Data science is definitely one of the most coveted and dynamic fields not only of this decade but also of this century. Once you get going in this field there is hardly anything to stop you. Even disruption makes a halt before it. All you need to do is get data science training, get a job and be great at it, and then move forward. Would it not be great had things been so simple? But sadly it does not work like that, at least not for all of us.
Creating the foundation
Data science is a multidisciplinary field which means it is nothing like anything but contains bits and pieces of a lot of things. There can be no data science models without the principles of statistical calculations; the utility of computer science is undeniable – there is an indispensable role of programming. You really need to know what you are getting into before you embark on this journey.
The list of unknown areas
So, you studied commerce in college, you have a decent grip over linear algebra and probability, you do not find statistical models completely out of your league. But you have never not for a single day studied computer science. You know your way around an Excel sheet but if asked to write a simple code you would stall never to move.
This is how you judge yourself, without any vanity or pretension. You must be honest with yourself and believe that there is nothing you cannot learn. Do not give up on your dream of becoming a data scientist instead attack your target with acute concentration and severe perseverance. You make an honest list of things you need to learn and start right away.
The trap of designation
The internet seeps with false titles. Companies that need you to fill excel sheets with data might call it a data science post. You need to stay patient till you know what it is that you are doing. Do not go for flashy designations. Find out what the job actually involves.
Less prioritized skills
You want to achieve anything in data science, you need to know your statistics well. You will encounter data from various sources in different forms and formats, ergo you need data mining and data wrangling skills. You might desperately need to learn Python because it is the most used language for data science. But there are certain other skills, often ignored and rarely practised.
- Communication and visualization : If you are going to succeed as a data science professional your management needs to let it happen. The management acts on your findings if you tell the story well. Visualize it with Tableau and they might even give you a hike.
- Domain knowledge : This cannot be highlighted enough. Your work means nothing if it does not solve a problem. You will not be able to see the problem if you do not understand the business. You can blindly run experiments and lose reputation or get the hang of the business and the market and then do data science like a boss.
You assimilate this group of technical skills and soft skills and tell your mother that you are going to be rich very soon.