Data Science

What is Data Science?

   Everyone you ask will give you a slightly different description of what Data Science is, but i will try my best to give definition that can be understand by anyone learning data science. Data Science is basically study of data. Study of data involves developing the method to record, store and analyze the data to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data i.e. both structured and unstructured data

Now question is what is structured and unstructured data.

a) Strucutred Data- It is the data which is present in some structured format which can be easily accessed and used by the reader or used by the computer program.

   Such data can include address, names , dates, credit card numbers, stock information, geolocation and more all these data will be in any kind model or format. Thus these data are highly organized and easily understood by machine language and can be processed relatively quickly to that of unstructed data.

b) Unstructured Data- It is the data which is not in any pre-defined model or is not organized in a pre-defined manner. They needed to be sorted first systematically and then can be processed by the user or computer programs.

   Such data is typically text-heavy, but may contain data such as dates, number, and facts as well. It includes e-mail messages, word processing documents, videos, photos, audio files, presentations, webpages and many other kinds of business documents.

   Most people agree that Data Science has a significant data analysis component which means it is not new. What is new is the vast quantity of data available from massively varied sources: from log files, email, social media, sales data,patient information files, sports performance data, sensor data, security cameras, and many more besides. As we have more data than ever computing power is needed to make a useful analysis and to extract a useful new information out off it which will help the user to increase the knowledge in that particular domain or area. New knowledge will help the organisation to learn and understand their environments, analyze existing issues, and reveal previously hidden opportunities.

   Data scientists use data analysis to add to the knowledge of the organisation by investigating data, exploring the best way to use it to provide value to the business. Many organisations use data science to focus on a specific problem, and so it's essential to clarify the question that the organisation wants answered for this specific problem which will help data scientist to focus on the that problem more precisely although they will extract all the possible valuable information out of the data but presenting it in-front of the concern persons will be much easier when the requirement of the organisation is clear. This is the small review of how Data Scientist works. Good data scientists are curious people who ask questions to clarify the business need. They use multiple models to explore the data reveals patterns and outliers; sometimes, this will confirm what the organisation suspects, but sometimes it will be completely new knowledge, leading the organisation to a new approach. When the data has revealed its insights, the role of the data scientist becomes that of a storyteller, communicating the results to the project stakeholders. Data scientists can use powerful data visualization tools to help stakeholders understand the nature of the results, and the recommended action to take. Data Science had completely changed the way we work, we use data, we understand the data, tha's why it is called as new oil to the industry.

   Many time I heard a question around what are the key requisites to be a data scientist on the typical technical basic I would answer them that you should learn skills like:-

1. Learn basics of Python.
2. Learn basics of Statistics and Mathematics.
3. Learn Python for Data analysis.
4. Learn Machine Learning.
5. Learn basics of SQL.
6. Most Important PRACTICE, PRACTICE and PRACTICE.

   Apart from all the technical skills what i learned that Data Science should have in them is curiosity to know more and more, extremely argumentative and Judgemental. Curiosity is absolute must. If you're not curious, you would not know what to do with the data. Judgemental because you need to have some sort of existing knowledge about something, to know where to begin with. Argumentative because you have an opinion and you can argue a case with the data presented as evidence. Even if you need to modify your assumptions and hypotheses, you will learn from your data and become a better story teller.

   The other thing that the data scientist would need is some comfort and flexibility with analytics platforms: some software, some computing platform, but that's secondary. The most important thing is curiosity and the ability to take positions. Once you have done that, once you've analyzed, then you've got some answers. And that's the last thing that a data scientist need, and that is the ability to tell a story. That once you have your analytics, once you have your tabulations, now you should be able to tell a great story from it. Because if you don't tell a great story from it, your findings will remain hidden, remain buried, nobody would know. But your rise to prominence is pretty much relying on your ability to tell great stories.

   For a Data Scientist starting point would be to see what is your competitive advantage. Do you want to be a data scientist in any field or a specific field? Because, let's say you want to be a data scientist and work for an IT firm or a web-based or Internet based firm, then you need a different set of skills. And if you want to be a data scientist in the health industry, then you need different sets of skills. So figure out first what you're interested, and what is your competitive advantage. Your competitive advantage is not necessarily going to be your analytical skills. Your competitive advantage is your understanding of some aspect of life where you exceed beyond others in understanding that. Maybe it's film, maybe it's retail, maybe it's health, maybe it's computers. Once you've figured out where your expertise lies, then you start acquiring analytical skills. What platforms to learn and those platforms, those tools would be specific to the industry that you're interested in. And then once you have got some proficiency in the tools, the next thing would be to apply your skills to real problems, and then tell the rest of the world what you can do with it.