All About the Data

Before understanding and learning Data Science, you highly need to first understand all about the data. Its existence, its use and why we are thinking about it.

In a sense this definition is true if we considered it for general audiences, but as you know that we are adding some science to data. So In terms of Data Science,

This is purely in terms of Data Science taken into consideration.

Basically data is information that has been translated into a form that is efficient for movement or processing. Relative to today’s computers and transmission media, data is information converted into binary digital form.

Now in this curriculum, you will understand the whole concept of data science as easy as reading a storybook.

Coming to the data, it is immensely important to understand about him, his interests, his beliefs, advantages, disadvantages, specialities and many other factors. Now if you are thinking why then just think of any hero who has fought with the villain with bare hand. Technically you can fight with him, but it will be very difficult and sometimes impossible to you to win the fight. Worst it will require more time to get better results.

So to be on the same page you should know something about a Data Scientist.

The Mindset

Data science is all about being inquisitive — asking new questions, making new discoveries, and learning new things. Ask data scientists most obsessed with their work what drives them in their job, and they will not say “money”. The real motivator is being able to use their creativity and ingenuity to solve hard problems and constantly indulge in their curiosity. Deriving complex reads from data is beyond just making an observation, it is about uncovering truth that lies hidden beneath the surface. Problem solving is not a task, but an intellectually-stimulating journey to a solution. Data scientists are passionate about what they do, and reap great satisfaction in taking on challenge.

Training

There is a glaring misconception out there that you need a sciences or math Ph.D to become a legitimate data scientist. That view misses the point that data science is multidisciplinary. Highly-focused study in academia is certainly helpful, but doesn’t guarantee that graduates have the full set of experiences and abilities to succeed. E.g. a Ph. D statistician may still need to pick up a lot of programming skills and gain business experience, to complete the trifecta.

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.

Technology and Hacking

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.

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.

As you see that the DATA is so interesting and fun, lets start learning basics types of data that we will use in Data Science.

This image basically gives you a good understanding, but still in terms of definition,

structured data as data that can be easily organized. As a result these type of data are easily analyzable.

Unstructured data refers to information that either does not have a pre-defined data model and/or is not organized in a predefined manner. Unstructured data are not easy to analyze. A primary goal of a data scientist is to extract structure from unstructured data.

Internal sources of data reflect those data that are under the control of the Business. These data are housed in financial reporting system, operational systems, HR systems and CRM systems, to name a few. Business leaders have a large say in the quality of internal data; they are essentially a byproduct of the processes and systems the leaders use to run the business and generate/store the data.

External sources of data, on the other hand, are any data generated outside the walls of the business. These data sources include social media, online communities, open data sources and more. Due to the nature of source of data, external sources of data are under less control by the business than are internal sources of data. These data are collected by other companies, each using their unique systems and processes.

— and if you want to learn and become Data Scientist, take this curriculum.

And Don’t forget to clap clap clap…

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