Top Data Science Skills that you won’t learn in Grad School! (and how to learn them)
Become a Data Scientist in 30 days! Follow the step by step guide and master the art. Wait! Don’t you think that if it was that easy and quick, the whole world would be a Data Scientist? Don’t fall into such traps. To become a good Data Scientist a lot of work, energy and time are needed. It is not a Cake Walk. But, if you are okay with learning data science the right way, this learning period of a few months will be one of your lifetime investments.
Data Science is a hot topic in the last few years. Even Harvard Business School has stated it to be The Sexiest Job of the 21st Century. Needless to say more. :p
Top Data Science Skills
Below are some of the data science skills that every data scientist must know
1. Change is the only constant. Cliched but the truth –
It’s not about “Learning Data Science”, it’s about “improving your Data Science skills!
The subjects you are learning currently in Grad School are important because no learning go waste but, the real world practicality is totally different from the theory of the books which is taught for decades. Don’t cramp the information, rather understand the big picture.
A report states that 50% of things that you learn today regarding IT will be outdated in 4 years. Technology can become obsolete but, learning can’t be. You should have the attitude of learning, updating your knowledge and focusing on your skills(Get your Basics clear) and not on the information you learn!
This will help you to survive in this tough and competitive world (I am not scaring you, I am just asking you to prepare your best! You should start focusing on the below skills for becoming a data scientist –
- Business Skills
- Practical Skills like Math and statistical skills
- Coding Skills(As I said, Learn and don’t Cramp)
- Soft Skills like People skills, Social skills, Data Visualisation, Presentation and Communication (Emotional intelligence quotients- I feel this is the most imp. one)
The best guide on Math and Statistics for Data Science
2. Essential Data Science Ingredients – Tools for Data Science
Companies employ Data Scientists to help them gain insights about the market and to better their products. There are several tools needed for Data Science. Skills like Big Data Technologies, UNIX, Machine Learning, Python, R, SQL, etc. are needed to master the art. You can start with the last three skills (PYTHON, R, SQL) from now. It will reap you benefits in the future. These three are the most used skills in the current scenario.
3. Become the Jack of all and Master of none for the BELOW points only
a. Develop the mind, discover new things and enhance your imagination- In short, Start Reading.
The above benefits are good enough to start reading NOW! Also, make sure that you start reading outside your discipline. It will help you to get exposure to a wide range of different techniques and problems and to get comfortable jumping feet-first into a new topic.
b. Try to analyze new types of Data
Data can be in any form. You can easily interpret data if it is in the form of text and images. But, try to interpret the Data in a Video or in an Audio. It can be in a Pre-trained model, in a Relational database, and in a Time series form. The last three might be tough for you as of now, but you can start with Video and Audio form.
c. Get Inspired with New Ideas and Be Impacted: Talk to new people
Create a network with people you can learn from. If someone further along the road can mentor you in the right manner, it can add direction to your career. Talk to people with a technical background who are outside your field. You will get new information, ideas, and loop-holes where you can create value.
Also, talking to people with a non-technical background will help you to enhance your soft-skills. You will get a chance to explain to them the technicals of your specific academic background.
Wait! Have you checked the best way to get your first job n data science?
d. You can now cry over spilt milk with Version Control
Imagine! You have got control over your actions and just by a click, the actions are revocable and controllable. Wouldn’t life be perfect then?
Don’t know about real life but, you can control that in the reel life. If a mistake is made you can turn back the clock and compare earlier versions of the code to help fix the mistake while minimizing disruption to all team members.
Here you can figure out your mistakes and what you broke. It’s very good for individual projects and can help you to master the art if practiced regularly.
4. Never settle
Here I am not talking about the beautifully designed phone with the premium build quality & the best technology to users around the world. I am asking you to not Stop at “Good Enough”.
This means that if a model is not accurate and needs additional tuning then it should not be left at the good enough stage. This will be a major factor that will differentiate you from the other Data Scientist. Bring perfection in the task and make sure you answer every single question you can with the data. The best thing is to try to add value. If someone else finds it valuable I bet it will be the most valuable work for you.
If you have the urge to learn, if you are ready to work on yourself and are ready to push your limits you will definitely. If you have the perseverance and discipline then I can guarantee you that you can master the art of Data Science no matter what your situation is.
Also, remember it’s never too early or late because being curious, self-driven, and passionate about finding answers is what keeps one alive. Focus on the things that are important for your next step!
Comment your experience of exploring these data science skills. I have something interesting for you – Top Programming Languages for Data Science
See you around!