Discussion about the important role of the Data Scientist dates back to May 2017 when, on the cover of The Economist, data was referred to as the “most valuable resource in the world”.
On that occasion, just above the title, some online giants like Facebook, Google, and Amazon were depicted as oil platforms. The message was clear and straightforward: data is the new oil.
Two years later, the data scientist is among the most sought-after professional figures ever, and in this article, you will discover:
- The definition of a data scientist and its debut in the job market.
- Who they are and what do they do with data for companies?
- The key skills required to perform this job.
- How can one embark on a career as a data scientist?
- The sectors in which they operate and how much a data scientist is paid.
So, if you are passionate about numbers, science, and data analysis, I assure you that this article will guide you in this professional world.
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The birth of the Data Scientist role
Until a few years ago, the analyst was the professional figure predisposed to Data Science. But what is data science and what does it do?
It is the analysis and interpretation of data in a company. The context and technologies of that time were very different from today: the amount of data to be analyzed was limited, allowing analysts to rely only on simple statistical tools.
The advent of Web 2.0 with the impetuous spread of the Internet and the birth of Facebook inevitably changed the game, dramatically increasing the volume of data.
For every Facebook like, every tweet, and every time an app is consulted on your smartphone, traces remain on the web: personal information.
If you think about the time each of us spends on the internet every day, you won’t be surprised to know that the amount of data is estimated to continue increasing!
The formats of this data are also of different types, further complicating matters. In this regard, it can be safely stated that companies have long been familiar with structured data.
For example, the management of CRM, where information is stored in a classic database, is certainly not a novelty.
On the contrary, it cannot be easily asserted that the same familiarity exists regarding unstructured data, meaning information is stored without any rigid schema or table.
These are not stored in common databases: it is up to the company to analyze these free-form texts and extract meaning from them.
The immense volume of data and their variety in terms of format has made manual analysis impractical, rendering obsolete the statistical tools previously used.
Therefore, in a context where Big Data reigns supreme, and companies increasingly rely on Machine Learning techniques, data science seems to be the most logical answer.
By this term, we mean the young and interdisciplinary field that relies on techniques from mathematics, statistics, and computer science to extract information from data, regardless of their size or shape.
And so, the role of the data scientist is born, nowadays essential for companies operating in sectors where Big Data plays a primary role.
With the term data scientist, we refer to the professional figure whose presence is necessary to extract value from every piece of data and consequently make more informed and targeted business decisions.
In short, in certain sectors, the data scientist is a fundamental element if the company wants to compete with its rivals. Remember: the data itself has no value if it is not analyzed, interpreted, and understood.
The individual piece of data represents a cost for the company, but the information derived from the comprehensive analysis of the viewed data represents an opportunity for monetization, and the meaning of a data scientist lies precisely in the ability to convert these elements for benefits.
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Who is a Data Scientist and what do they do?
Defining the data scientist as a highly specialized professional figure dealing with the management of Big Data would be limited.
The data scientist brings value through data analysis, and this makes a huge difference. Using specially designed software, they can extract relevant and meaningful information from a vast amount of data for the company.
These pieces of information, which were previously hidden in the data and have been made understandable by the professional, are crucial for strategic business actions.
What do we mean by “information”? Essentially, we refer to anything that can be useful to the company in achieving its goals: trends, opportunities, and developments.
For example, through the data scientist’s analysis, a company may find that a product missing from its e-commerce product catalog is something customers expect to find in its online store.
Or maybe an email marketing campaign has a higher open rate when scheduled at specific times of the day.
The work of the data scientist can also be useful to the company in better specifying its market niche or in segmenting its target audience.
To further clarify what a data scientist does in practice, let’s imagine their work as a process and examine each specific step.
The Data Scientist’s work step-by-step
Embark on the journey of data exploration as we delve into the fundamental steps of data analysis.
From defining the problem at its core to crafting insightful presentations, join us in understanding the intricate phases:
- Problem Definition: this is typically the starting point of every data analysis, and it’s important to emphasize that, especially in this phase, the data scientist doesn’t work alone. For their work to produce the expected results, they must have a deep understanding of the dynamics and operation of the company. Additionally, they must be able to engage with company executives and managers to thoroughly understand what they want to achieve from the data analysis they have commissioned.
- Data Collection: the data scientist may draw from various sources. They might refer to company databases, such as ERP or CRM software, data collected from social media, or data intercepted by web analytics tools or other databases available to the company.
- Data Processing: not all available data is needed to solve a problem. That’s why it’s necessary to consider only the useful ones, eliminating any errors or unnecessary information. In other words, a skilled data scientist must transform the available data into organized and accessible formats, ready for use.
- Model Creation: to identify relationships in the data (data mining), an expert in data science uses software for data analysis, algorithms, statistical methods, and machine learning tools.
- Results Presentation: the final phase. The information extracted by the data scientist throughout the entire process needs to be communicated and made understandable to relevant managers, using presentation tools such as Google Data Studio, for example. The data scientist’s task continues with the creation of reports, using graphs, tables, maps, diagrams, and a lot of creativity!
Difference between a Data Scientist and a Data Analyst
While these two terms may seem synonymous, it is necessary to underline that they refer to two different professional categories. We could say that the data analyst is a kind of predecessor to the data scientist.
While the former is limited to solving specific problems using traditional statistical tools, the latter processes data breaks it down, and interprets it.
There is often confusion with the definition of the term data engineer as well, who designs the systems within which data is collected for the data scientist to analyze.
The expert data scientist resorts to traditional statistics, but not only. All to discover opportunities, critical points, and trends that may be of interest to the company and those who define its strategies.
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Attitudes and Skills of a Data Scientist
Because the data scientist employs a holistic approach, they must possess a series of cross-functional and diverse skills. Their work is distinctly multidisciplinary, and therefore, they must be experts in various fields.
Below are the essential disciplines for building a career in the field:
- Computer Science: the data scientist knows programming languages and can develop and implement machine learning algorithms.
- Mathematics, Statistics, and Analysis.
- Economics and Management: don’t just see it as a mere computer science role because the data scientist must also have a deep understanding of the market and the business in which they work.
Furthermore, it is essential for such a professional to be predisposed to:
- Communication. The work of a data scientist doesn’t end with data analysis but continues with visualization. You can be among the best data scientists in the world, but if you can’t communicate results with beautiful, understandable, and quickly consultable reports, your work will be worth little!
How to become a Data Scientist?
Since the skills of a successful data scientist are varied, a transversal type of education is required. So, what degree is needed for a data scientist? In reality, it is not necessary to attend a specific training course.
Generally, however, it can be said that when talking about a data professional or Data Science Manager, reference is made to individuals with a natural predisposition for numbers.
My educational background of most is statistical and mathematical, computer science, and engineering, but also economics.
There are not many data scientists without a degree; in fact, the most qualified usually have at least a bachelor’s degree, often a master’s, and in some cases even a doctorate.
However, traditional degrees do not offer the interdisciplinary approach required in the workplace for this profession. Those who want to embark on this path often attend data science courses and data analysis in addition to and external to the university path.
The training of a data scientist also never ends. They need to stay constantly updated to keep up with the incessant evolution of the software and technologies they work with every day.
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Fields in which Data Scientists work and average salary
I have already emphasized how the volume of data that companies have to manage increases year by year. Consequently, a well-trained and prepared data scientist has a clear path, and a thousand doors open before them.
However, there are sectors where this role is more in demand, and some of them are easily predictable. For example, consider the case of e-commerce, where a myriad of elements on consumer purchases is collected every day.
Here, the data scientist is indispensable because they will be able to communicate any necessary changes to those responsible for the website content. This will result in improved customer service.
Another fairly obvious example is the case of a company that heavily relies on social media for its marketing strategy. The data scientist in digital marketing is crucial in this regard for precise targeting of advertising campaigns.
Even more traditional sectors increasingly need data scientists. Think about finance and the immense amount of data on accounts, credit and debit operations, and transactions collected every day.
Or, in the insurance or healthcare sector, where this profession is very helpful both in prevention and in structuring more targeted care possible.
So what do you think? Do you believe you have what it takes to become a data scientist?
Consider that a few years ago, the Harvard Business Review defined this profession as the sexiest of the twenty-first century! This title comes from the enormous opportunities associated with the profession.
If you’re wondering how much a data scientist earns on average, a Junior Data Scientist starts with a salary of about 30K gross per year, which increases with experience, reaching over 100K per year for a Senior Data Scientist.
If you think you have all the cards on the table, start studying and specialize in data science. I can’t tell you exactly how many data scientists there are, but there are certainly plenty of job opportunities for data scientists, even freelance!
If you’re interested in the topics covered in this article, fascinated by the magical world of numbers and data analysis, and want to know where to study data science to become a professional in the field, I recommend the following courses: Web Analytics Base and Web Analytics Advanced.
Conclusion and free consultation
In conclusion, you will have understood that it is not difficult to be a data scientist if you have the qualities and initiative necessary to learn this discipline and dive headfirst into the world of data.
So don’t waste any more time and book your Free Career Consultation with the experts at Digital Coach, who will work with you to create your ideal study plan based on your needs.
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Hello there! I’m passionate about a variety of interests, including playing badminton, diving into video games, indulging in movies and series, and exploring new destinations while listening to my favorite tunes. I thrive on learning, constantly seeking out new knowledge and experiences. My professional journey started in a call center, but I’ve transitioned into freelancing, dedicating myself to continuous improvement and striving to be the best at what I do.