16 Essential Tips for Starting a Career in Data Science

Let’s start a new journey in a data science career. Essential tips to learn to create your roadmap to becoming a successful data scientist

Data science is not easy! And learning it without proper guidance can be intimidating. If you are searching for a proper resource to start a career in data science, then this article is for you.

There are tonnes of things in data science like programming languages, modeling techniques, statistics, various soft skills, etc., that you need to focus on.

Indeed, you would get confused about where to start, what to learn first, or where to give most of your time.

To help you get started, I am making this guide which consists of top-notch tips to learn data science from scratch.

This guide will help you to start your career in big data and make you aware of all the know-how as well as how to land a job as a fresher.

What is Data Science?

Data science is an amalgamation of various concepts like maths, statistics, and machine learning. It is used to identify hidden insights from multiple data sources.

This information is further used to make different business decisions. Not only that, but data science also helps in predicting various future events related to business like sales and production.

So, in a nutshell, data science is used to draw insights from data and make decisions and predictions. It is done by using different techniques like predictive causal analytics, prescriptive analytics, and machine learning.

For example, suppose you work in a telephone company, and your job is to identify the best locations for towers in new regions.

First, you will collect the data relating to the work, i.e., collecting frequencies from different locations.

Then, you will use data science to find the best tower locations, which will ensure that the users receive a perfect network.

Data Scientist Role and Responsibilities

A data scientist’s work is very diversified. Their very first responsibility is to understand the organization’s goals and ascertain how data can be used to achieve those goals.

Data scientists collect data, design processes, create algorithms and different predictive models, analyze the data, and share insights with the management.

Although each project is different, the usual roles and responsibilities held on a data scientist are as follows:

1. Identifying the objective of the research to begin the discovery process.
2. Collecting data from multiple sources.
3. Processing and cleaning of data.
4. Storing the collected data in databases for further use.
5. Investigation and exploratory analysis of data.
6. Choosing potential models and algorithms.
7. Applying different data science techniques to derive insights from the data.
8. Measuring the results with pre-setted standards and improving results if not up to the mark.
9. Data visualization and presentation of final results to management.
10. Making adjustments as per the feedback of management.

Important Tips to Start a Successful Career in Data Science

So far we have learned about the basics of data science and the roles and responsibilities of a data scientist.

There is a huge growing demand in this field but most of the roles are still undiscovered.

That is why I thought of writing this article to list the important elements to decide before choosing this role.

I am sure that this article would be an eye-opener for students who are planning to start their careers.

Data Science is not rocket science and with proper guidance and a roadmap, you can easily reach success.

1. Choose the Right Role

This is where you get started. Your very first step should be deciding the role that you want to take up.

There are various roles in the field of big data like data engineer, machine learning expert, data analysts, etc. that you can choose from.

Choosing a role or profile totally depends on what you like or what subject background you’ve studied.

For example, if you have experience in coding (Python or R), it would be much easier for you to become a data scientist rather than being a machine learning engineer.

Or, if you are good with numbers, it would not be difficult for you to go for data analytics. So, first of all, make yourself clear about what you want to become, what subject you like, and then choose a role.

Otherwise, you will stay confused until the very end. Here are some tips to help you decide your path in big data:

• Try to connect with the working professionals in the industry, ask them relevant questions to figure out what each of the roles entails.
• Do SWOT analysis and figure out your strengths and weaknesses and choose the role according to that.
• Try choosing a role that suits your field of study.

Remember to take plenty of time in this step to clearly understand each role and its requirement and hence prepare yourself for it.

2. Join an Institute

Data science is a highly technical subject. Having proper guidance and curriculum-based learning is very critical.

There are many ways to learn data science. The simplest way is to waste lakhs and lakhs of rupees only to learn from decade-old pre-recorded videos and get frustrated with it.

Another way is to find an institute that gives live classes with an updated curriculum. There are thousands of institutes and courses available out there, but you will have to do your own research to choose the best one.

Choose a reputed institute that has a good track record. Try contacting the alumni to have a clear-cut idea about the authenticity of the institute, and then just hop on to it.

After taking up a course, put your heart and soul into it. Follow the coursework, projects, and assignments, and all the discussions happening around the community.

Assignments are, to be honest, the most crucial part of a course because it is where you apply all your learnings.

3. Select Proper Tool/Language

A data scientist makes use of various tools, techniques, and languages to get their work done.

But being a beginner, it might be a little intimidating for you to choose the tool/language to get started.

Whichever tool/language you choose, you will have to master it completely. But which one to choose? This has been one of the most asked questions by beginners.

The most straightforward answer to this question would be to choose any of the mainstream tools/languages.

Although tools are merely a means for implementation, having a proper understanding of tools is essential.

4. Join a Peer Group

Now that you are done with the most critical part, the next thing is to join a community. But why? Why does everyone say to join a community?

Well, a community or a peer group keeps you motivated as well as updated about all the recent changes. And the most significant benefit is collaborations among the peer members, which ultimately leads to solid networking.

You collaborate with your fellow data scientists and discuss various sorts of stuff, be it the issues you are facing or anything else.

You can join millions of peer groups available over the internet and connect with people who share similar goals.

And, I would suggest that you join the communities or peer groups with people with whom you can physically interact.

On top of it, tons of online forums available over the internet will give you the same kind of environment. You can familiarize yourself with the actual problems people face during their career or while pursuing data science.

5. Focus on Practical Applications

More than 60% of those who take up the courses end up not making any growth! But why?

The answer is pretty simple.

• They don’t focus on practical application; they just focus on learning concepts and definitions.

While undergoing training, you should not only focus on theoretical knowledge but on practical applications as well.

This helps you understand the concepts in a better way and gives you a more profound sense of their real-life application.

A few tips you should follow when pursuing a course:

• Do all the projects and assignments to have a better understanding.
• Collect random data, store it, and try interpreting results from it. You may not understand the things initially, but it will be very helpful in the long run.
• Read the research papers, models, and case studies of all the big shots of the field to know what works or what does not.

Another way is to participate in online or offline data science competitions to build up your resume and hone your existing skills. Even learn new things which may not be covered in your course.

6. Follow the Right Resources

Learning must never stop. It would be best if you swallow every drop of information available right there. But it has to be more right sources.

You would find numerous blogs run by the most influential Data Scientists.

These Data Science blogs are really active and keep updating their followers on their findings and regularly post about the recent advancement in this field.

Make this a habit to read one such blog on a daily basis to stay updated about what’s happening in the field.

7. Work on Your Communication Skills

Communication skills are a must, no matter what industry you work in. But in the case of data science, it is the key to your career trajectory.

Data scientists stand at the middle level of management and deal directly with higher management. They are the ones who connect with the management for problems.

They also present and explain their findings to the management and shareholders that too in a more straightforward and non-technical manner.

Shareholders or higher management may not have the technical knowledge. That’s why data scientists are required to take the help of storytelling to explain the numbers and graphs in a simple and plain manner.

And that is the reason why a data scientist must have top-notch communication skills. Most people have this misconception that being technically profound is enough to ace the interview.

But that is not true. So, make sure you also work hard on your soft skills like critical thinking, networking, and communication skills, if you want to become a top-notch and renowned data scientist.

8. Network with Like-minded People

In the beginning, your entire focus has to be on learning and following the right mentors and industry leaders.

If you do too many things at the initial stage, you’ll be overwhelmed and eventually give up because of too much pressure.

Once you have learned enough and got the hang of the field, then you can pack your bags up for:

• Industry events and conferences
• Meetups in your area
• Different hackathons and competitions – even if you know only a little.

The benefit of attending all these events and doing all these meetups is that you will get to network with a lot of people.

This is where you actually start building a brand for yourself. This way, you get to meet people who work actively in the field, and it gives you an opportunity to establish a solid professional relationship with them, which will undoubtedly help you advance in your career.

Let me list out some of the significant benefits of networking

• Networking gives you insider information about the field of interest
• It helps you find the best mentor
• You can land a job, either through some leads, references, or direct employment opportunities.

9. Be Prepared to Un-Learn and Re-Learn

Data science is a field that requires you to stay updated with all the recent trends and updates.

Believe me; this field is so affected by the changes that the tool you learned yesterday tends to get useless the very next day.

So, be ready to learn, unlearn, and again rel-learn a lot of things. So, always focus on keeping yourself updated with new knowledge.

10. Become a Proactive Problem Solver

Data science is all about finding the problems persisting in the business and finding their solutions.

I can write this on a wall, that you can not become a data scientist unless you have the skills or desire to solve problems.

A good data scientist is a proactive problem-solver who has the desire to dig to the root of an issue.

Sometimes problems are not easy to find or hidden, and that is when a data scientist has to wear his detective’s hat to identify the problem and find the most effective way to solve it.

11. Intellectual Curiosity

As we just discussed, data science is all about finding problems, their solutions, and answering questions that data presents.

But a good data scientist must have the curiosity and a drive to find and answer questions that were never asked or were hidden inside the data.

Data scientists work to discover underlying truths and never settle for “just enough,” but stay on the continuous hunt for answers.

Having this curiosity will help you dive deeper into the data and think creatively.

12. Business Sense

I believe that data scientists are half businessmen. They must not only know about their own field but also about the sector or industry they are giving their services to.

Most data scientists are hired by organizations to crack business-related data that further help organizations make better business decisions.

And to ace this part, a data scientist should deeply understand the business.

Having a good sense of the business world can help a data scientist understand organizational problems precisely and translate the data into such insights that actually work for the company.

They should have proper knowledge about the current market conditions to understand the underlying problem properly.

A data scientist is not merely an employee but a business-minded person who understands everything about the business.

13. Basic Database Knowledge and SQL are a must

Everyone has to start with the basics. Generally, all data science beginners start their journey by using data in the form of excel files or CSV.

I would advise you to learn the most fundamental skill that is SQL.

Having solid basics will be much more helpful than having fancy words in your resume.

Knowledge of SQL will help you deal with the small day-to-day tasks of your organization.

14. Guidance is Essential

Big Data is a relatively new field, and only a few people have been able to decrypt their path in this field. And most of the people are just getting started.

So, the best thing to do is to find a mentor who has steered his way to a reputed position in the field of data science.

Start following them, learn about their journey, failures, and success, and learn how they did it; and how you can do it too.

Having a mentor does not mean having a personal mentor but someone whose advice you can follow or with whom you can relate your journey to.

Plus, you can also try to make good connections with your data science teachers and see if they can mentor you.

15. Join an Internship

Once you have finished your course, it’s time to join an internship. You can go for an internship also while pursuing the course.

Internships are a great way to learn things at an early stage. I suggest that people should do a few internships before joining a full-time job because you cannot commit mistakes when you do a full-time job.

You are answerable for each mistake you make, plus they treat you as a skilled individual, an individual who knows everything.

But that’s not the case with an internship. You are treated as a newbie and a student who does not know much about the work and industry and is keen to learn new things.

So, before you plan to step out in the market as a professional, step out as an intern to have the proper understanding of the industry.

And another benefit of internships is that, in most cases, companies hire their interns as full-time employees.

16. Keeping up with your Resume Game

Now the final point, which is reasonably the most crucial one – Building Your Resume.

Let’s say you are all done and are ready to start a career in data science. But is it easy? Well, I would say… it is easy, at least in the field of data science.

But I’ll be honest! It’s not even that easy. No matter how good you are with your skills and knowledge, it’s all futile until you impress the recruiter.

But what’s the very first step to impress the recruiter? It is your resume. Your resume is the first thing that can give or take the opportunity.

Only if your resume is good enough will you get the chance to get interviewed. Include everything you have done in your resume, from your school achievements to the projects you have completed during the course tenure.

Here are some of the things that you can include in your resume –

• Prioritize skills based on the role offered
• Mention all the projects you have done to prove your skills
• Mention your GitHub profile or forum profile
• Include certifications
• Be strategic with the orders of your achievements

Conclusion

There is a huge demand for data scientists in the market, and organizations are ready to pour huge money on data science professionals.

So taking the proper steps and having the right skills will lead to exponential growth. I know learning data science can be a little overwhelming but have patience and keep improving.

Data science is a vast field that has so much to learn, so obviously, it will take a lot of time.

Anyways, I hope this guide was at least a little bit helpful for you to learn something new or to help you get started in this booming field.

One last tip to start a career in data science would be – learn from others’ mistakes to avoid making mistakes that can cost you your time as well as money.

We ExcelR solutions are into this Ed-tech space from 2014. ExcelR is an initiative from the alumni of IIT & IIM which have footprints around the globe.

We are proud of our quality of training and a team of our top-notch faculty members who are from IIT & IIM. We deliver training for both Freshers and Young professionals who want to transform their careers into the field of Data Science.