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8 Essential Tips for People Starting a Career in Data Science

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8 Essential Tips for People Starting a Career in Data Science

If you want a career in data science but you're new to the field, check out this article learn what tools and languages to learn, what techniques to focus on, and more!

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There is a saying by Nick Bostrom that machine learning is the last invention that humanity has to make. I am new to the data science career and have just stepped into the "future of technology." I have a bundle of questions at this stage. What tools and languages should I learn? What new techniques should I focus on? You'll be faced with these and many more as you proceed with your journey.

This article by Faizan Shaikh has proved to be a beautiful guide to pave a pathway for all data scientists to start their career in data science.

Just follow these eight essential tips and you will get a good head start in your career.

So let’s get started!

1. Choose the Right Role

There are a lot of varied roles in the data science industry. Data visualization expert, machine learning expert, data scientist, and data engineer are a few of the many roles that you could go into. Depending on your background and work experience, there are related important job roles in the data science industry.

But what if you are not clear about the differences or you are not sure what should you become? A few things I would suggest are:

  • Talk to people in the industry to figure out what each of the roles entails.
    • Take mentorship from people; request them for a small amount of time and ask relevant questions. I’m sure no one would refuse to help a person in need!
  • Figure out what you want and what you are good at and choose the role that suits your field of study.

There's a descriptive comparison on what is it like being a data scientist vs. data engineer vs. statistician on the Analytics Vidhya blog. I’m sure it will help you reach your decision. 

2. Take Up a Course and Complete It

Now that you have decided on a role, the next logical thing for you is put in a dedicated effort to understand the role. The demand for data scientists is huge — so thousands of courses and studies are out there to hold your hand so that you can learn whatever you want to! Finding material to learn from isn’t difficult, but learning it may be difficult if you don’t put in the effort.

You can take up a MOOC that is freely available, or join an accreditation program.

When you take up a course, go through it actively. Follow the coursework, assignments, and all the discussions happening in the course.

Some good MOOCs to look for include:

3. Choose a Tool/Language and Stick to It

As I mentioned before, it is important for you to fully understand whichever topic you pursue. A difficult question you're going to face is, Which language/tool should I choose?

Which would is the best option to start with? There are various guides and discussions on the internet that address this question. The basic idea is that you should start with the simplest language or the one with that you're most familiar with. If you are not as well-versed with coding, you should stick to GUI-based tools for now. Then, as you get a grasp on the concepts, you can get your hands dirty with the coding part.

4. Join a Peer Group

Now that you know that which role you want to opt for and are getting prepared for it, the next important thing for you to do is join a peer group. Why is this important? A peer group keeps you motivated and keeps you updated with the trends. 

The best peer group is a group of people who you can interact with, who shares similar goals as you do, and who can share inspiring stories with you.

There are online forums that give you this kind of environment. A few of them are:

  1. Analytics Vidhya
  2. StackExchange
  3. Reddit

5. Focus on Practical Applications, Not Just Theory

While undergoing courses and training, you should focus on the practical applications of things you are learning. This will help you not only understand the concept but also give you a deeper sense on how it can be applied in reality.

A few tips you should do when following a course:

  • Make sure you do all the exercises and assignments to understand the applications. Join discussion forums and start asking and answering questions.
  • Work on a few open data sets and apply your learning.
  • Take a look at the solutions by people who have worked in the field and trends going on in the industry

6. Follow the Right Resources

To never stop learning, you have to engulf each and every source of knowledge you can find. The most useful source of this information is blogs run by influential data scientists. These data scientists are really active in the community and frequently update their followers with the recent advancements in this field.

Read about data science every day and make it a habit to keep updated with the recent happenings, along with solving real-world problems

Here is a list of data scientists that you can follow. Here are few newsletters to keep you on the go:

  1. WildML
  2. NYU
  3. KDnuggets News 

7. Work on Your Communication Skills

People don’t usually associate communication skills with rejection in data science roles. They expect that if they are technically profound, they will ace the interview. This is actually a myth. Ever been rejected during an interview, where the interviewer said "thank you" after listening to your introduction?

Try to enhance your data scientist interview skills and be prepared with the generally asked interview questions. Approach your friend with good communication skills and ask for honest feedback. 

Communication skills are even more important when you are working in the field. To effectively share your ideas with a colleague or prove your point in a meeting, you should know how to communicate efficiently.

8. Network, but Don’t Waste Too Much Time on It!

Initially, your entire focus should be on learning. Doing too many things at initial stage will eventually bring you up to a point where you’ll give up.

Gradually, once you have gotten a hang of the field, you can go on to attend industry events and conferences popular meetups in your area and participate in hackathons in your area — even if you only know a little. You never know who will help you out!

A networking contact might:

  • Give you inside information on what’s happening in your field of interest.
  • Provide mentorship support.
  • Help you search for a job (this could either be tips on job hunting through leads or possible employment opportunities directly).

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Topics:
data science ,big data ,career

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