What do you know about data science? Educating yourself on the topic is a great way to better understand the complexity of the world we live in today. There is not the slightest possibility of comparing the amount of information we have access to today with what humanity had available decades ago.
It’s what we call the Information Age.
The problem is that we’re talking about so much information that exceeds the processing capacity of human beings to collect and organize all this data, only with the help of technology.
But machines don’t do it all on their own.
They are the people who set guidelines, establish criteria, and can handle the technological solutions. In addition, of course, to transform all this information into useful content, into action, and into results.
Within this scenario, the data scientist is a fundamental figure.
In this text, we will present a good overview of this professional’s area of expertise.
You’ll learn about:
- What is data science?
- What is the difference between data science and statistical analysis?
- What is data science used for?
- Descriptive analytics
- Diagnostic analysis
- Predictive analytics
- Prescriptive analytics
- What is the importance of data science?
- Data science in business
- Benefits of Data Science in Businesses
- Challenges of Implementing Data Science
- How does the data science process work?
- O (Get Data)
- S (Suppress Data)
- E (Explore Data)
- M (Model Data)
- N (Interpret Results)
- Key Applications of Data Science
- Data Science and Big Data
- The Professional Practice of a Data Scientist
- Profile of a Data Scientist
- What is the Salary of a Data Scientist?
- What are the main duties of a data scientist?
- How does the job market work for a data scientist?
- Data Science at FIA.
Follow along until the end to learn what data science is in practice and use on a daily basis!
What Is Data Science?
Data Science is an interdisciplinary discipline that uses methods and techniques from Statistics, Mathematics, and programming to extract knowledge from large data sets.
A piece of data is the lowest level of abstraction of a piece of information, a raw form of knowledge that has not yet been properly addressed to actually provide some insight. It can be in the most different formats, such as texts, numbers, images, audio, or video.
Data sources are also plentiful. When we access the internet through our smartphone or laptop, all our movements leave virtual traces about our consumption preferences. Everything that is posted, clicked, and searched on the web is recorded and can be analyzed.
By 2025, it is estimated that the amount of data generated in the world will be 175 zettabytes, which is equivalent to 175 trillion gigabytes. The forecast is from IDC’s Data Age 2025 report.
This means that Data Science is an area that is constantly evolving, driven by the growing volume of information available and the need to process and analyze it.
What Is The Difference Between Data Science And Statistical Analysis?
The relationship between data science and statistics is like that between content and continent. This is because statistics is one of the disciplines used to analyze and model data, without necessarily turning to the capture and storage of information.
In other words, data science uses statistics as one of its tools to extract information from raw data in all stages of its processes, it uses advanced Artificial Intelligence, Machine Learning, and Deep Learning algorithms, among other techniques.
Thus, we can say that data analysis is the final part of data science work in practice. In Big Data, data, by itself, doesn’t tell you anything, even though it’s stored, sorted, and segmented in a variety of ways.
What gives them value is the ability to relate them to the reality that one is interested in analyzing, identifying problems and opportunities for a company, for example.
Therefore, it is up to the data analyst to know applied statistics and at least the basics of Machine Learning to perform their duties.
What Is Data Science Used For?
Nowadays, data is so present in a company’s routine that no area is unrelated to it. Data science practices are applied in the manufacturing sectors, as well as sales, marketing, communication, finance, legal, and any area you can imagine.
Whatever the context, there is always a proposed goal, so data science can never lose alignment with the company’s strategy. “Many solutions are generated and the best one is chosen based on initially defined metrics,” explains Alessandra Montini.
Thus, the so-called optimal solution is reached, the one that presents the best performance in a given context.
Data science is a means of finding it and putting it into production.
Here are four types of analysis in which this science applies.
One of the types of analysis that can be done from data is one in which trends are identified based on certain patterns. This is the essence of descriptive analysis, which is not based on hypotheses or theories, but on the observation of what the data shows.
In it, the data scientist is dedicated to organizing and tabulating the data in order to present results based on that. Descriptive analyses are made through calculations to compose and distribute the variables in question, which point to certain types of trends.
A classic example of this type of analysis is the one done to establish statistically significant differences between interest groups.
For example, do women really consume more shampoo than men?
The term diagnosis is formed by the suffix “dia”, which means “by means of” and “gnosticu”, which refers to “the knowledge of something”.
In medicine, diagnosing means knowing a disease by observing its symptoms. In data science, the term is used to designate a type of analysis with which one seeks to find cause and effect relationships for certain phenomena.
Unlike descriptive analysis, in diagnostic analysis, the scientist uses data not only to interpret reality, but also to modify it.
It uses many more probability-based tools and techniques in order to exhaust the possibilities of diagnosis, as medicine also does.
In many cases, the data scientist will have to work harder to eliminate hypotheses than to directly diagnose a problem.
It is very common to have some confusion between predicting the future and what is done in predictive analytics. While they do serve to anticipate what’s to come, that doesn’t mean they know exactly what the future holds.
What makes this type of analysis reliable is that it does not predict events, but rather points to what might happen if certain conditions are met. Companies do this type of analysis a lot when they want to know what to do if a certain competitor enters the market.
In this way, they anticipate possible consequences if this threat actually materializes, thus helping their managers to make better decisions. An important aspect of this type of analysis is that, in it, data science works from correlations, thus being able to point to probabilities.
Statistical tools and resources like machine learning wouldn’t be as useful if they didn’t also guide you on what to do from the results of the analyses.
To this end, the so-called prescriptive analyses are carried out following predictive analyses. The purpose, in this case, is to determine what can be accomplished from the predictions and insights obtained, considering both internal and external factors.
In this type of analysis, solutions based on Artificial Intelligence and Machine Learning are even more necessary, in view of the many possibilities they can indicate. This is because predictions suggest what can happen without considering the objectives at stake, the characteristics of a company, its weaknesses and external risks.
Prescriptive analytics weighs these and other factors in the balance, so as to establish links with other types of analytics to guide the decision-making process.
What Is The Importance Of Data Science?
With so much relevant data at their disposal, a manager has many more subsidies to make their decisions.
It finds reliable and accurate inputs with great agility, unlike when professionals had to use mainly intuition or order time-consuming, costly and not so precise research.
The world is becoming more and more “data driven”, that is, data-driven, so it is no longer possible to make the right decision without analyzing this gigantic volume of information available.
After all, if you don’t, you can be sure that the competition is doing it and thus you will have an important advantage in relation to your business.
Data Science In Business
Change is faster than ever, which requires a new model for each case. The FIA professor notes that, for this reason, companies are no longer accepting to pay millions for ready-made solutions.
Instead, they need to develop data science expertise in-house in practice. It is what allows you to arrive at the best solution based on an intimate knowledge of the business.
Therefore, as we said before, the work of the data scientist is not dissociated from strategic management thinking.
In this way, contrary to what many think, algorithms will not dehumanize the management of companies.
In reality, they allow professionals to occupy their time with even more human tasks, which machines are not capable of performing.
Benefits Of Data Science In Businesses
By 2025, it is estimated that the Big Data market is expected to generate revenues of around $68 billion.
Data science has its work cut out for it, as it is its role to direct how companies use the colossal volume of data available online.
By the way, by 2025, this volume is expected to reach the mark of an incredible 181 zettabytes.
Time has shown that Big Data is indeed a gold mine, which needs to be explored to generate the expected riches.
This exploitation, in turn, not only makes companies more profitable, but also brings a series of advantages, such as:
- Development of Market Intelligence (Business Intelligence)
- Less exposure to external and internal risks
- Attracting more qualified investors
- Increase in the quality of products and services and, consequently, in customer satisfaction
- More engaged employees, thanks to the application of analytics to improve benefits programs and stipulate salaries compatible with their expectations.
Challenges Of Implementing Data Science
The NewVantage Partners Big Data and AI Executive 2019 survey reveals that 92% of companies have increased their investments in Big Data and AI.
Of these, 38% did not get the expected results, at least not in a measurable way.
Converting efforts in dealing with Big Data into tangible results is therefore one of the challenges facing data scientists.
Another obstacle is dealing with the exponential increase in the volume of circulating data, which according to an IDC report, doubles every two years.
In a country like Brazil, where most companies are not digitally mature, many organizations are not even aware of Big Data and what it represents.
In this context, another challenge arises, that of generating insights in time to generate quick results.
This is one of the challenges foreseen in Serasa Experian’s Global Data Management Survey.
How Does The Data Science Process Work?
Every science has its modus operandi – and data science is no different.
The basis of its results comes from the projects, which are structured in a work framework known by the acronym OSEMN.
Let’s see below what each of its terms means.
O (Get Data)
Data science starts with them, of course.
In this step, the scientist queries the available databases, using MySQL for information processing.
Data can be received in different formats, including good old Excel.
For those who master Python or R languages, there are specific packages that can read data from these sources directly into their programs, speeding up the process.
But there are many more databases to work with.
The most commonly used are PostgreSQL, Oracle, or non-relational (NoSQL) databases such as MongoDB.
Another way to get data is to “scrape” websites, using tools like Beautiful Soup.
Connecting to internet APIs is another popular option for collecting data.
Social networks like Facebook and Twitter, for example, allow users to connect to their web servers and access their data via API.
The collection stage requires the data scientist to have knowledge of the tools and solutions to work with Big Data, the most famous being: Apache Hadoop, Spark or Flink.
S (Suppress Data)
In this second step, the data is debugged, which leads to a good part of it being suppressed.
In this process, the data is converted from one format to another, consolidating it into a standardized format.
For example, if your data is stored in multiple CSV files, you need to consolidate it into a single repository so that it can be processed and analyzed.
Data scrubbing also entails extracting and replacing values.
If you notice that there are missing datasets or they may appear to be non-values, this is where they should be replaced.
Another task inherent to this phase is that of splitting, merging, and eliminating columns in Excel files.
For example, in a spreadsheet containing data about the place of origin, you might have columns for “City” and “State.”
Depending on your requirements, you may need to merge or split data of the same nature.
E (Explore Data)
Now it’s time to examine the data before it can be processed with machine learning and AI solutions.
It is where the scientist inspects the data and its properties, considering its characteristics.
Different types of data such as numbers, categories, ordinal and nominal data, for example, require different treatments.
The next step is to compute descriptive statistics to extract characteristics and test variables, through correlation tests.
An example of this type of analysis is the one that measures someone’s risk of having high blood pressure considering their height and weight.
M (Model Data)
In this advanced step, one of the first things to do is to reduce the dimensionality of your dataset, as not all of them are essential to building a model.
Here, the data scientist can work with another very important professional, the data engineer.
Modeling is used to group data so that one can understand the logic behind “clusters,” i.e., groups of data with one or more characteristics in common.
N (Interpret Results)
In the final phase of the data science process, the results are presented, which must be intelligible to lay people.
An essential skill at this stage is the ability to tell a story, so as to arouse some kind of reaction in people.
Therefore, the data scientist must be someone who is able not only to read and interpret data, but also to communicate it with clarity and empathy.
Key Applications Of Data Science
To think that data science is only about numbers would be a big mistake, as we have just seen.
Text, image, sounds, and movements are also considered data.
It’s just a matter of applying the right technology to capture, store, and process each type of information.
Here are some examples of practical applications of data science.
- Text: Algorithms can read in any language and present a translated summary of the content. Useful in the medical field, in the field of law, marketing, journalism, public safety, and other areas
- Imaging: Automated image analysis accelerates the detection of diseases and reduces time in the hospital, assists in the search for lost people and criminals, facilitates the analysis of customers’ consumption patterns and clocking in companies, etc.
- Sound: the capture and analysis of sound information can be used by service robots, in the diagnosis of diseases and to find out the opinion of customers
- Forecasting: Data science allows you to predict sales, revenue, website visits, complaints, visits to a point of sale, user behavior, etc.
- Segmentation: by creating groups of customers, suppliers, students, employees, users, etc., according to certain similarity criteria, it is possible to obtain precious insights and create segmented actions
- Classification: Audiences can be classified in a variety of ways, based on past data. For example: whether or not they have a disease, whether or not they buy a product or service, whether they have a claim or not, whether they leave the company or not, whether they sue the company or not, whether they like the new function or not, and so on
- Shopping basket: Based on a customer’s habits, it is possible to suggest a shopping basket. For example, if the customer buys coal and meat, then he will buy beer as well
- Social Network Analysis (SNA): Helps map leaders and followers in a network of relationships
- Business Intelligence (BI): through graphs, it is possible to map the company’s important data. The country, state, city, seller, or point of sale that generates the most revenue for each product, for example
- Geolocation: through maps, it is possible to identify geographic patterns of sales, complaints, accidents, diseases, etc.
Data Science And Big Data
Big Data is all about data science, but it’s not an area of knowledge and it’s not a profession.
Big Data is a set of methodologies used for capturing, storing, and processing information.
In other words, the data scientist’s job is there.
In Big Data, structured and unstructured data are processed in a scalable system, that is, in which the number of machines increases as needed.
The Professional Practice Of A Data Scientist
A data scientist is a professional who studies and works with data science.
To be classified as such, it needs to have a complete knowledge, which involves everything from data capture to modeling.
It is a profession with many technical demands.
After all, as Professor Alessandra Montini pointed out, those who master only one or two data capture and processing technologies are not scientists, but specialists.
Profile Of A Data Scientist
We are talking about a very technological area that involves calculations, statistics, and algorithms.
Those who do not do well with numbers and exact sciences in general, therefore, may not have the ideal profile for the profession.
But there is an important particularity: data science requires a balance between technique and strategic thinking, the ability to understand the relationship of data to the challenges of people and organizations.
As for soft skills, the data scientist needs to enjoy learning and facing new problems, in addition to knowing how to communicate clearly.
These characteristics are more important than which degree the professional attended.
What Is The Salary Of A Data Scientist?
Based on the knowledge required, you can imagine that a data scientist can be well compensated.
On Vagas.com, the average salary reported for Brazil is R$ 6,144.00, while on Glassdoor, this average rises to R$ 8,311.00.
Remembering that these values refer to a national average, that is, they can vary greatly depending on the size of the company and the region.
It is possible to earn more than double that in a large company in a metropolis, for example.
What Are The Main Duties Of A Data Scientist?
The job of a data scientist involves data management, which includes capturing, storing, and processing the information of interest to the company.
From there, it must work to extract value from that data, and it does so by generating multiple models and comparing them to arrive at an optimal solution.
To continuously qualify their work and continue their personal development, it is essential that the data scientist is always studying new technologies.
After all, as we mentioned at the beginning of the text, languages and technologies change constantly.
How Does The Job Market Work For A Data Scientist?
The data scientist is a professional that is highly valued by the job market.
Due to the importance of the craft, which we have already highlighted here, but also to the fact that there is not such a large supply of labor with this specialty.
Technology companies are still more likely to bet on data science, but more and more we see companies from the most diverse areas hiring professionals with this knowledge.
In this content, you have learned what data science is, its benefits, and best practices.
If you’re wondering how to work as a data scientist, the answer must have already been clear throughout the text, right?
There is no way to work in the area without a lot of study.
It is necessary to have a very strong theoretical basis, both in the knowledge of programming languages and in modeling.
The most common undergraduate degree among data scientists is Computer Science.
But that’s not a rule.
The most important thing is that the professional has a thirst for constant learning and solving problems.
With so many demands and such a distinct profile, it is natural that this is a highly valued profession in the job market.