Data science is amongst the fastest growing aspects in technology today. Data has always been around, but the difference is that today, a lot more data is being collected at a very fast rate and there is now an ability to get valuable insights, and do a lot more with the collected data. This looming power of data science has grown at such a fast pace that skilled data scientists are actually hard to find. A report by McKinsey suggested that there will soon be a shortage of 190,000 data scientists within a year in the US alone and another shortage of over 1.5 million of them in the coming years.
So how can IT job aspirants or professionals in the IT industry cash in on this huge demand? By becoming a job ready data scientist of course! Here are 4 useful things than an aspiring data scientist must know:
- Statistical Learning: Picking up meta skill of statistical thinking is very important for a data scientist. This includes:
- Looking at data as being taken from a generative model through parameters by probability distributions
- Picking up biases in data and figuring out how sampling methods can be used to correct biases
- Detecting garbage data soon enough so as not to proceed with inference and considering experimental design instead
This meta skill of statistical thinking happens only with practice. If you can work with a project that involves this aspect, then it would give you good experience right in the start. Here, a lot of logical thinking also goes into play.
- Software Engineering: Software Engineering is basically about three major things:
- Learning how to organize and connect abstract ideas in a logical method that is user friendly
- Code well, write tests and document
- Staying familiar and updated on the ever-evolving ecosystem of software packages used.
Software Engineering is crucial for data scientists since predictions making models are usually put into the production system and are used beyond the Data scientists themselves.
- Business Cases that are Industry-Specific: Business cases can be very industry specific and an aspiring data scientist usually gets exposed to them only in tests or interviews. Here the data scientists’ imaginative capacity and experience (if he/she is not a fresher) counts. The two areas that are tested are – The creativity required to solve complex business problems and the passion as a drive to solve them. Both areas are tough to fake when faced with a properly designed business cases. For a beginner, it is advised to pick general industries or ones that they plan to target first. They can then go for meet ups or network with professionals in those fields to get a good exposure of the industry. Reading journals of the specific industry is a great way to start. This gives one good context when working with data of that specific industry.
- Computer Science Fundamentals: This would be coding in basic programming languages apart from data structures and algorithms. This comes into play when you have to crack an interview. Problem solving with the fundamentals will also be used at work, even if only at a later date. The subjects under this are vast but you should learn to prioritize based on your target industry. For ex. If your target industry is biotechnology, starting with Python would be the best thing.
Learning and preparing yourself for a data scientist career can be daunting. This is why picking a course that includes intensive hands on training in all 4 of the above will help make you job ready. Go to Big data course for more details on one of the best courses in Big data in the training industry.