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👨🏻‍💻 Data Scientist 📚 MSc. Mathematics & Computer Science 🎓 BSc. (Hons) Physics
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Chronic Obstructive Pulmonary Disorder is currently one major causes of death worldwide. It also poses a global public health challenge due to its high prevalence — bringing mortality, disability, and socioeconomic burden to high income and low income countries alike. As we abolish infectious diseases, the rates of chronic diseases such as cardiovascular diseases have also increased, particularly in regions with high levels of urbanization and industrialization (such as China, India, Mexico, Brazil, etc.). Early diagnosis and prevention of non-communicable diseases is thus important. …

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EntiTree is an effort that started in mid-2020 and is a merger of other wikidata visualisation tools about trees, with some extra features that make it more usable and navigable.

The mission of EntiTree is to support the following people:

  • researchers of any level that want to explore wikidata connections in a visual way 🧪
  • scientists that are keen to use an interactive taxonomy tree 🔬
  • historians investigating royal families 👑
  • students of any kind of discipline, that want to enrich they knowledge 🎓
  • curious random and non English-speaking people from around the globe, thanks to the multilingual feature 🌎🌍🌏

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Nowadays, data science is not restricted only to the tech giants, but is already used by organizations across many industries, from FMCG to the financial services. Although the exact data science process varies depending on the specific use case, there is a common framework that is used to deliver data science solutions: the cross-industry standard for data mining (CRISP-DM). The CRISP-DM framework is widely regarded as the industry standard for developing predictive analytics solutions.

CRISP-DM consists of six major phases:

  1. Developing business understanding
  2. Gaining data understanding
  3. Conducting data preparation
  4. Doing predictive modeling
  5. Evaluating the results against your business problem/question of…

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In a famous study, it was shown that beliefs (i.e. expectations) can actually bring about the expected behaviour, creating a reality. A famous example: people who expect to die young may smoke and take drugs, ending up dying young in reality. This phenomenon also holds true in social interactions, in what psychologists call behavioural confirmation.

Our perception of reality (social or otherwise) is influenced by what we happen to be paying attention to, our emotions, prejudices, and so on. According to social psychology, we only see what we are expecting and usually do not notice what we are not expecting…

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On its own, agile is more of a mindset, and there are several established frameworks that one can adopt to achieve agility.

The two most popular methods to achieve agile principles are Kanban and Scrum. Kanban (Japanese: 看板) is used to to manage a continous queue of work items, which was originally developed by Taiichi Ohno at Toyota for lean manufacturing. Adopted from the Toyota production system, core ideas of the Kanban method include:

  1. Limit amount of work in progress so that there are only pending issues that the team can sustainably handle.
  2. Identify and remove bottlenecks in your workflow…

Agile is a continuous learning approach that concerns two aspects: project management and product development. In particular, it is an approach to managing projects that avoids expensive upfront planning while reducing complexity and minimizing risk. Agile methods are traditionally applied in software development and IT support (e.g. service ticketing).

Some characteristics of agile projects are:

  1. Incremental — products are released in small, valuable increments instead of shipped all at once.
  2. Iterative — products (and also the product development process itself) are continuously improved through user as well as internal feedbacks from the incremental releases, so that mistakes are quickly fixed…

In summary:

  1. Clarify the problem
  2. Generate inputs and/or outputs
  3. Create test cases
  4. Plan your program
  5. Code
  6. Analyse the performance: speed/runtime
  7. Debug
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Step 1: Clarify the problem to be solved. This is important to make sure that you do not spend wasted effort on solving something that the interviewer is not even interested in due to your misunderstanding. It is often helpful to even just repeat your understanding of the problem to make sure that you and the interviewers are on the same page.

Step 2 and 3: Brainstorm some expected inputs and outputs that your solution would face. These also…

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This post is targeted mainly at computer scientists, statisticians, engineers, physicists, data scientists, mathematicians, and any other professionals in a (esp. scientific) discipline who are interested in consuming the state-of-the-art literature in biology and medicine. Since lots of texts in biomedicine often use heavy jargons, non-biologists usually find it quite intimidating to read publications in this and related fields, let alone contribute their unique expertise to biology. Yet, recent technological advances in experimental techniques have led to a dramatic explosion of data, which in turn requires interdisciplinary collaboration between people of different know-how so as to make sense of these…

Kevin Siswandi

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