07 Sep 2022
5 Critical Insights You Gain As A Data Scientist In Initial Years | Ekeeda
Works at Ekeeda
The initial 3-5 years as a data scientist will be a roller coaster ride. New information, techniques, and ways of working will be coming from the left, right, and center. You’re going to have to re-learn some of the things that you may have learned in your data science course online and you will need to figure out a place in the organization. Towards the end of the first five years, you will feel as though you’ve been hit by a truck full of information & learning experiences.
When you look back on these five years, you will notice something cool; you’ve gained some valuable insights that you can use to elevate your career for the next couple of years – you have the skills, courage, and confidence to clear the interview and move to the next level of the game. If you’re passionate and hungry for success you might want to gather as many insights as possible before we hit that 5-year mark. Who doesn’t want to elevate their career now? Are you passionate to know the contribution of data science for business?
Here are five critical insights that you can expect to gain in your initial years as a data scientist:
One of the biggest obstacles for data scientists is that the work is quite repetitive and seems monotonous. Therefore, why not skip to the good part and learn to automate everything you could do? If you love your job 80%, then why are you leaving it for 20% boring stuff? Automate the boring thing and use your time more efficiently to tackle the exciting things. Believe us – 20% of the boring stuff you automate will yield 80% of the results. Why waste time on tasks that will yield little impact? Learn to automate tasks – it could be a simple set up automatic reminders for yourself, uploading data, converting file types, backing up files, and more. Your ability to automate is limited to your creativity. Just make sure you don’t rely too much on it and if it goes too far making you slow and sluggish.
Documentation is at the start of your career as a data scientist. Without good documentation, code could be sent to get integrated, nor can it be reworked by an aspirant couple of years down the line who’s told to rework it. Good documentation will tell you what it does and how it works and provides a look into the original data scientist's thought process about why thought process about the code had to be written as such. Documentation should include flow charts, Readme files with complete descriptions, and provision of contact information if the documentation couldn’t help the intern. Many of the other data scientists you come across are not software engineers or computer science degree holders. Thus, you should work together to produce documentation to help others understand the work.
Good Coding Practice
Five years' time is enough to run good coding in your veins. Most of the data scientists who enter the industry are not engineers or have computer science backgrounds. If anything, data scientists you see have majors in physics statistics and arts to political science. Thus, data scientists are not skilled enough to write code and make mistakes like - the absence of functions, unclear variable names, no hint of unit tests, severe lack of style, etc.
Therefore, the value of data scientists is the inability to write clean code that can be updated, debugged, and moved to production efficiently and in one go. It cannot be stressed how clean code can help a company run efficiently. The ability to assure stakeholders that you can get results will rest on the ability to write clean and efficient code that runs correctly at the first time. If you write clean code, you will favourite and in great demand for the rest of your career.
Think we’re kidding? Speak to any software engineer about it and you’ll come across horror stories and fairytales. Good coding practices are what separate professionals from amateurs. So always try to improve your coding skills.
Wanna crack your FAANG interview with excellent coding skills? Read: A quick guide to crack FAANG Interviews
Be A Great StoryTeller
Well, many data science aspirants can write clean code and conduct data analysis. However, the story doesn’t end here – it begins from here. Confused? We’ll help you with it. Everyone can conclude what data analysis is telling them, and can produce great-looking data visualizations, but can everyone narrate a great story that triggers stakeholders or clients to take action? Can they develop stories that will tell everyone at the table where we are, what we are doing and how we will profess in the future? Data scientists who can tell great stories are indispensable assets to the company. Telling stories will encompass all forms of communication that are vital to data scientists. The ability to communicate is a skill that is not in the pocket of every data scientist. It’s beyond the regular job but it helps to advance rapidly in your career.
You must practice communicating at every chance you get. Develop stories for your stakeholder presentations that have a clear beginning, body, and end. Shape your data analysis to provide a clear story of what that data is currently telling you. Practice brings up stories of what future projections could hold for your company that gives stakeholders a clear idea of what the future could look like.
To be a great communicator isn’t something that you achieve overnight – it needs constant practice and deliberate efforts.
Keep It Simple
Your fancy designs, models, and structure are just zero if they don’t draw any stern conclusions and come up with actionable insights for clients and stakeholders. Your complex algorithm doesn’t prove that you’re better than everyone – it makes you look only a nut. Your two coding lines saving ten lines don’t make you a data science pro if no one can understand it. This adage seems to go out to each one at some point in everyone’s career as a data scientist. Because the work is boring and you’ve learned something new and thus, you don’t want to keep it simple. That’s fine.
As long as you find your way back to keeping it simple is the key to achieving brilliance – it makes you a great mentor, allows you to capture people’s attention, gains efficiency in your work, and leads you to manage teams of your own. Keeping it simple means looking for the short logical path between A&B. It means being direct in your work – that makes more sense written even in 7 to 8 lines than write it. Furthermore, it means you avoid looking at complicated ways of doing things.
The Data Scientist job challenges your intellectual & analytical; puts you at the forefront of new advances in technology. Being high prestige job the package is equally attractive in the industry. Data Scientists have become more and more in demand since big data continues to be increasingly important to the way companies take decisions. These are a few tips to follow before you move on to be a senior data scientist.
There are top data science courses online that will help you learn data science from basic to advanced and make you a pro player in the market. Ekeeda Data Science program is designed by fortune 500+ data science experts. Live classes and 1:1 mentor sessions with industry experts will take your preparation to the next level. Industry-focused curriculum and real-world projects will give you a sneak peek into the industry. Career-building workshops and mock interview sessions will help you gain confidence while speaking at the interviews in top tech companies and start-ups. The 100% placement assistance team will leave no stone unturned to help you find a perfect job opportunity.
The sky is the limit when it comes to making a career in data science. So don’t miss a chance to build the trending career of the 21st century. Sign up for one of the best data science course online in the industry and become industry-ready today.
Data science, ML, and AI have been the hottest career fields in the last couple of years now. From science to data science, it has been a paradigm in the way people look upon the intricacies of business and how to accelerate the company's performance and overall growth. Over hundreds of top Tech companies and start-ups are willing to hire skilled data science aspirants like you. Upskill and uplift your career now.
All the best!
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