Are you looking to transition into Data Science roles?
If you’re seriously considering a switch to Data Science roles, we have an exhaustive list for you to follow. In this article, we have laid down 10 steps to becoming a successful Data Scientist as you move from 2019.
Who is a Successful Data Scientist?
You can’t ignore data and its gravity; similarly, you can’t just look through a skilled and efficient Data Scientist; what he should be and what skills should he be well-versed with.
Data Scientists make numbers speak and hence, derive useful and meaningful insights that can be utilized for the maximum benefit of the organizations and businesses.
There are two types of Data Scientists- one kind claims to be extremely professional and highly skilled but don’t have the proof to back their claims.
The second category of Data Scientists are the ones who not only have a strong portfolio to support their profile but also have worked a lot to carve a niche in their domain.
There might be several questions hovering your mind- does this mean there’s nothing for the freshers? What if I am a non-programmer? How can I build a portfolio? Read what INSAID Chief Data Science Mentor has to say.
You need to master your domain skills and have that extra edge over others to successfully get into Data Science roles for the year 2020.
You are just 10 Steps Away from being a Successful Data Scientist…
1. Figure out the business before you get into it
It’s quite understandable that as a Data Scientist you want to get straight to business. But don’t do that. I mean, before getting into the project, wouldn’t it be a great idea to explore and know everything about the business.
Get all the information like target group, number of customers, practices to retain customers and to attract new ones, how does a product function etc.
These are just examples to show the extent of information you need to have before setting off on your business. These details will help you in the process of generating and analyzing insights, picking the right Data Science tools, visualizing data, applying insights to get the answers to the questions at hand and do more.
If you can answer the 5Ws (Who, Why, What, Where and When) and 1H (How) then you just took the first step.
2. Don’t overlook important details in datasets
You won’t be able to make sense out of large, chunky and disorganized data at once. You’ll need a lot of practice and Data Science tools before you extract valuable insights from a given dataset.
To put it in simple words, Data Science roles need an eye for detail and in-depth work so that your numbers transform into data and speak to you. For instance, why did something work and why did something not; if these are the datasets and variables then why are results not what they should be, etc.
Be patient and don’t let any detail go past you.
3. Don’t digress from the Business Problem at hand
Just because you are a Data Scientist, doesn’t mean you have to innovate just for the sake of it. Never lose sight of the business problem you were initially solving and get too involved in the need to innovate.
When you are in the process of zeroing in on the probable solutions to a problem, do not get biased with the ease, the usual Data Science tools and the time taken to develop.
Confident that you now know just enough? Apply the filters and narrow down your approaches.
4. Learn new Data Science tools and technologies
Due to constant work and developments in Data Science roles and field, there is a sea of latest technologies every year. As a Data Scientist, you need to stay aware of them.
This is imperative, as staying updated will mean you have a fair judgment of how the latest technology will be more useful than its older counterpart.
Data Science tools which reduce your work, help you work towards realizing your strategies, get you the solutions easily and effectively, etc. is the right one for you. Adopt it!
5. Diversify your skills
A rich domain expertise is expected out of a Data Scientist.
However, you can’t stay limited to all you know because that brings stagnation. Venture out and explore more algorithms, concepts and applications of Data Science than what forms a part of your current work schedule. You should diversify and learn as many Data Science tools as possible.
This is also important because one solution isn’t applicable to all problems. Even if you’re good at one aspect, you can’t solely rely on it to solve all problems.
6. Keep it simple
Ideating complex things, using complex calculations or terminologies, etc. won’t always be necessary. Keeping it simple will ensure you implement your projects quickly and successfully.
After you have your data and the variables, analyze the complete thing and draw insights. You will get a report that will have terms too difficult to interpret by a layman.
Tone it down to a language that has easily comprehensive terms; what will go straight to the big brains of your business (your stakeholders) will be the most fruitful of all your efforts.
7. Automate repeated processes
In due course your experience as a Data Scientist, you are very likely to work on monotonous tasks. What will be your rescue path? Automating monotonous and intricate processes.
This will be a win-win situation. Automation will not just make you efficient but also result in a successful process. The costs will reduce too.
When repetitive tasks will be automated, you can also focus on other important aspects of a project; which solution to implement, what innovation to do, etc.
8. Delegate Responsibilities in your team
Data Science roles often require leadership.
A talented team would not be able to work to its true potential. Allocating the responsibilities will increase the team’s efficiency. Do you know one of the best practices of team management? Empowering team members, irrespective of their designation, should be your action point.
Being in Data Scientist roles will call for teamwork and involving great minds to work towards a common goal. So, constantly work on every team member and make the most out of them.
9. Communicate with all Stakeholders
Starting from the first day of your stepping in your role, do not forget to interact with your every stakeholder. There might not be many instances that you get to do so, but find out a chance on your own.
If you are only building the model and not communicating the findings to your stakeholders, you might fail to relay why your approach solves a particular business problem.
Interact on a regular basis and let them know about the technical stuff, in a comprehensive way.
10. Explain non-Data Science functions to your team
When you are a Data Leader and not just a Data Scientist, what will make you stand out is how knowledgeable is your team in non-Data Science business areas. A business functions well and prospers only if the team knows how to correlate non-Data Science spheres with their Data Science business.
Often people working with too many Data Science tools and complex datasets may have difficulty correlating it to non-technical decision making in business.
This will be possible only when they know how this co-ordinated working will help the business earn profit. Explain to them and keep them motivated to work towards contributing to the Data Science field.
Data Science Roles 2020: What else should I know?
This brings us to the end of the listicle. These were the steps that any budding Data Scientist needs to take to make it forward in their career.
Noticed some of the steps like becoming a skilled programmer, gain domain expertise, master Python and Hadoop, etc. were missing? It was a deliberate attempt; you can check out these skills here!
Put your best foot forward and succeed in your desired Data Science roles.
All the best!