12 Essential Tips for People starting a Career in Data Science

Learning data science can be intimidating. Especially so, when you are just starting your journey. Which tool to learn – R or Python? What techniques to focus on? How many statistics to learn? Do I need to learn to code? These are some of the many questions you need to answer as part of your journey.

That is why I thought that I would create this guide, which could help people starting in Analytics or Data Science. The idea was to create a simple, not very long guide that can set your path to learning data science. This guide would set a framework that can help you learn data science through this difficult and intimidating period.

Starting and navigating through the data science career can become a daunting challenge for beginners due to the abundance of resources. It is not rocket science, it is Data Science. What you need is proper guidance and a roadmap to become a successful data scientist.

1. Choose the right role

There are a lot of varied roles in the data science industry. A data visualization expert, a machine learning expert, a data scientist, data engineer, etc are a few of the many roles that you could go into. Depending on your background and your work experience, getting into one role would be easier than another role. For example, if you a software developer, it would not be difficult for you to shift into data engineering. So, until and unless you are clear about what you want to become, you will stay confused about the path to take and skills to hone.

What to do, if you are not clear about the differences or you are not sure what should you become? I few things which 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.

To clear the confusion, here is a great resource to differentiate between business analyst, data scientist, and even data engineer –

2. Take up a Course and Complete it

Now that you have decided on a role, the next logical thing for you is to put in a dedicated effort to understand the role. This means not just going through the requirements of the role. The demand for data scientists is big so thousands of courses and studies are out there to hold your hand, you can learn whatever you want to. Finding material to learn from isn’t a hard call but learning it may become if you don’t put effort.

What you can do is take up a MOOC which is freely available, or join an accreditation program which should take you through all the twists and turns the role entails. The choice of free vs paid is not the issue, the main objective should be whether the course clears your basics and brings you to a suitable level, from which you can push on further.

When you take up a course, go through it actively. Follow the coursework, assignments, and all the discussions happening around the course. For example, if you want to be a machine learning engineer, you can take up Machine learning by Andrew Ng. Now you have to diligently follow all the course material provided in the course. This also means the assignments in the course, which are as important as going through the videos. Only doing a course end to end will give you a clearer picture of the field.

3. Choose a Tool / Language and stick to it

As I mentioned before, it is important for you to get an end-to-end experience of whichever topic you pursue. A difficult question which one faces in getting hands-on is which language/tool should you choose?

This would probably be the most asked question by beginners. The most straightforward answer would be to choose any of the mainstream tools/languages there is and start your data science journey. After all, tools are just a means for implementation; but understanding the concept is more important.

Still, the question remains, which would be a better option to start with? There are various guides/discussions on the internet which address this particular query. The gist is that start with the simplest of language or the one with which you are most familiar. if you are not as well versed with coding, you should prefer GUI based tools for now. Then as you get a grasp on the concepts, you can get your hands-on with the coding part.

4. Join a peer group

Now that you know which role you want to opt for and are getting prepared for it, the next important thing for you to do would be to join a peer group. Why is this important? This is because a peer group keeps you motivated. Taking up a new field may seem a bit daunting when you do it alone, but when you have friends who are alongside you, the task seems a bit easier.

The most preferable way to be in a peer group is to have a group of people you can physically interact with.  Otherwise, you can either have a bunch of people over the internet who share similar goals, such as joining a Massive online course and interacting with the batch mates.

Even if you don’t have this kind of peer group, you can still have a meaningful technical discussion over the internet. There are online forums that give you this kind of environment.

5. Focus on practical applications and not just theory

While undergoing courses and training, you should focus on the practical applications of things you are learning. This would help you not only understand the concept but also give you a deeper sense of how it would 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.
  • Work on a few open data sets and apply your learning. Even if you don’t understand the math behind a technique initially, understand the assumptions, what it does, and how to interpret the results. You can always develop a deeper understanding at a later stage.
  • Take a look at the solutions by people who have worked in the field. They would be able to pinpoint you with the right approach faster.

The best way to build your machine learning profile is to participate in data science competitions and get a feel for data science projects. Analytics Vidhya’s DataHack platform offers you dozens of projects to chose.

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 the most influential Data Scientists. These Data Scientists are really active and update the followers on their findings and frequently post about the recent advancement in this field.

Read about data science every day and make it a habit to be updated with the recent happenings. But there may be many resources, influential data scientists to follow, and you have to be sure that you don’t follow the incorrect practices. So it is very important to follow the right resources.

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 within an interview, where the interviewer said thank you after listening to your introduction?

Try this activity once; make your friend with good communication skills hear your intro and ask for honest feedback. He will definitely show you the mirror!

Communication skills are even more important when you are working in the field. To share your ideas with a colleague or to 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 the initial stage will eventually bring you up to a point where you’ll give up.

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

Actually, a meetup is very advantageous when it comes down to making your mark in the data science community. You get to meet people in your area who work actively in the field, which provides you networking opportunities along with establishing a relationship with them will in turn help you advance your career heavily. A networking contact might:

  • Give you inside information of what’s happening in your field of interest
  • help you to have mentorship support
  • Help you search for a job, this would either be tips on job hunting through leads or possible employment opportunities directly.

9.  Basic Database knowledge and SQL is a must

Data doesn’t magically appear in the form of tables. Usually, beginners start their machine learning journey by using data in the form of CSV or an excel file. But something is definitely missing! It’s SQL. It is the most fundamental skill for a data science professional.

Knowledge of data storage techniques along with the basics of big data will make you much more favorable than a person which hi-fi words on the resume, it’s because organizations are still figuring their data science requirements.

These organizations want SQL professionals that can help them with their day-to-day tasks.

10. Model Deployment is your secret sauce

Model Deployment is not even added in many beginner-level data science roadmap and this is a pathway to disaster.

Once you have made the complete data science project, it is time for the intended user/ stakeholder to reap the benefits of the predictive power of your machine learning model. In simple words, this is model deployment. This is one of the most important steps from a business point of view but also the least taught one.

Let us take an example here. An insurance company has initiated a data science project which uses Vehicle images from accidents to assess the extent of the damage. The data science team works day and night to develop a model that has a near-perfect F1 score. After months of hard work, they have the model ready and the stakeholders love its performance but what after that?

Remember that the end-user, in this case, are the insurance agents and this model needs to be used by multiple people at the same time who are NOT data scientists. Therefore they’ll not be running a Jupyter or Colab notebook on GPUs. This is where you need a complete process of model deployment.

This task is usually done by machine learning engineers but it varies according to the organization you are working in. Even if it is not the job requirement of your company, it is very important to know the basics of model deployment and why it is necessary.

11. Keeping up with your resume game

Let’s solve a riddle here – What’s the first thing that the recruiter experiences about you which may be your last? It’s your Resume! These are the ultimate obstacle that you must pass through to get the most coveted job!

Make sure that you include these pointers in your next resume –

  • Prioritize skills according to the job role offered
  • Mention data science projects to prove your skills
  • Don’t forget to mention your GitHub profile
  • Skills are more important than Certifications
  • Update your skills and projects side-by-side and not once in a blue moon.
  • Overall resume counts – make sure all your fonts and format are standard all along

Resumes and Interviews can be hard and requires an exhaustive preparation of each and every skill and project you mention in your resume. Get access to all the resume tips and tricks along with an endless number of interview questions as part of the Analytics Vidhya’s Blackbelt + program. The goal is not to help you become an industry-ready professional. 

12. Guidance is essential

Coming to the final point which is perhaps the most crucial one – finding the right guidance. Data Science and machine learning, data engineering, and relatively a very new field and so are its alumni. There are only a few people who have decrypted their path in this field.

There are many ways to become a data scientist, the simplest one is to cough up lakhs of rupees for a recognized certification only to later get frustrated with the recorded videos or even follow along with a youtube playlist but you are still not an industry-ready professional.

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