Big Data Specialist – Who Is This?
Of course, it is difficult for one person to know everything at all, so we most often work in teams – this is much more productive. For example, a colleague of mine is only a data visualization and storytelling specialist. She’s an amazing infographic that can tell any story in numbers. The main thing is to have a 360-degree angle of view, which comes with experience. It took me almost 15 years to do this myself.
What is the best background to have if you want to work with big data?
There are many different roles in data analysis consulting services: for example, you can be a Big Data Engineer (that is, an engineer) or an analyst, and these are completely different functions. Basic things are knowledge of mathematics, statistics and computer science.
Describe the main stages of the work of a Big Data specialist?
We work in different areas: finance, retail, legal industries. One of the most important roles. Moreover, it is sometimes very difficult to understand what exactly the problem with the data in the company is and how to solve it.
The stages of work on a task for data scientists from different fields are similar:
- Clarification of customer requirements;
- Solving the fundamental question “Is it advisable to solve the problem using machine learning methods?”;
- Preparation of data, their markup;
- Adoption of metrics for evaluating the effectiveness of the model;
- Developing and training a machine learning model;
The next step is making a list. We are discussing the future strategy of the company. The implementation of Big Data is not just about hiring one specialist; it is a change in the mindset of all employees.
It is very important that everyone understands what the guy who calls himself a Big Data specialist is doing. It is very important to dispel the myth that Big Data is just some part of the IT department. After defining the strategy, we suggest ways to implement it.
What is a Data Science Specialist?
A dataset processes data arrays, finds new connections and patterns in them using machine learning algorithms, and builds models. A model is an algorithm that can be used to solve business problems.
For example, in Yandex.Taxi, models predict demand, select the best route, and control driver fatigue. As a result, the cost of the trip goes down and the quality goes up. In banks, models help to make more accurate decisions about issuing a loan, in insurance companies – they assess the likelihood of an insured event, in online commerce – they increase the conversion of marketing offers.
Global search engines, recommendation services, voice assistants, autonomous trains and cars, facial recognition services – all of this was created with the participation of data scientists.
The importance of natural intelligence
Business automation and the widespread adoption of artificial intelligence will not be complete without investment in building IT teams or DevOps teams that constantly monitor the activity of neural networks, identify their incorrect or suboptimal behavior, and retrain models.
Moreover, it will not be so much universal IT specialists that will be important as professionals in specific industries. This is due to the fact that it is impossible to transfer neural networks from one industry to another.
The neural network packs the decision tree so tightly that even a small deviation can lead to an incorrect result. Therefore, applied specialists who are versed in narrow industries will be in demand.