5 Things You Should Know Before Choosing a Data Science Course
The advent of data science, machine learning and artificial intelligence has evidently disrupted the job market. Which means the traditional jobs supported by the traditional skill sets have taken the back seat while a wave of new jobs with different requirements and challenges have come forward. Data science had hardly gotten out of the academic closet for decades. Now, all of a sudden it is out their in the middle, controlling industries and taking the market by storm. In the current situation the only logical move would be to gravitate towards the skills that can help you survive and succeed. If you have already given a career in data science some thought, these are things that you need to know before you start.
A data science course does not turn you into a data scientist
There is a lot of confusion about the different roles and responsibilities in and around the field of data science. While the most glorified of all roles is that of a data scientist, you should also be aware that there is nothing such as an entry-level data scientist. It is not something you can become after finishing a course or getting a degree. It is a gradual process which may take years. So you should be ready for a good start as a data architect, data engineer, data analyst, so on and so forth.
It is important to start at the right point
With the number of different courses and material available out there one could make a career shift to data science from any stream. For example, a linguistics major can totally kill it in the field of natural language processing; or a psychology student can do a great deal in the field of behavioural analytics. There is something for everyone since we are all essentially working with data. You can even start with different Excel analyst courses before getting your data science certification. The important thing is to know what you do not know and not to skip it. For instance if you know the basics of statistics and a couple of spreadsheet sheet tools like Excel you can easily go for R training.
Managing your time is of essence
A bulk of the data science professionals come from different professional fields. The transition period is difficult because one has to carry on with the old job while studying for a new one. Whatever course you choose, you must deploy your resources thoughtfully, which in this case are time, money and energy.
Learn from the practitioners
The best part of a good informal course is its focus on the industrial aspect of learning rather than the academic aspect. You should always look for training programmes that help you acquire practical knowledge, hence it is better to learn from the working professionals.
Keep an eye open for disruptions
The tech world has always been characterized by disruptions. That is the way of its growth and it always takes some people down. We cannot help it, what we can do is prepare for it. Stay ready for further shifts and more learning.