From I.T. to Data Domain: How Does it look like?

I.T. To Data Domain

Having worked for some time in I.T. Field in Java Development & now having some knowledge in Data Domain, let’s explore how the journey would look if you were to make a switch. How would be the life if You decide to change your career & switch from I.T. To Data Domain? What are all the domains one may get to work in? How would be the life? What are the skills that you would easily learn? What would be a little difficult to master?

I am going to keep things simple and give you a clear picture of the domain.

But why should anyone make a switch to such a trending career?

I.T. To Data domain: Confusion?
You do have options to Improve Life

It’s because it’s worth all the efforts and the returns would be outstanding if you are willing to persist and work hard to reach some level. How would you feel to be amongst a few in the world to have an in-depth understanding of Data Domain & finding multiple companies to recruit you in? How would it feel to enjoy monetary benefits at a newer dimension? You would be an exceptional case having command over multiple skills. And if you are bored of traditional jobs, Nothing can challenge you better than the complex data problems that each big company is facing.

Basic Terminologies

Before delving further, I would like to mention a Few terms with their basic definitions so that rest of things make sense to you. Below are a Few of these terms.

  • Big Data
  • Data Mining
  • Machine Learning
  • Data Analyst
  • Data Scientist

Big Data: Whatever online transactions we do, we leave a digital footprint. There is data being generated from our smartphones, bank transactions, every time we book a ticket or we go to a shop. The amount of data being generated in 2 days today is equivalent to all the data we had until 2000. That’s a huge amount of data being generated.The term “Big Data” refers to the collection of all this data and our ability to use it to our advantage across a wide range of areas, including business.

Data Mining: Data mining, also known as Knowledge Discovery in Data (KDD) is about searching large stores of data to uncover patterns and trends that go beyond simple analysis.

Machine learning: Machine learning is an application of artificial intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

Data Analysis: Data Analytics is a bunch of tool and techniques to perform analysis of data (big and small).

Data Scientist:  Data scientists, on the other hand, design and construct new processes for data modelling and production. In addition to performing and interpreting data studies and product experiments, these professionals are tasked with developing prototypes, algorithms, predictive models, and custom analysis.

Also Read: Data Analytics Vs Data Science

Basic Overview

Let’s Suppose that you are working at Amazon, Google, Accenture etc. and wish to make the transition from I.T. To data domain. What would You be doing?

You would have to deal with big data, you would have to write computer programs in SQL, Python, R, C++, Java, Scala, Ruby…and so on, to only maintain big-data databases. You would be called a database manager.

As an engineer working on process control, or someone wanting to streamline operations of the company, you would perform Data Mining and Data Analysis; You may use simple software to do this where you would
only run a lot of codes written by others, or you may be writing your elaborate codes in SQL, Python, R and you would be doing data mining, data cleaning, data analysis, modelling, predictive modelling and so on.

All this will be called Analytics. Many software exists to do this. One popular one is Tableau. Some others are JMP and SAS. A lot of people do everything online where an SAP-based business intelligence setup can be used.
Here, simple reporting can be done easily.

Further, you would then be able to use machine learning to derive conclusions, and come up with predictions, wherever analytical answers are not possible. Think of analytical answers as [If/then] type of computer programs,
where all the input conditions are already known, and only a few parameters change.

Machine learning uses statistical analysis to partition data. An example would be this: Read the comments written by various people on Yelp, and predict from the comments whether the person would have marked a restaurant 4 star or 5 stars.

If that is not enough, you would be able to use deep learning as well. Deep learning is used to process data such as musical files, images, even text data such as natural languages, where data are enormous, but their type is very diverse.

You would use everything to your advantage ~ analytical solutions, partitioning data, hacking mindset, automation by programming, reporting, deriving conclusions, making decisions, taking actions, and telling stories about your data.

I.T. To Data Science: Programming
Programming & math is key to Data Science

Last but not the least, a part of this will be happening on cruise control, where you may not be there physically, but the programs you may have created would do most of the things themselves. Probably if you take it to the level of AI,
one day it may get smarter than you, needless to say, it would already be faster than you. One day it can go to the level that it can surprise you with the solutions that you may not even have imagined.

Now you are a data scientist, and what you would do is called Data-science. Whatever you would do may or may not be seen by people outside your company such as people asking Alexa various questions if you work for Amazon, or people asking questions to “Ok Google” if you work for Google. Or they may not be getting to see anything you do. Your functions would be helping the companies engineer things better.

To do all this, you may need lots of expertise in handling data and knowledge of a few programming languages.

Content Source: Rohit Malshe Quora

Is Your Journey Tough?

Why should I be lying about this? Yes, it’s not easy to master the multiple skills that you require as a Data Scientist. Let’s look at all the skills required for a Data Domain & how difficult it would be to acquire them with the perspective of an I.T. Person.

Venn Diagram: Jobs Skills
Understand Data Science Skills

Programming: If you already have some strong foundation in Programming ( any language like C, C++ or Java ), it won’t be so difficult to learn languages like R, Python & Database Management Softwares like SQL. This is the field where Programmers do have an easy scope to expand.

Statistics: This is where things get a little difficult for I.T. Persons. It’s a must and necessary to start loving numbers, data and statistics. Do you like Maths? Do you like playing with numbers? Is statistics your field of interest? I do understand that a lot of you may not be having a great interest in numbers but it’s just a matter of learning. Spend some time reviving your interest, put in work, have some real time projects to try and you may soon discover your love for maths and numbers.

Business Knowledge: Isn’t it crucial to understand the complete business of the company before you can start playing with Data? It’s very crucial. A Data Domain expert always makes it a rule to have crystal clear Business clarity so that he/she is able to understand her roles. How can you develop an automated machine learning tool without understanding the business Operations? By reading Business & personality development content, by being more open and doubtful, anyone should be able to understand the business working.

Communication: When you are dealing with data and extracting patterns or visuals from it, it becomes mandatory to be able to convey these data points to normal beings. It would be only You who would be able to gather insights and deploy models from Data and so it’s a must to be able to convey them in a sane language. And if you have worked for some time in I.T. Field, the communication won’t be a big issue.

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