The Path From Business Analyst To Data Scientist Hasn’t Got To Be Labyrinthine
The boundaries delineating business analyst and data scientist is sometimes not lucid. The employees who are hired as a business analyst are supposed to work as a data scientist and vice versa. It isn’t easy playing the roles of the two difficult positions simultaneously.
Armed with the top big data certifications can ease the path anyway. A business analyst can comfortably transition to become a data scientist. They have competitive advantages over other positions to be in the shoes of a data scientist. For example,
- They are armed with the knowledge of the industry they work in. The industry-knowledge is crucial to understand the industry and analyze it.
- Spreadsheets and other tools of database are at the tips of their fingers
- They have great communication skill with which they dealwith the clients. Making managers or other laypeople about what that representsand can how it can help them achieve profits is part of the game you will be playing as a data scientist.
The business analystsrelyheavily on the industry-knowledge and their power of intuition to judge the future of the industry based on this knowledge. The shift in the talent acquisition process from hiring business analysts to data scientist hasits underpinnings in the same weaknesses of the business analyst profession. The businesses no longer want to rely on the intuition power of the humans but on the machine language and dataanalytics to crunch numbers and come up with a prediction of future which looks more authentic.
Speaking about the data scientists, they are experts in systems engineering, statistics, and computer programming they can evaluate data and present cold hard data which is irrefutable for anyone. This provides an upper hand to the data scientists. Another shortcoming of the business analysts is their dependency on what went right and what wrong in the past and how it can be used to better the future. The data scientists, on the other hand, are massively dependent on the future model of the data.
When you have finally decided that a certification in big data is all you need to make that transition, please pay attention to these pointers:
- The top big data certifications are difficult to understand if it has been a while since you have done your majors on statistics and you may have forgotten a step or two. A refresher course before you start doing a full-orientation certification in big data analytics.
- Machine learning is the engine behind the machine of data engine. Get yourself a fast-paced certification in big data or a crash course.
- The data scientists who can create own algorithms and systems are high in demand and that makes learning a coding language indispensable. Add that to the portfolio as an icing on the top big data certifications cake.
- No certification will be able to present your case better than the experience you earn developing a system in your spare time.