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The most recent packages are found in these directories:
src- the implementation source code
tests- tests for the implementation source code
These directories contain old implementations that will be replaced eventually, they are just here to avoid confusing people who find this repo through the old blog posts:
data-structures- data structure implementations that have not been updated yet
encodings- encoding implementations that have not been updated yet
algorithms- miscellanous algorithm implementations that have not been updated yet
As I update these, implementations will move from these folders into
You must be using Node.js v8 or later.
First, clone the repo:
Then install the dependencies:
$ npm install
You can then run tests like this:
$ npm test
These are the most recent blog posts covering the most recent version of the code.
At some point I will update these blog posts for the new implementations. For now, they still refer only to the 2009 version of this code.
As this is part of series of tutorials I'm writing, only bug fixes will be accepted. No new functionality will be added to this module.
License: MIT License
For this week’s data science career interview, we got in touch with Dr Suman Sanyal, Associate Professor of Computer Science and Engineering at NIIT University. In this interview, Dr Sanyal shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.
With industry-linkage, technology and research-driven seamless education, NIIT University has been recognised for addressing the growing demand for data science experts worldwide with its industry-ready courses. The university has recently introduced B.Tech in Data Science course, which aims to deploy data sets models to solve real-world problems. The programme provides industry-academic synergy for the students to establish careers in data science, artificial intelligence and machine learning.
“Students with skills that are aligned to new-age technology will be of huge value. The industry today wants young, ambitious students who have the know-how on how to get things done,” Sanyal said.
#careers # #data science aspirant #data science career #data science career intervie #data science education #data science education marke #data science jobs #niit university data science
The following article deals with questions/answers you may encounter when asked about Lexical and Syntax Analysis. Check the bottom of the page for links to the other questions and answers I’ve come up with to make you a great Computer Scientist (when it comes to Programming Languages).
Descriptions are clear and concise.
Syntax analyzers can be generated directly from BNF.
Implementations based on BNF are easy to maintain.
Lexical analysis: deals with small-scale language constructs such as name
Syntax analyzer: deals with large-scale constructs such as expressions
#programming #programming-languages #computer-science-theory #computer-science #computer-science-student #data science
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I loved to work as a scientist. There is a deep feeling of completion and happiness when you manage to answer why. Finding out why such animal would go there, why would they do this at that time of the year, why is that place so diverse… This applies to any kind of field. This is the reason why I want to advocate that if you are a scientist, you might want to have a look at what is called Data Science in the technological field. Be aware, I will not dwell in the details of titles such as Data engineer, data analyst, data scientist, AI researcher. Here, when I refer to Data Science, I mean the science of finding insights from data collected about a subject.
So, back to our **_why. _**In science, in order to answer your why, you will introduce the whole context surrounding it and then formulate an hypothesis. “The timing of the diapause in copepods is regulated through their respiration, ammonia excretion and water column temperature”. Behaviour of subject is the result of internal and external processes.
In marketing, you would have to formulate similar hypothesis in order to start your investigation: “3-days old users un-suscribes due to the lack of direct path towards the check-out”. Behaviour of subject is the result of internal (frustration) and external (not optimized UE/UI) processes.
Although I would have wanted to put that part at the end, as for any scientific paper, it goes without saying that your introduction would present the current ideas, results, and hypotheses of your field of research. So, as a researcher, you need to accumulate knowledge about your subject, and you go looking for scientific articles. The same is true for techs as well. There are plenty of scientific and non-scientific resources out-there that will allow you to better understand, interpret and improve your product. Take this article, for instance, Medium is a wonderful base of knowledge on so many topics! But you could also find passionating articles on PloS One on Users Experience or Marketing Design and etc.
2. Material and Methods
As a Marine biologist and later an Oceanographer, I took great pleasure to go at the field and collect data (platyhelminths, fish counts, zooplankton , etc…). Then we needed to translate the living “data” into numeric data. In the technological industry, it is the same idea. Instead of nets, quadrats, and terrain coverage, you will setup tracking event, collect postbacks from your partners and pull third-parties data. The idea is the same, “how do I get the information that will help me answer my why”. So a field sampling mission and a data collection planning have a lot in common.
#ai #data-science #science #tech #data science #from science