Data Scientist is one of the most demanded jobs of the century. The jobs of data scientists consist of revolutionary, intricate and impactful learning. It is a very broad career opportunity. It means different things are required for different companies. Every company requires different skill sets. The skill set developed at one company is not sufficient to carry out work for the entire career. It is a disciplinary job. The scientists should have knowledge of programming, statistics, mathematics, business understanding, and many more. The field is evolving at a fast speed.
The job is about defining and solving business problems. Mathematics and coding are important skills for having a good career in data science. It is necessary to know all the programming languages. The candidate should have good communication skills and should gel with the team members. They should know about SQL, Social mining, Microsoft Excel and need data and business savvy. The people working in a company focus on current fashionable techniques. They should have deep learning rather than having a detailed knowledge about the foundation.
The job of yours is to communicate and educate the co - workers and stakeholders in a desired manner which is digestible to them. It is also required to break down the data science projects into various steps. Business stakeholders care about progress and they want to see how a project is improving over time. The main responsibility is to communicate your progress and your outcome.
People get into the job for the excitement it provides. In various companies you will have to spread the time between technical work and the other known stuff. The students who belong from education or research background often fall into the trap of infinite timescale and infinite budget mindset. Data Scientists can not set a timeline for the work they do. They either have to fix the scope of what they are trying to get and can vary the timescale.
Data Scientists spend their maximum time in pre- processing data to make it consistent before it is to analyse instead of building meaningful models. It is much messier. The task involves cleaning the data, removing outliers, encoding variables etc. The worst part of opting this career is data pre - processing, which is crucial because models are built on clean, high- quality data.