Essential Tips for People Starting a Career in Data Science
Learning data science can be daunting. Especially so, when you are just beginning your journey. Which tool to learn – R or Python? What strategies to focus on? How many statistics to learn? Do I need to learn to code? These are some of the questions you need to answer as a part of your journey.
That is why I assumed that I would certainly create this guide, which could help people starting in Data Science or Analytics. The concept was to create a simple, not very long guide that can set your path to learning data science. This guide would set a structure that can help you learn data science through this difficult and challenging period.
Beginning and navigating through the data science career can become a complicated challenge for beginners due to the abundance of resources. It is not rocket science, it is Data Science. What you need is proper support and a roadmap to become a successful data scientist.
Choose The Right Role
There are a lot of different roles in the data science industry. A data visualization expert, data engineer, a data scientist ,a machine learning expert etc are a few of the many roles that you can enter into. Depending upon your background and your work experience, entering into one role would be easier than another role. For example, if you a software designer, 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 course to take and skills to develop.
Take Up a Course & Complete it
When you take up a course, go through it actively. Follow the projects, assignments, and all the conversation 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 need to carefully 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.
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Choose a Tool/ Language and Stick to it
As I said before, it is important for you to get an end-to-end experience of whichever topic you pursue. A tough question which one faces in getting hands-on is which tool /language should you choose?
This would probably be the most asked question by beginners. One of the most simple answer would be to choose any of the mainstream tools/languages there is and begin your data science journey. After all, tools are just a means for implementation; however understanding the concept is more important.
Still, the question remains, which would be a much better option to start with? There are numerous discussions/ guides on the internet which resolve 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 need to choose GUI based tools for now. Then as you get a grasp on the concepts, you can get your hands-on with the coding part.
Join a Peer Group
Now that you know which role you want to select and also getting prepared for it, the next important thing for you to do would be to join a peer group/colleagues. Why is this important? This is because a peer group keeps you inspired. Taking up a new field might appear a bit difficult when you do it alone, however when you have friends who are along with you, the task seems a little bit easier.
One of the most preferable way to be in a peer group is to have a group of people you can physically communicate with, or else you can either have a number of people over the internet who share similar goals, such as joining a Massive online course and communicating with the batch mates.
Even if you don’t have this type of peer group, you can still have a meaningful technical discussion over the online. There are online discussion forums that give you this kind of environment.
Work On Your Communication
People don’t usually associate communication skills with rejection in data science roles. They expect that if they are technically knowledgeable, they will ace the interview. This is actually a myth. Ever been rejected within an interview, where the recruiter said thank you after listening to your introduction?
Try this activity once; make your friend with good communication skills hear your introduction and ask for honest feedback. He/she 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 coworker or to prove your point in a meeting, you should know how to communicate effectively.
Basic Database Knowledge and SQL is must.
Data doesn’t magically appear in the form of tables. Generally, beginners start their machine learning journey by utilizing data in the form of CSV or an excel file. Yet something is definitely missing! It’s SQL. It is one of the most basic skill for a data science specialist.
Knowledge of data storage techniques along with the fundamentals of big data will make you much more beneficial than a person which hi-fi words on the resume, it’s because companies are still figuring their data science requirements.
Network
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 quit.
progressively, once you have got a hang of the field, you can go on to paticipate industry conferences and events, popular meetups in your area, participate in hackathons in your area – even if you know only a little. You never ever know who, where, and when will help you out!
Actually, a meetup is very useful 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 gives you networking opportunities along with developing 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.
Assist you to have mentorship support.
Help you search for a job, this would either be ideas on job searching through leads or possible job opportunities directly.
Guidance is Essential
Coming to the final point which is probably one of the most important one – finding the best guidance. Data Science and machine learning, data engineering, and relatively very new field and so are its graduates. There are only a few people who have actually 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 annoyed with the recorded videos or even follow along with a youtube playlist but you are still not an industry-ready expert.
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