Mathematical Biologist to Deliveroo Data Scientist
Why Science to Data Science? Read about Jonny’s journey from mathematical biologist to data scientist, what a typical day looks like, and his experience after a year in the role.
Why Science to Data Science? - a questions a answer a possible solution to find.. A very clearly written
Data Science and Big Data: definition and common myths
This paper defines what big data and data science are and common myths that need to be explained in order to fully understand and use this powerful technology.
data science are and common myths that need to be explained in order to fully understand and use this powerful technology. - A paper worth a share..
Solving relationship issues in data science: Top regression techniques
When we hear about regression in data science, the two techniques that come to mind are linear and logistic regression and many professionals end up learning those techniques well. Having said that, there are many types of regression that can be performed, in this article we will go through some of the key regression techniques.
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Propensity Modelling for Business
Propensity modelling is a statistical approach and a set of techniques which attempts to estimate the likelihood of subjects performing certain types of behaviour (e.g. the purchase of a product) by accounting for independent variables (covariates) and confounding variables that affect such behaviour.
Algorithm for Propensity Scoring - a step by step framework is nicely written and explained
A new Data Science Framework for Analysing and Mining Geospatial Big Data
Geospatial Big Data analytics are changing the way that businesses operate in many industries. Although a good number of research works have reported in the literature on geospatial data analytics and real-time data processing of large spatial data streams, only a few have addressed the full geospatial big data analytics project lifecycle and geospatial data science project lifecycle. Big data analysis differs from traditional data analysis primarily due to the volume, velocity and variety characteristics of the data being processed. One motivation in introducing a new framework is to address these big data analysis challenges. Geospatial data science projects differ from most traditional data analysis projects because they could be complex and in need of advanced technologies in comparison to the traditional data analysis projects. For this reason, it is essential to have a process to govern the project and ensure that the project participants are competent enough to carry on the process. To this end, this paper presents, new geospatial big data mining and machine learning framework for geospatial data acquisition, data fusion, data storing, managing, processing, analysing, visualising and modelling and evaluation. Having a good process for data analysis and clear guidelines for comprehensive analysis is always a plus point for any data science project. It also helps to predict required time and resources early in the process to get a clear idea of the business problem to be solved.
the full geospatial big data analytics project lifecycle and geospatial data science project lifecycle. - Agree nice framework
Big data management: How Organizations Create and Implement Data Strategies
Big data is often approached as a mere technical problem, while many times projects fail because of lack of strategic focus. Implementing the right process may help companies to efficiently run analytics works and for this reason, the paper proposes a few concepts that can support the organizational development of a big data blueprint. Three main ideas will be outlined here: the data lean approach, the maturity map, and different organization models for a data team.
Implementing the right process may help companies to efficiently run analytics works and for this reason, - Agree
Fantastic (data)-Beasts and Where to Find Them: Data Scientists and Data Engineers
This paper discusses the role of the data scientist, what a data scientist is, and the set of skills needed to become one.
Fantastic (data)-Beasts and Where to Find Them: Data Scientists and Data Engineers - Nicely written artcile.
Big Data: The next frontier for advance, competition, and efficiency
Nowadays organisations are starting to realise the importance of using more data in order to support decision for their strategies. The size of data in the world is growing day by day. Data is growing because of vast use of internet, smart phone and social network. Big data is a collection of data sets which is very large in size as well as complex. Generally size of the data is Petabyte and Exabyte. Traditional database systems are not able to capture, store and analyse this large amount of data. As the internet is growing, the amount of big data continues to grow. Big data analytics provide new ways for businesses and government to analyse unstructured data. Nowadays, big data is one of the most talked about topic in the IT industry. It is going to play an important role in the future. Big data changes the way that data is managed and used. Some of the applications are in areas such as healthcare, defence, traffic management, banking, agriculture, retail, education and so on. Organisations are becoming more flexible and more open. New types of data will give new challenges as well.
Generally size of the data is Petabyte and Exabyte. Traditional database systems are not able to capture, store and analyse this large amount of data - Agree
Architecture of Data Lake
Data can be traced from various consumer sources. Managing data is one of the most serious challenges faced by organizations today. Organizations are adopting the data lake models because lakes provide raw data that users can use for data experimentation and advanced analytics. A data lake could be a merging point of new and historic data, thereby drawing correlations across all data using advanced analytics. A data lake can support the self-service data practices.
Managing data is one of the most serious challenges faced by organizations today. - Problem for almost every organisation
SURVIVAL PARAMETRIC MODELS TO ESTIMATE THE FACTORS OF UNDER-FIVE CHILD MORTALITY
Exploring parametric survival models in daily practice of child mortality research is challenging. It may be due to many reasons including popularity of Cox regression and lack of knowledge about how to perform it. This paper provides the application of parametric survival models by using available R software with illustration.
Exploring parametric survival models in daily practice of child mortality research is challenging. - A nicely written paper