- No comments
Job hunting in itself is a very daunting task, almost all of us have experienced the anxiety over acquiring enough knowledge, and getting through the challenging task of landing ‘The Dream Job’. Add to that the desire to get into the ever-evolving world of data science, let’s admit, it can be very intimidating, more so at the entry level. Preparation is the key to success, it is what takes your dependency away from luck and progresses towards definite success. This blog is a small effort in the direction of laying down the guide which could perhaps clear the vision of people, thinking of starting their career in data science or analytics. It is aimed at assisting you in decoding the framework which can help you to learn the relevant skills in data science.
- Clarity in Goal
First thing first, be clear what do you need to advance in, the field of data science is very vast and varied. Data analyst, Data Engineer, Machine Learning Analyst, Data Architect, Data analyst, or the revered data Scientist, as you can see there are many options available in the field of data science. The choice also depends on factors like your academics, interest, work experience, etc…, for example, if you are a software developer, getting into data engineering would be the most obvious choice. Hence clarity of though is imperative to avoid confusion and fluctuation from one vertical to another. You can gain clarity by talking to people from the industry, career counseling, conduct your own research and choose the role that suits your interest and field of study. Do not make a hasty decision.
- Upscale with the Help of a Course
Once you have decided on the role, assess what lacks and accordingly pick up a course that will help you reach the goal. Taking up a course not only adds to your skills but it will help you network with like-minded people, further making it that much more possible to land your dream job. Data science is considered the coolest field to work in, so clearly, besides the high demand, there is also a high number of applicants, hence ensure you are skilled and networked enough to grab the opportunity when you see one.
- Pick up a Tool/ Language and practice
Breaking into the data science industry is tough, hence it is important that you have an understanding of what lies underneath data science. Statistics, Machine Learning, Software Engineering, Math, Data Mining, Data Mugging, Probability, Predictive Analytics, Sentimental Analytics etc…, are some areas that you need to have knowledge in. Fluency in Programming Languages like R and Python, GUI tools, and coding is a must have.
- Practical Application
Even when undergoing a course, ensure you learn with practical experience as opposed to theory, this way you will not only understand the concept but will also have a sense of applicability. A tip is to practice what you learn in theory as a continuous process. Continue to research and read in the area of interest, blogs and white papers by the most influential data scientist can be a starting point.
- Soft Skills
In the effort of acquiring all the technical know-how do not overlook sharpening your soft-skills. A person working in the data science field needs to possess a combination of technical, analytical and presentation skills. It is not only required from you to be a creative problem solver but to be able to communicate the solution to a non-technical audience to get their buy-in.
Data science is an evolving field, and there are no signs of it slowing down or becoming redundant. Perks such as big pay packs, and job security aside, you also get to make a big impact in the organisation, by solving complex problems. Overall it is indeed a rewarding career choice. So to advance in this field take the right steps to refine your skills to inch towards securing the job of your dreams.