Build a Career in Data Science with these 7 tips
Today’s economy is inclining more toward analytics—companies have been gathering data for long years. According to LinkedIn, there is a huge demand for individuals who can mine and interpret data and they are the data scientists.
Learning data science can be scary. Particularly in this way, when you are just starting your journey. Which tool to learn – R or Python? What procedures to focus on? Do I need to learn how to do coding? These are some of the numerous questions you need to answer as part of your journey.
Who is a Data Scientist?
Data scientists are a blend of mathematicians, trend-spotters, and computer scientists. The data scientist’s role is to translate large volumes of data and carry out further analysis to find patterns in the data and gain a profound understanding into what it all means. Data scientists operate between the business and IT worlds and drive industries by examining complex datasets to coax out bits of knowledge that companies can leverage into actions.
If you’ve been thinking about how to start a career in data science, you’ll need hard skills like analysis, machine learning, statistics, Hadoop and so forth. In any case, you’ll also excel in this kind of role if you excel at critical thinking, persuasive communications, and are an extraordinary listener and problem solver.
This article would set an outline that can help you learn data science through this difficult and intimidating period.
1. Choose the right role
There is a great deal of varied roles in the data science industry. A data visualization expert, a machine learning master, a data scientist, data engineer, and so on are a couple of the many roles that you could go into. Contingent on your background and your work experience, getting into one role would be simpler than another role. For instance, if you are a software developer, it would not be hard for you to move into data engineering. So, until and unless you are clear about what you want to become, you will remain confused about the path to take and skills to hone.
2. Take up a Course and Complete it
Now that you have chosen on a role, the next logical thing for you is to place in a dedicated effort to comprehend the role. This implies not simply going through the requirements of the role. The demand for data scientists is huge so thousands of courses and studies are available to hold your hand where you can learn whatever you want to. Finding material to learn from isn’t a hard call however learning it may become if you don’t invest your efforts.
What you can do is take up a MOOC which is freely accessible, or join an accreditation program which could take you through all the twists and turns the role involves. The decision of free vs paid is not the issue, the fundamental objective should be whether the course clears your basics and takes you to a suitable level, from which you can push on further.
At the point when you take up a course, go through it actively. Follow the coursework, assignments, and all the discussions occurring around the course.
3. Choose a Tool / Language and stick to it
As I mentioned previously, it is significant for you to get an end-to-end experience of whichever topic you seek after. A difficult question which one faces in getting hands-on is which language/tool would it be advisable for you to pick?
This would likely be the most posed question by beginners. The clearest answer would be to choose any of the mainstream tools/languages there is and start your data science journey.
4. Join a peer group
Why is this significant? This is because a peer group makes you motivated. Taking up a new field may seem a bit overwhelming when you do it alone, but when you have companions who are alongside you, the task seems a bit simpler.
5. Focus on practical applications and not just theory
While going through courses and training, you should focus on the hands-on applications of things you are learning. This would help you not only comprehend the concept as well as give you a deeper sense of how it would be applied in reality.
Work on a couple open data sets and apply your learning. Regardless of whether you don’t understand the math behind a technique at first, understand the assumptions, what it does, and how to interpret the outcomes. You can always develop a profound understanding at a later stage.
6. Basic Database knowledge and SQL is a must
Data doesn’t mystically show up in the form of tables. Typically, beginners start their machine learning journey by utilizing data in the form of CSV or an excel file. But something is certainly missing! It’s SQL. It is the major fundamental skill for a data science professional.
Knowledge of data storage techniques along with the essentials of big data will make you much more considerably than a person with hi-fi words on the resume.
7. Model Deployment is your secret sauce
Model Deployment is not included in many beginner-level data science guides and this is a pathway to disaster.This is one of the most important phases from a business perspective yet also the least taught one.
Remember that the end-user, who needs this model wants to be used by multiple people at the same time who are NOT data scientists. Subsequently they’ll not be running a Jupyter or Colab notebook on GPUs. This is the place where you need a complete process of model deployment.
Regardless of whether it isn’t the employment prerequisite of your company, it is very critical to know the basics of model deployment and why it is essential.
Data Scientist Job Market
With millions of worldwide employment opportunities in Big Data, the role of a data scientist has become the most sizzling position job of the decade. With the right capabilities, you’ll enjoy a bright career outlook as a data scientist. The demand for individuals with these abilities will continue to increase, and those already in data science roles are certain to see their salaries increase in the future.
