Introduction to Data Science: What Is It and What Does a Data Scientist Do?








Throughout history, different cultures around the world have told us about beings that emerged to impose order in a universe of chaos. Entities carrying the most varied knowledge, ready to shed light and guidance in a space extremely difficult to conceive and comprehend, converting it, structuring it, refining it and giving it meaning. This foundational work has been critical so that all the components of reality have the necessary certainties and a direction to move toward.


Thousands of years later, history repeats itself once again. The super-charged technological development has once again put us in a similar situation. Today, the universe is digital and that chaos is the more than 60 trillion gigabytes that are out there waiting to be interpreted, organized, understood and refined to provide new directions to aim for in the multiple fields of knowledge. Fortunately, as happened back then, new beings have also appeared ready to take on that titanic mission. We're talking about the Data Scientists.











In this new Coder Blog we're going to make an introduction to Data Science, the discipline that seeks to provide certainties through the exhaustive analysis of Big Data, understanding what it's about, how it works, what it's for, its differences with other similar disciplines, who its protagonists are and how we can get into this world.


Let's start diving into this methodology that is increasingly making its mark when it comes to shaping the future ahead!








1.1 Emergence and History of Data Science


The history of data analysis dates back to the early 90s and has gone through different, very distinct stages, just as can be observed in depth in our article on Data Analytics.


The Data Science methodology arises in the context of the new information era, the fourth industrial revolution and the so-called digital transformation, which involves the evolution and emergence of new business models, incorporating the digitization of files and the digital world into all business areas.


We can say without a doubt that in recent years we've experienced a true explosion in the use of computer systems by the world's population. We're increasingly interacting more with systems, new technologies, social networks, e-commerces, platforms and applications, generating a lot of data involuntarily with our simple interaction and voluntarily when we make registrations, purchases or subscriptions. Likewise, our cities are becoming smart, as we equip them with sensors that measure different events and the environment.


The increase in sensors and in people using smart systems has resulted in an explosive growth of data, generating each year 90% more data than the previous year. Just as we could see in the opening chart, it's forecast that by the year 2026 there will be 175 trillion gigabytes in data (Big Data). These records store extremely useful knowledge that can be extracted and that companies are taking advantage of strategically to obtain differential advantages over the competition. This is why disciplines like data science have emerged, in charge of carrying out this crucial mission given the great difficulty of analyzing these monstrous volumes of records.


So, what is Data Science?








1.2 What is Data Science?


We can define Data Science or data science as the methodology in charge of collecting, processing, analyzing and refining enormous volumes of structured and unstructured data in order to obtain information, certainties and knowledge. These conclusions will allow the different areas of an organization to make decisions such as the launch of new products, the optimization of existing products, the expansion of operations, the tracking of trends, among many others.


Data Science is an interdisciplinary field that involves scientific methods, processes and systems to extract knowledge or a better understanding of data in its different forms. Among its ranks we can find professionals specialized in:


Scientific method.Data engineering.Mathematics.Statistics.Programming.Advanced computing.Information visualization.Hacking.Domain Knowledge.


As we mentioned in the first paragraphs of this Introduction to Data Science, this is an area that gains more strength every day because of the context we currently live in. This very thing makes it increasingly in demand by companies in all fields since they allow services to be improved and sales to be increased.





Get to know our Data Science courses and career tracks by clicking the photo:











1.3 How does Data Science work?


When we ask ourselves how to implement Data Science we have to take into account the process that the following methodology entails.








This is how the Data Science process works. Source: OpenWebinars





Making a brief description of the different stages this complex insight-discovery process takes, we can say that everything begins with the collection of raw data that comes from reality. This data can come from the company itself, from its digital platforms and from information open to the public.








Data is obtained from diverse sources.





Once all the necessary data is collected, the Data Scientists set out to process and clean it so it's useful when creating models in the later stages of the process. The untreated data is refined in order to eliminate that which is unnecessary or that can generate errors. Likewise, this type of action is also carried out to facilitate the later analysis of the records.


Once they've been processed and cleaned, the data is treated through exploratory analyses, where different variables are used and an attempt is made to understand the data before starting to create models.











Finally the moment comes to create models and algorithms with which the data finally ends up being converted into valuable information that is communicated to the different areas of an organization through highly visual reports.











The enormous set of disciplines applied in this process such as mathematics, statistics, programming, communication and business intelligence complement each other to add value with information, preventing organizations from being guided by instinct or theories. Data lets you obtain conclusions and helps people decide based on information they can obtain through dashboards, code, ETL pipelines, reports, dataviz, artificial intelligence or machine learning models.








1.4 What is Data Science for?


Recapping on the concepts analyzed in this article and providing even more applications of this methodology, we can say that Data Science serves many purposes like:





Manipulate and give practical usefulness to Big Data.


Obtain, process, refine, analyze and model data to convert it into valuable information for decision-making.


It lets you create new products, optimize existing ones, follow trends and bring expansion plans down to earth.


It significantly reduces impulsive decisions, instinctive or based on unproven theories. 





Currently, Data Science is being applied to the world's most important industries in diverse ways.





Banking Industry: Important advances have been made regarding cybersecurity, detecting atypical patterns and strange behaviors that have prevented a large number of frauds. Likewise, this methodology has also been used to perfect the calculation of insurance premiums and loans.


Medical Industry: Data Science has been used crucially in the medical industry to accelerate the testing processes of vaccines against SARS-COV-2. Likewise, new products have also been created such as image recognition for the detection of diseases and tumors.


Technology Industry: Data Science has made possible image-based searches through search engines like Google.


Automotive Industry: Crucial innovations for road safety such as automatic driving have been achieved.


Marketing: Data Science allows an extremely deep segmentation and classification of customers that lets you know what to offer each person depending on their specific needs. Next, we share a documentary that illustrates the advances in this phenomenon.











Now that we've made a first Introduction to Data Science, it's time to analyze its main protagonist: the Data Scientist. In the second part of this article we're going to understand what a Data Scientist is, what they do and the roles they take on. 





2.1 What is a Data Scientist?


A Data Scientist is the person on a team in charge of obtaining truths and reaching conclusions through the massive analysis of data, to allow the organization they belong to to make better decisions as a whole.


Being a Data Scientist involves a large number of factors: 


First, it's crucial that these professionals have a vast understanding of the business in which they work. It's very important for these individuals to understand what the company sells, how the consumer looks for the business or service the organization offers and how that product is brought to market. Having these basic notions helps professionals understand what data should be stored and what shouldn't.


Second, it's very important that these professionals know how to effectively acquire that data, understanding what data patterns can be obtained from users and how that data can be obtained. Knowing how to acquire data also involves understanding how to refine it so that it's useful. It's important to understand that, if we input garbage data into a Data Science flow, we'll obtain deficient information as a result that won't allow us to make sound decisions. It's crucial to consider the backing that the data source has, the journey of the data and the update frequency of those records before thinking about how that data can be refined and organized in order to work on it.


In a third instance it's critical that the professional knows how to choose the most accurate mathematical models to interpret the data. These models are algebraic or statistical. This modeling lets the Data Scientist understand different competencies of the business such as why users subscribe, which product should be manufactured more, what type of attention should be dedicated to users, etc. From this work, the data scientist will be able to start making predictions.


In a fourth and final phase we find a fundamental requirement: knowing how to communicate the insights obtained to the different areas of the company. At this point it's important to be clear and to be able to translate the results into the language of each area. Tools like storytelling are extremely useful for performing this work correctly.








2.2 What does a Data Scientist do?


Having understood what a Data Scientist is, we can say that this profile is in charge of:





Carrying out the Data Science process.


Correcting and adjusting the Data Science process and finding new ways to acquire data.


Monitoring the Data Science process constantly.


Analyzing in depth the databases that companies create to obtain findings and identify opportunities.


Interpreting the results obtained so that everyone can understand them.


Communicating Insights and opportunities to the sales teams.


Analyzing information from different sources and transmitting it to different layers of the company through charts.


Predicting trends such as, for example, how much a home will sell for or understanding whether a patient will have a disease.


Analyzing data statistically.


Systematizing the data.


Perfecting Machine Learning processes.





Now that we understand what a Data Scientist is and what functions they fulfill, it's time to analyze the differences between this discipline and others that interact with data.








3.1 Difference between Data Science and Business Intelligence


Data Science and Business Intelligence are two concepts that are often confused since both work with data; however, there are notable differences between both disciplines.


On one hand, we have to consider that Data Science works with incomplete and often disorganized data, while Business Intelligence does so through complete and extremely refined data.


Another key difference between both concepts is that Data Science analyzes this data to discover what information it obtains while Business Intelligence reports what this data says.


On a third level we can appreciate that Data Science works with enormous volumes of data while BI works with manageable data sets.


Finally there's a marked difference of purposes, since DS generates findings that drive future decisions while BI's findings measure past performance.








3.2 Difference between Data Science and Data Analytics


On many occasions the concepts of Data Analytics and Data Science tend to be confused since these work closely together; however, there are big differences between both methods. The difference between Data Analytics and Data Science lies mainly in the function of each one. While a data scientist makes predictions based on patterns from the past, the data analyst extracts vital information from a business's historical data. 


Another important difference is that while the Data Scientist is concerned with asking questions, the Data Analyst is concerned with answering questions. Likewise, a scientist will turn to extracting information from various sources, while the analyst will generally do so from just one. Regarding this point, it's important to mention that the Scientist uses Machine Learning to obtain information, while the Analyst turns to programming languages like Python.


Regarding the field of application of their knowledge, we can say that the data scientist has greater freedom of action while an analyst is in charge of applying it exclusively to solving business problems.








3.3 Difference between Data Science and Data Mining


Another usual confusion within this area occurs between the concepts of Data Science and Data Mining.


While Data Science is a global methodology, Data Mining is a discipline within that world that involves techniques and technologies applied for the exploration of large databases in an automated and semi-automated way, with the mission of obtaining repetitive patterns that explain the behavior of the data.








3.4 Difference between Data Science and Data Engineering


Very often a confusion also tends to occur between the concepts of Data Science and Data Engineering which, although they tend to be complementary, this doesn't make them similar.


While the Data Scientist is the one in charge of refining, processing and organizing large volumes of data, the Data Engineer is the one in charge of developing, building, testing and maintaining architectures like databases and large-scale processing systems, which are the ones that data scientists later use to carry out their tasks.


Data Engineering involves much more advanced knowledge in programming, but applied to building processing tools, while Data Science uses programming languages to obtain and process data.


An example of the difference between these concepts and their complementary way of working is in the fact that data engineers recommend and implement ways of optimizing the data so that it's more useful in the later analysis by data scientists.








3.5 Difference between Data Science and Computer Science


A final focus of confusion also exists when comparing the concepts of Data Science and Computer Science. These are the main differences between both roles:


While data scientists focus more on machine learning algorithms, computer scientists focus on software design.


Another point of comparison between both roles is that the education they receive is different. While a computer scientist has a degree in computer or information science, data scientists usually have greater statistical training.


Finally, another key point of comparison is that computer scientists are more focused on systems while data scientists tend to be more focused on the business.





Now that we understand the difference between Data Science and other professions that work around data, it's time to understand what's needed to be a Data Scientist, what their learning path is, and why and where you can study it.








4.1 What do I need to be a Data Scientist?


To be able to work as a Data Scientist, we must acquire a series of knowledge and meet some requirements that will turn us into extremely competent and complete professionals in this field. 





Among this knowledge we can highlight Hard Skills like:





Advanced knowledge in descriptive and inferential Statistics.


Advanced knowledge in Mathematics.


Advanced knowledge in Programming: Python, R and/or Ruby.


Advanced knowledge in SQL or other database query languages.


Knowledge of predictive modeling.


Basic knowledge of Machine Learning, Big Data and Text Mining.


Basic knowledge of business analysis, project management, strategy and finance that are key to being able to converse in management environments.


Basic knowledge of intelligent conversational systems (Chatbots) and time series.


Advanced English.





Likewise, it's also necessary to develop Soft Skills like:





Ease of communication. This is extremely important when it comes to turning immense databases into insights that are easy to understand.


Ease for the interpretation and visualization of data. 


Practice and experience.


Analytical thinking and insatiable curiosity.


Knowing how to work and relate with a team.


Ease of learning and willingness to face the changes of a field that is constantly evolving.





Finally, we can also highlight added values like:





Knowledge of the market of the company in which they work.








4.2 How to learn Data Science? | Data Science learning path


Like every specialty, Data Science has an optimal learning path that can be followed to turn us into extremely competent specialists in this field. We share a practical summary of this learning path:














4.3 Why study Data Science?


As we've seen throughout this article, Data Science is an extremely thriving specialty and increasingly in demand worldwide. Beyond this, there are a large number of reasons that can lead us to study Data Science:





Each year a greater amount of data will need to be processed, so a greater number of people dedicated to this mission will be needed. In the coming years millions of positions will be created in the Data Science areas that companies will have difficulty filling.


The world is quickly heading toward this type of job, which can give us greater opportunities in the future.


Companies are starting to value the work of a data scientist more and more, which leads to great salaries for their employees.


It's an extremely creative job that is in continuous evolution.


Weekly, new discoveries emerge in this area, which makes it extremely interesting.


It's extremely attractive for people who are fans of computing since it comprises algorithms, mathematics, statistics, machine learning and research.


It lets you obtain knowledge from different fields with which you can solve multiple problems of different kinds.








4.4 Conclusion and Where to study Data Science


As we could appreciate throughout this article, Data Science has become that fundamental guide that leads organizations to make great decisions. Decisions based on data, on facts and on concrete events that take impulsive, instinctive or theory-based actions off the discussion table.


This methodology will keep becoming a reference for more and more companies in the future, allowing them to generate products and services that meet needs in a more optimal way and give a true benchmark for generating better expansions of operations.


If you want to always stay up to date on the new trends that emerge we recommend subscribing to the Coder Blog by scrolling down, and if you want to learn alongside the best professionals in the industry we invite you to discover the Data Scientist career that Coderhouse has for you.

If you'd like to keep exploring this topic, you can also read Power BI vs Tableau: which to choose based on your profile and goals.

Recommended Coderhouse courses

If you want to go deeper into data analysis and applied artificial intelligence, Coderhouse has programs for all levels:

• Data Analytics Course: to learn to analyze data, build dashboards and make decisions based on real information.

• AI Engineering Course: to incorporate AI and machine learning models into your data projects.

• Introduction to Artificial Intelligence Course: to understand the AI ecosystem and complement your data profile with knowledge about language models.