What is data science? Data Science is the science of methods for analyzing data and extracting valuable information and knowledge from them. It overlaps closely with areas such as Machine Learning and Cognitive Science and, of course, Big Data technologies.
There are three types of competency model.
This single format of project work requires a combination of rather specific competencies. This is, for example, a job profile for a junior, intermediate and senior scientist at ELEKS.
We divide the skills that a scientist should possess into three blocks: Business, Logic and Technology:
– Business block – skills of communication with a customer, identification of requirements, formation of a vision of a solution.
– The Logic block is the main workhorse: statistics, machine learning, artificial intelligence.
– Technology block – the skills required to implement the model as a complete component. As a component, we usually make a microservice with RESTful interfaces, “wrap” it in a Docker container and give it to the customer in this form.
Benefit: Access to the skills that are in short supply
Data Science Outsource companies can mitigate the resulting deficit by offering clients their services in these areas.
“With the continued growth of info, you can’t count on traditional centers to help you deal with this,” said Ring. “The growing demand for big data management in the cloud is driving the continued development of Amazon Web Services, Microsoft Azure, and the Google Cloud Platform.”
Multiple categories of data scientists
Just as there are several categories of statisticians (biological statisticians, general statisticians, econometrics, operations research, insurance mathematics) or business analysts (analysts specializing in marketing, products, finance, etc.), we can talk about different categories of scientists, who have:
- The strong point is statistics. Sometimes they develop new statistical theories for big data that regular statisticians don’t even know about. They are experts in a field covering the following disciplines: statistical modeling, experiment planning, sampling, clustering, data preprocessing, calculating confidence intervals, testing, modeling, predictive modeling, and others.
- The strong point is mathematics. Professionals in NSA (National Security Agency) or defense companies, astronomers, operations research specialists, who are engaged in analytical business optimization (inventory management and forecasting, pricing optimization, supply chain, quality control, profitability improvement) by collecting , analyzing data and extracting useful information from them.
- Strengths – Hadoop, optimization and architecture of bases, memory, file systems, API (application programming interfaces), analytics as a service (Analytics as a Service); optimization of data flows and infrastructure.
- Strengths – Business; optimization of return on investment (ROI); decision theory; participation in solving some of the tasks traditionally included in the responsibilities of business analysts in larger companies (designing dashboards, choosing metrics, high-level database design).
- Strong point – software development (know several programming languages).
- The strong point is visualization.
- Strengths: Geographic information systems, data modeling using graphs, graph databases.