To secure a job in any domain one has to give it a lot of preparation, should be trained for the role and should have absolute knowledge about the field, usually people will dedicate years in preparing for their desired roles. Shifting from a prepared role of domain to a different domain will not usually be easy, strong gust of skepticism would surely haunt. The process of shifting from one domain to another is hard, it gets harder to learn data science for working professionals because they will have to prepare for the new job role while maintaining their current one.
If and only if you plan the whole process of domain shifting in an organised and rational way, you can have a win-win situation.
Have a vision and plan your strategy
You must win in both the games of learning and working, for that you will have to strategize in such a way that your time in learning data science should not in any way collide with your work life and vice-versa. Because both of the activities are equally important as they require immense attention and individual preference.
let us start from the scratch, here are some possible concerns of a working professional:
- Time management
- Balancing the energy between two activities
- Scheduling
- Risk of affording a wrong move
- Risk of inefficient or improper execution
As a working professional you will have to manage your responsibilities in a way that you will have control over every single thing that happens to exist. With proper planning and the right way of approach, the above mentioned concerns could be easily tamed.
Firmly state your purpose of learning data science
Why do you want to change your domain into Data Science while you already have a job? firmly define the purpose. You should know that by shifting to data science everything will change, you will have to develop new skill sets for the role that you are targeting, processing of workflow will be different, your future job role will have different goals, purpose and aim. Act consciously when you are risking to give up on the comfort and expertise you have in your current job, be very sure about the purpose of doing so. Doing this will eliminate the skepticism about the risk of getting out of your comfort zone. The efforts that you put over learning Data Science will never go in vain because you will learn about the currently trending technologies and tools, that will help you survive not only in data science but anywhere in the IT firm.
Have a soft target
People think only the role of ‘data scientist’ matters the most but the fact is that there are several other roles in data science which significantly matter in the field, choose one role that which you want to become and start preparing for it. Doing this should be good for the starters, because you do not have to be a scholar in every tool that has ever been used in the field, smartly target those topics that are the essentials in Data Science. When you specifically work on a targeted role you will have the chance to completely know about it and its importance in the field. This way of approach will be a very smart move because you will not be confused regarding what exactly to study in the vast field of data science and the field generally prioritizes those who holds master expertise in specified field. So be very sure about the role you want to serve in, in data science.
Plan the execution
To perfectly plan the execution part you will first have to design the implementation part, do it wise and rationally. Revise your daily-life activities, reschedule it for the sake of balancing between learning and working.
Exercise on the way you spend time on everyday things, revise it according to your daily schedules. Practice to make a note of your tasks everyday, according to that plan on how much time you would invest on the things and try your best to act as decided. In other words, this way of dealing with the things is called as discipline, to have a structured day you will have to practice discipline in all possible ways. Revise your activities from sleeping habits to break sessions, reschedule them in such a way that the things will itself fall in the right place. Set targets, set your own deadlines and design the way that you want things to work in.
Networking and understanding the field
Involve with the people that come from the field of Data Science, know about the insider story of the field and about how it works. Having field knowledge is very much necessary, remember that when you get into data science you will have to work in teams, so practice skills in communication and confidence. Get interactive with the people by asking them about the ways to reach to the field, this way you will build good connections and will get great suggestions as well. Start associating yourself with the people who belongs to Data science, you will need to get used to that.
A good course
Everything that you do and every effort that you put is only to learn Data Science, but if you make the mistake of choosing a wrong course every effort of yours will go in vain. Your purpose of learning Data Science is to shift your domain into that of Data science, you cannot do this without the help of a good course. The course that you choose should not only help you to have fine knowledge in data science but also should help you to manage your planned schedules. There are many data science courses that are specially built for working professionals, it will greatly help if you choose the right one among them.
Conclusion
With the right approach and proper planning you can triumph in learning Data Science while maintaining a full time job. Stick to your plans and preparations, seek help from a good course, practice as much as you could and start involving yourself with the field. If you manage to everyday execute the plans you will surely reach your destination in ease.
Learnbay could help you
The data science course of Learnbay is specially designed for working professionals, the benefits provided in the course will help you balance your scheduling. Learnbay powered by IBM will help you throughout the journey of learning and experiencing data science.
