Data Science journey gives you a some amazing questions on data science which will increase knowledge on data science.
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Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining.
Data science is a "concept to unify statistics, data analysis, machine learning and their related methods" in order to "understand and analyze actual phenomena" with data. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, information science, and computer science.
Data science is the field of study that combines domain expertise, programming skills, and knowledge of math and statistics to extract meaningful insights from data. Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems that perform tasks which ordinarily require human intelligence. In turn, these systems generate insights that analysts and business users translate into tangible business value.
First, data science is not a software engineering piece of work. That is, data science is not about building products or product features or systems or any related fancy things.
Second, data science is not a visualization piece of work. Creating the cool visual is neither the end goal nor the beginning part of how a data scientist works. Needless to say, data science is not about creating visually impactful infographics.
Third, data science is not a scientific piece of work. In particular, data scientists don't work in the academia. It is the industry's particular requirements and the business markets' call that makes the job of data scientist needed. Data scientists usually don't publish papers, and neither is the paper or book publishing business part of any data scientists' daily concerns.
Last but not least, I don't agree with the public view that data science is, at least mostly, statistics. Just to cite a quick story of myself. Once I was asked to hire someone to assist my work and ended up interviewing lots of applicants through phone. Many of the applicants came from the filed of statistical analysis and most of these applicants tended to sound really confident that he or she would be more than qualified for the role. However, I didn't end up calling any of them on-site. One thing I realized at that time was that statistical knowledge alone doesn't make a person qualified for assisting me effectively on the kind of data science work that I needed to do, for reasons I'll mention in a short while.
Now, we are ready to talk about what data science is. It's a thing that encapsulates some programming skills, some statistical readiness, some visualization techniques, and, last but not least, a lot of business senses. The kind of business sense that I in particular care about is the ability and willingness, sometimes eagerness, to translate any business questions into questions answerable using currently or forthcomingly available data within one's reach.
In fact, it takes a special way of connecting all the dots in the random world full of data most of which you may not find immediately useful to make a working data scientist.
A data scientist, based on my current understanding, is the person who connects the dots between the business world and the data world. Similarly, data science is the craft that a data scientist utilizes to make this happen.
The process of data science consists of data munging, data mining, and delivering actionable insights. Based on my own experience, a common toolset to get all or part of these done include Python, R, Tableau, SQL, etc.