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What is Data Science


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Is it AI or is it ML?
Or is Data Science its own thing?

15 replies

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Yay!
Thank you @richk for starting this thread. I am curious for the answer to your query as well. 🙂
Incidentally, I just created another thread being inspired by your query asking the differences between data science and bioinformatics here: https://coursera.community/data-science-8/data-science-versus-bioinformatics-what-are-the-differences-173 . Let's hope we both receive some answers to our queries. 🙂
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There are a lot of buzz words surrounding Data Science so I understand why you bring this questions up.

Data Science is a process of solving a problem:
  1. Define the problem you wish to solve
  2. Gather data
  3. Clean/Tidy/Pre-process Data
  4. Develop a Machine Learning/Deep Learning Model
  5. Evaluate the model
  6. Deploy the model
Machine Learning and Deep Learning models learn without being explicitly programmed. They are trained on data in order to learn a mapping from input data to output data, and use that to make predictions for future data points. Much like a child learns to speak from hearing others speak around him/her.
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Denise wrote:

Thank you @richk for starting this thread. I am curious for the answer to your query as well. :)



@Denise, I guess @richk is familiar with some of the terms, he wants to generate curiosity among members to know AI, ML or Data Science for that matter as it's apparent from the nature of his topic which is discussion type rather question answer type. I think the quest for useful information has never been so high and Data Science is the next big game changer in the technology + research world.
@Mohammad Sameer Hasan I may be naive sometimes to differentiate what you observed but I guess only @richk could clarify if this was a true question or just a post for discussion. In any case, I’m truly interested in the answer. 🙂
Thank you @Liz for your step by step clear explanation. So, machine learning and deep learning use data science for their programming then if I got it correctly? Artificial intelligence is based on machine learning as far as I know? What is the difference between machine learning and deep learning? -if you don’t mind me asking? Thank you.
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Denise wrote:

@Mohammad Sameer Hasan I may be naive sometimes to differentiate what you observed but I guess only @richk could clarify if this was a true question or just a post for discussion. In any case, I’m truly interested in the answer. 🙂



@richk what's your take on Artificial Intelligence, Machine Learning, or Data Science now. If you want to add any value points to our conversation, you may do so. You have every right to know the answer @Denise.
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Denise wrote:

Thank you @Liz for your step by step clear explanation. So, machine learning and deep learning use data science for their programming then if I got it correctly? Artificial intelligence is based on machine learning as far as I know? What is the difference between machine learning and deep learning? -if you don’t mind me asking? Thank you.


@Denise Some good questions here that highlight what I mean about buzz words confusing people!

No, I would use Data Science as the sort of umbrella term that encompasses all the others.

Artificial intelligence systems are computer systems able to perform tasks which normally require human intelligence such as computer vision, NLP (natural language processing) and decision making.

Machine learning is training a computer to learn something without being explicitly programmed and deep learning is simply put machine learning with more layers. What I mean by more layers is more calculations. Deep learning is seen as the future for ML because it allows us to solve more complicated problems by breaking them down into smaller problems. For example, CNNs (convolutional neural networks) are used for computer vision and can detect faces, but first they detect the edges of a face, then features (eyes, nose, mouth), and finally the whole face.

You might find this article helpful.
Thank you so much @Liz . That's very informative and helpful indeed. I like the article too. I will share it with others who need clarifications on these terminologies as well. So, I guess bioinformatics if a branch of data science too, which I was trying to inquire about in another thread here: https://coursera.community/data-science-8/data-science-versus-bioinformatics-what-are-the-differences-173 ? If you don't mind commenting there? Thank you. 🙂
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@Denise Happy to help! I will add a response to your thread as well.
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Liz wrote:

There are a lot of buzz words surrounding Data Science so I understand why you bring this questions up.

Data Science is a process of solving a problem:

  1. Define the problem you wish to solve
  2. Gather data
  3. Clean/Tidy/Pre-process Data
  4. Develop a Machine Learning/Deep Learning Model
  5. Evaluate the model
  6. Deploy the model
Machine Learning and Deep Learning models learn without being explicitly programmed. They are trained on data in order to learn a mapping from input data to output data, and use that to make predictions for future data points. Much like a child learns to speak from hearing others speak around him/her.



there is an interesting website, they provide tools to do experiments using various types of data ----I have not used it myself, but am curious if anyone here has? Also, those doing Data Science classes on Coursera can find this website useful to do real experiments?

Here is the website name and URL...
website is VolunteerScience here, anyone can do a science experiment that they can define and conduct..they provide tools to collect and analyse data --- I have not used or explored this site myself ---- still am curious, if anyone here has any experience with this website tools ....
https://volunteerscience.com/
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What is Data Science


Data science is a multidisciplinary blend of data inference, algorithmm development, and technology in order to solve analytically complex problems.

At the core is data. Troves of raw information, streaming in and stored in enterprise data warehouses. Much to learn by mining it. Advanced capabilities we can build with it. Data science is ultimately about using this data in creative ways to generate business value:

Data science – discovery of data insight


This aspect of data science is all about uncovering findings from data. Diving in at a granular level to mine and understand complex behaviors, trends, and inferences. It's about surfacing hidden insight that can help enable companies to make smarter business decisions. For example:

  • Netflix data mines movie viewing patterns to understand what drives user interest, and uses that to make decisions on which Netflix original series to produce.
  • Target identifies what are major customer segments within it's base and the unique shopping behaviors within those segments, which helps to guide messaging to different market audiences.
  • Proctor & Gamble utilizes time series models to more clearly understand future demand, which help plan for production levels more optimally.
How do data scientists mine out insights? It starts with data exploration. When given a challenging question, data scientists become detectives. They investigate leads and try to understand pattern or characteristics within the data. This requires a big dose of analytical creativity.

Then as needed, data scientists may apply quantitative technique in order to get a level deeper – e.g. inferential models, segmentation analysis, time series forecasting, synthetic control experiments, etc. The intent is to scientifically piece together a forensic view of what the data is really saying.

This data-driven insight is central to providing strategic guidance. In this sense, data scientists act as consultants, guiding business stakeholders on how to act on findings.

Data science – development of data product


A "data product" is a technical asset that:

1) utilizes data as input, and

2) processes that data to return algorithmically-generated results. The classic example of a data product is a recommendation engine, which ingests user data, and makes personalized recommendations based on that data. Here are some examples of data products:

  • Amazon's recommendation engines suggest items for you to buy, determined by their algorithms. Netflix recommends movies to you. Spotify recommends music to you.
  • Gmail's spam filter is data product – an algorithm behind the scenes processes incoming mail and determines if a message is junk or not.
  • Computer vision used for self-driving cars is also data product – machine learning algorithms are able to recognize traffic lights, other cars on the road, pedestrians, etc.
This is different from the "data insights" section above, where the outcome to that is to perhaps provide advice to an executive to make a smarter business decision. In contrast, a data product is technical functionality that encapsulates an algorithm, and is designed to integrate directly into core applications. Respective examples of applications that incorporate data product behind the scenes: Amazon's homepage, Gmail's inbox, and autonomous driving software.

Data scientists play a central role in developing data product. This involves building out algorithms, as well as testing, refinement, and technical deployment into production systems. In this sense, data scientists serve as technical developers, building assets that can be leveraged at wide scale.

Data science is a blend of skills in three major areas:


Mathematics Expertise
At the heart of mining data insight and building data product is the ability to view the data through a quantitative lens. There are textures, dimensions, and correlations in data that can be expressed mathematically. Finding solutions utilizing data becomes a brain teaser of heuristics and quantitative technique. Solutions to many business problems involve building analytic models grounded in the hard math, where being able to understand the underlying mechanics of those models is key to success in building them.

Also, a misconception is that data science all about statistics. While statistics is important, it is not the only type of math utilized. First, there are two branches of statistics – classical statistics and Bayesian statistics. When most people refer to stats they are generally referring to classical stats, but knowledge of both types is helpful. Furthermore, many inferential techniques and machine learning algorithms lean on knowledge of linear algebra. For example, a popular method to discover hidden characteristics in a data set is SVD, which is grounded in matrix math and has much less to do with classical stats. Overall, it is helpful for data scientists to have breadth and depth in their knowledge of mathematics.

Technology and Hacking
First, let's clarify on that we are not talking about hacking as in breaking into computers. We're referring to the tech programmer subculture meaning of hacking – i.e., creativity and ingenuity in using technical skills to build things and find clever solutions to problems.

Why is hacking ability important? Because data scientists utilize technology in order to wrangle enormous data sets and work with complex algorithms, and it requires tools far more sophisticated than Excel. Data scientists need to be able to code — prototype quick solutions, as well as integrate with complex data systems. Core languages associated with data science include SQL, Python, R, and SAS. On the periphery are Java, Scala, Julia, and others. But it is not just knowing language fundamentals.

A hacker is a technical ninja, able to creatively navigate their way through technical challenges in order to make their code work.

Along these lines, a data science hacker is a solid algorithmic thinker, having the ability to break down messy problems and recompose them in ways that are solvable. This is critical because data scientists operate within a lot of algorithmic complexity. They need to have a strong mental comprehension of high-dimensional data and tricky data control flows. Full clarity on how all the pieces come together to form a cohesive solution.

Strong Business Acumen

It is important for a data scientist to be a tactical business consultant. Working so closely with data, data scientists are positioned to learn from data in ways no one else can.

That creates the responsibility to translate observations to shared knowledge, and contribute to strategy on how to solve core business problems. This means a core competency of data science is using data to cogently tell a story. No data-puking – rather, present a cohesive narrative of problem and solution, using data insights as supporting pillars, that lead to guidance.

Having this business acumen is just as important as having acumen for tech and algorithms. There needs to be clear alignment between data science projects and business goals. Ultimately, the value doesn't come from data, math, and tech itself. It comes from leveraging all of the above to build valuable capabilities and have strong business influence.
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Thank you for sharing, @Ts. Evaggelos. This seems like useful information. I found this YouTube video that seems to be related: Data science I Data analytics–discovery of new career/Career in Data Scientist.
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Laura wrote:

Thank you for sharing, @Ts. Evaggelos. This seems like useful information. I found this YouTube video that seems to be related: Data science I Data analytics–discovery of new career/Career in Data Scientist.


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An extremely good video on YouTube @Laura is what explains in detail what Data Science is:

https://www.youtube.com/watch?v=KxryzSO1Fjs

This Data Science tutorial will help you understand what Data Science is, because we need Data Science, a prerequisite for learning Data Science, what Data Scientist does, the life cycle of Data Science with an example and career opportunities in the field Data Science. You will also learn the differences between Data Science and Business intelligence.

The role of a data scientist is one of the most sexy jobs of the century.

Demand for data scientists is high and the number of opportunities for certified data scientists is rising.

Every day, companies are looking for more and more sophisticated data scientists, and studies show that there is a continuing shortage of candidates who qualify for roles.

So let's get deeper into Data Science and understand what Data Science is.

This Science Data Seminar will cover the following topics:

1. Need for ereliability of data? (00:50)
2. What is Data Science? (05:55)
3. Data Science vs. Business Intelligence (11:44)
4. Prerequisites for learning Data Science (16:36)
5. What does the data scientist do? (24:31)
6. Life Cycle of Science of Science Using Use (30:17)
7. Demand for data scientists (47:17)

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