a M.Catell que se anticipo hace 20 años a este escenario post social planteando el tiempo de las redes que terminaria por subvertir el capitalismo generando nuevos escenarios transaccionales y de rentabilidades desconocidos en las tangibilidades post industriales
M.Castell, mirado en perspectiva tuvo mayor consistencia que Fukuyama en anticiparse a lo que en eso tiempo muy pocos creian en la sociedad informacional que se avecinaba.
Hoy los sistemas de salud están siendo sometidos a situaciones progresivamente disruptivas que necesitan de respuestas que incorporen en su core modelos innovadores con tecnologias como la bigdata.
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