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Saturday, October 29, 2016

Data Science - Machine Learning - NLP - Analytics - Decision Supporting System [Grabbed informations from Internet]


The purpose is the biggest factor that dictates what form data science takes, and this is related to the Type A-Type B classification that has emerged (see here: What is Data Science?). Broadly speaking, the categorisation can be summarized as:

  • Data science for people (Type A), i.e. analytics to support evidence-based decision-making
  • Data science for software (Type B), for example - recommender systems as we see in Netflix and Spotify
  2. Expert in Machine Learning/ Statistics 
  3. COMPUTING [Programming/ Distributed Computing/ Software Engineering]
  4. Data Wrangling (Collecting data from different sources, removing noises, making it as a clean data)
  5. another tools like SQL knowledge to query
  6. Business - How to communicate your project with non-technical guys? UI. How will you reach them?
6. Communication / Business Acumen
This should not be understated. Unless you are going into something very specific, perhaps pure research (although let’s face it, there aren’t many of these positions around in industry), the vast majority of data science positions involve business interaction, often with individuals who are not analytically literate.
Having the ability to conceptualise business problems and the environment in which they occur is critical. And translating statistical insights into recommended actions and implications to a lay audience is absolutely crucial, particularly for Type A data science. I was chatting to Yanir about this, and this is how he put it:
“I find it weird how some technical people don't pay attention to how non-technical people's eyes glaze over when they start using jargon. It's really important to put yourself in the listener's/reader's shoes”
Rock Stars
It probably isn’t clear: I have used this heading ironically. No – data scientists are not rock stars, ninjas, unicorns or any other mythical creature. If you are planning on referring to yourself like this, perhaps take a long look in the mirror. But I digress. The point I want to make here is this: there are some data scientists who possess expert level ability in all of the above, and perhaps more. They are rare and extremely valuable. If you have the natural ability and desire to become one of these, then great – you are going to be hot property. But if not, remember: you can specialise in certain areas of data science, and quite often, good teams are comprised of data scientists with different specialities. Deciding what to focus on goes back to your interests and capability, and this leads us nicely to the next chapter in our journey.
“Regardless of education or experience, there’s something more fundamental, which is your nature of curiosity, determination and tenacity. There are so many times when you hit a problem: perhaps the algorithm isn’t performing in the way it needs to, or perhaps the technology is being a pain. Either way, you can study machine learning algorithms or software engineering best practice, but if you’re not really determined, you're going to give up and not get through it”

To provide some much-needed clarification on these terms, machine learning can be viewed as a multi-disciplinary field that grew out of both artificial intelligence/computer science and statistics. It is often seen as a subfield of AI, and while this is true, it is important to recognise that there is no machine learning without statistics (ML is heavily dependent on statistical algorithms in order to work). For a long time statisticians were unconvinced by machine learning, with collaboration between the two fields being a relatively recent development (see statistical learning theory), and it is interesting to note that high dimensional statistical learning only happened when statisticians embraced ML results

“In physics, you naturally learn a lot of what you need in data science: programming, manipulating data, getting the raw data and transforming it in a useful way. You learn statistics, which is important. And crucially: you learn how to solve problems. These are the basic skills needed for a data scientist”

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