The operation of every organization relies on information and data that determine key decisions. Apart from collecting and storing them, these data are subject to processing and subsequent analysis. Professionalism and knowledge of data analysis determines the value of the results. Data scientist is a profession with a future that brings tangible benefits to organizations. Thanks to the unique skills of the analyst, the company has a chance to increase its profitability and strengthen its competitive advantage in the market. What is a data scientist? Read on to find out more.
Data scientist – table of contents:
- What is a data scientist?
- Data scientist – skills and requirements
- Data scientist’s area of expertise
- How to become a data scientist?
What is a data scientist?
A data scientist is a person who collects, processes and analyzes data based on machine learning and learning algorithms. In their work they use research methods, mathematics, economics and statistics to achieve the desired business value in the areas under study. Data scientist is a profession that meets the market’s expectations in terms of big data processing. It simultaneously combines various roles from machine learning, through performance and planning issues, to the implementation of proposed solutions.
Data scientist is both a great programmer, a statistician dissecting algorithms on a cluster and a person who knows the mechanics of business with high communication skills. What distinguishes data scientists from data analysts working on standard collections is that they work in an unstable environment of data growing in real time, which is why they are often referred to as data masters.
Their goal is to create visualizations of these analyses, explore any data, define new variables, and analyze deep data. What is more, it’s up to them to select research methodologies that will verify the set hypothesis and then translate it into a business concept that will meet a predetermined goal in the development of the company. An effective data scientist is a person who has above-average programming skills (with a hacker’s streak) and above-average knowledge of statistics.
Data scientist – skills and requirements
The profession of data scientist requires numerous and varied skills from different fields and specialties. When dealing with data science, one should be mathematically and analytically skilled, be a good programmer, be able to present the analyzed data and draw firm conclusions. In addition, a person working in this profession should be meticulous, accurate, patient, have the ability to tell a story through data, and have business intuition. Key competencies:
- Mathematics and statistics – statistical data analysis, machine learning, data mining, distributed algorithms
- Programming – big data technologies, statistical packages, libraries and tools regarding machine learning, Python language
- Industry knowledge – understanding the business objective and linking it to relevant data, ability to present a problem based on data, ability to collaborate with experts
- Communication skills – ability to present data, discuss the problem, propose solutions, ability to discuss and collaborate with the group
- Intuition and inquisitiveness – in relation to the processed data and feasible methods of investigation, and in assessing the correlation of causes and effects
Data scientist’s area of expertise
Data analysis is present in virtually every field and industry. The key areas that a data scientist deals with are:
- Financial and banking sector – analyzing data about banking transactions, supporting credit decisions, detecting fraud
- Marketing – analyzing user behavior on websites, creating recommendation systems, tracking brand visibility and opinions
- Sales – analyzing sales data, predicting trends, segmenting customers, adjusting product offerings to meet customer requirements
How to become a data scientist?
Data scientist is a relatively new profession, which has especially evolved in recent years. When thinking about working in this profession, there are two educational paths. The first is for people who, after graduating from high school, already know that they want to choose this profession.
Studying one of the subjects related to data science, big data or data analytics may turn out to be the best, though not the shortest path to the profession. Studying both undergraduate, graduate, engineering and postgraduate programs is undoubtedly a good direction in starting a career as a data scientist. They guarantee a holistic, broad and diverse approach to this multidisciplinary field.
Another option for graduates of mathematics, computer science, economics or other related studies is to complete specialized courses. A very wide range of different types of training courses are available on the market, covering primarily knowledge of programming and the use of databases.
These training courses are tailored to the individual needs of the participants. These include bootcamps, traditional workshops, online courses, hackathons and challenges. It is important to earn a certificate that will prove the acquired skills and qualifications. During recruitment, a certificate can be a great asset.
Working as a data scientist is an ideal development path for people who are fascinated with databases, statistics and programming, and for those who like challenges and are not afraid of out-of-the-box solutions. According to data from 2020, it was the third best-paid profession in the IT market in the United States with earnings of $107,000 a year. In the UK, on the other hand, a data master can earn more than £80k a year.
The key to success as a data scientist is to understand that data science is primarily about being able to answer business questions, rather than the very essence of the tools used. It is more important to learn the concepts than to learn the syntax. Creating projects and developing new solutions is the main goal of a data scientist’s work. This is certainly a profession of the future that will create innovative business solutions.
Read also:The basics of data storytelling.