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What can you do after learning big data related concepts, Hadoop, ML, NLP etc? Where can you implement these?
Not really a software related question - but it's very relevant to current technology and why some software exists. So here is an opinion.
We now live in a world where it is possible to monitor and digitally record information on an epic scale that continues to expand with concepts like The Internet of Things.
With this information it becomes possible to look at the evidence behind decisions that previously would have been made by gut instinct or opinions. What impact does road design have on traffic flow? Which medical drugs get best results in the real world and not just in drugs trials? Is there a correlation between office temperature and productivity? and so on for millions of questions in different domains.
Around the world, organisations are using data they never previously had, to get better at whatever they do (Good or bad).
The big data concepts are the tools for managing all this information. Big Data is not just large in volume, it is often unstructured and in different forms.
So to answer your question. You can implement these concepts by working with organisations that are using Big Data. Hopefully you can see the potential of Big Data along with the mind bending headache it can create when trying to make sense of it.
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I fully understand that this question may be closed as it can be more of an opinion than an actual technical question with an objective answer. However I want to ask it in case someone can help and provide a good response. I think it is important to define what you do in a succint way, so here it goes.
Q: If you are asked, "what's Data Engineering?" what would your definition be? (Not "what does a Data Engineer do?")
This one came to mind, but does someone have a better one? And I am talking in the context of Hadoop/Big Data
A:
Data engineering is the process of taking Big Data that is stored in
either a structured or unstructured format, processing it in batch or
real-time, and generating data in a new format that can be used for
further consumption, visualization, Machine Learning or Data Science
I would like to share what I think is a definition of Data Engineering related to Big Data:
Data Engineering supports and provides expertise to elaborate,
contruct, and maintain a Big Data. Data Engineering uses
tools, techniques, frameworks and skills that are essential to a good
"Data Infrastructure" or "Data Architecture" behind a Big Data.
A good way to define Data Engineering is understanting what a Data Enginer does. Here is a great infographic about: https://www.datacamp.com/community/blog/data-engineering-vs-data-science-infographic
Some responsibilities listed includes:
Develop, construct, teste and maintain architectures;
Ensure architecture will support the requirements of the business;
Develop data set process for data modeling, mining and production;
Recommend ways to improve data reliability, efficiency and quality.
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Recently, I was reading a paper whose name is "On the Effectiveness of Concern Metrics to Detect Code Smells: An Empirical Study".
I come from a non-English speaking country, and I can not quite understand what Concern Metrics means in the field of software engineering.
It is not referring to the relationship between objects?
I have some understanding of java and c #, some people may be able to use java to give me an example.
Thanks.
Like it is said in the paper's abstract: "While traditional metrics quantify properties of software modules, concern metrics quantify concern properties, such as scattering and tangling." Are you familiar to the cross-cutting concern concept? This question provides examples of concerns: Cross cutting concern example Try to read papers on aspect-oriented programming (AOP) to grasp more concepts in order to understand better the relationship between concerns and code. The metrics are attempts to quantify, for instance, the amount of scatterness of a concern (e.g. login) over the source code.
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Recently, I am working on my course project, the topic is the creation of a new hybrid software process model by integrating Scrum and Team Software Process (TSP). Integration of these two models will be based on the SEMAT Essence Kernel Framework.
I am wondering:
Which steps should be followed for this integration (like
determination of the roles and artifacts in these two models)?
What should be the criteria to decide on good sides?
Thanks in advance!
The best way I think I can answer this question is by quoting the agile manifesto.
"Individuals and interactions over processes and tools"
Agile is about people, teamwork and craftsmanship. It's about involving the customer closely to figure out what really is needed - and delivering that, in small increments of working software. Agile is inspect and adapt, based on experimental delivery and the feedback and evidence that comes from that.
Trust yourself. Work closely together and you can do this. The best learning often comes from doing. :)
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Is there a legitimate reason why the majority of society (beyond CS-educated persons) should know about the abstractions we become familiar with in CS/programming (such as data abstractions, computer systems abstractions, etc)? Or does it truly not really matter to anyone but programmers, scientists, and engineers?
There is absolutely no reason why the majority of society should know about CS concepts. This is not the way society works, IMO. To be honest, a much stronger case could be made why the majority of the people should know about first aid then about CS.
There are a lot of professions out there which are crucial to maintain the standard of living we have now. Computer science is one of them, I'll give you that, so are doctors, engineers, mechanics, teachers,...
The majority of the people already know how to use computers because it's useful to them. Just like you know how to live in your house, you probably don't know how to build one from the ground up.
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R can be very useful for prototyping and some exploratory work. However, I'd like to know what kind of on-line or real-time applications have you (the SO reader) successfully deployed using R and why did you choose R for that task.
Questions:
What kind of problem did it solve? (trending, analysis, etc.)
Why did you choose R over other programming languages? (pros/cons: speed, scaling, etc.)
I'm looking for first-hand experiences, so please do not answer unless you have that.
The breadth of applications is beyond the scope of StackOverflow.
Suppose I said that Java is a fine language for mucking around with APIs, but can anyone point to where it's used in the real world for real deployments? The list would be rather large, with, I kid you not, at least dozens of examples.
If you wish to see examples of real world use, stop by the Bay Area R Users Group, which has had presentations # or by people from Google, Facebook, LinkedIn, Twitter, and more. There are smaller offshoots in NYC, LA, Chicago, London, and elsewhere (there are even Aussies and Kiwis who use R).
So, the best solution to see documented usage is to go to Meetup.com, search for [city] R user group, and identify a Meetup, then select "Past" to see past presentations.