“… and we are not far from the time when it will be understood that, in order to exercise citizenship efficiently, it will be as necessary to know how to calculate and think in averages, maximums, and minimums as it is now necessary to know how to read and write.” – H.G. Wells, “Mankind in the Making” (1903)
Indeed, nowadays, approximately 95 million terabytes of data are generated per day (one terabyte equals 1024 gigabytes), and specialized individuals are required to sift through the most relevant ones, interpret them, and thereby transform them into useful information that facilitates informed decision-making. Naturally, several higher education institutions offer courses to train these professionals, with degrees in statistics and data science standing out, with the former being the focus of this article.
Best institutions to study
According to the QS World University Rankings by Subject 2021, promoted by Quacquarelli Symonds, five of the top 200 Statistics courses in the world are offered by Brazilian universities, which are:
- USP (University of São Paulo): This is the best course in Brazil, according to this ranking, ranking between 51st and 100th position overall. It is offered in two different campuses: São Carlos and São Paulo. The former has a higher workload and practical hours, while the latter focuses on theoretical classes.
- Unicamp (State University of Campinas): Ranked between 101st and 150th place, the course is offered at the Campinas campus. It’s worth noting that since 2021, any student who completes a specific group of disciplines during their undergraduate studies can obtain a certificate in Data Science. This allows graduates to not only pursue a career in Statistics but also work in the field of data science, greatly enhancing their employability. Additionally, Statistics students at Unicamp can join the country’s first junior company in the field: Estat Júnior.
- UFRJ (Federal University of Rio de Janeiro): The course is ranked similarly to Unicamp and is offered at the Rio de Janeiro campus. It is worth noting that UFRJ is the highest-ranked university outside the state of São Paulo.
- UFMG (Federal University of Minas Gerais): The course is ranked between 151st and 200th position and is offered at the Belo Horizonte campus. It is the top-ranked Statistics course in the state of Minas Gerais, according to the ranking.
- UFPE (Federal University of Pernambuco): The course is ranked similarly to UFMG and is offered at the Recife campus. It is worth highlighting that UFPE is the only university outside the Southeast region included in the ranking.
Areas of expertise
A professional in Statistics can work in a wide range of fields because their education in mathematics equips them with skills to analyze, collect, and interpret data. Additionally, they can make projections for the future by analyzing data results with useful information to anticipate potential events in business, economics, social, and even natural areas.
The areas of expertise for professionals in this field include:
Financial market: Being one of the highest-paid areas with more job opportunities and appreciation, statisticians work in banks, stock markets, risk analysis, and other specific areas within the banking sector.
Industry: Statisticians in the industrial sector can work in quality control, process improvement, market positioning, and collecting customer and employee satisfaction data.
Agribusiness: Much of the work in agribusiness is based on statistics, including production analysis, specifying the most productive plant/animal variety, projecting commodity prices, among other areas that can be studied by a statistician.
Market research, opinion, and marketing: This is one of the most well-known areas, especially due to election polls, public opinion surveys, censuses, blind tests, and opinion research.
Biomedicine and pharmaceutical industry: Statisticians in this field assess the performance of medications, and their analysis is mandatory throughout the entire drug production process.
Information technology and telecommunications: Professionals in this area develop models and simulations to solve complex problems, which may involve machine learning, deep learning, and bootstrap. Want to know what these terms mean? We’ll explain them later!
Environment: Changes and impacts on the environment can be studied and monitored using statistical models, including prediction and regression models.
Education institutions and research centers: Statisticians can work in scientific research or teach at institutions that train statisticians and other professionals.
Hospitals and medical research institutions: Statisticians contribute to the planning of controlled clinical experiments, determination of disease risk factors, comparison of various clinical treatments, and scientific research.
Big data: As a statistician, one can work in the technology field, which has gained significant prominence in the job market. This field involves manipulating large volumes of information using tools that enable faster reading and synthesis of information, making it useful.
Future perspectives
As seen, Statistics is a field with enormous applicability in the job market and has a lot of room for growth, especially in Brazil. As data becomes increasingly valuable for decision-makers in both the private and public sectors, statisticians gain great importance in the current era.
People are fascinated by the diverse range of areas in which statisticians can work, from medical research to modeling for the financial market. And the evolution doesn’t stop there! In this historical period of information rationalization and data science, techniques like machine learning, deep learning, bootstrap, among many others, are increasingly valued and generate interest among statistics students, both for their ability to summarize massive amounts of data and for their innovation.
But what do all these complicated terms mean?
Machine Learning: Also known as ML, it refers to techniques that use artificial intelligence (AI) to recognize patterns with minimal human intervention. It involves machines learning on their own. It may seem dystopian at first – “Machines will dominate humans!” – but machine learning is already present in your daily life without you knowing it! Remember those recommended movies that appear on Netflix? Or those ads that your browser suggests to you? Or even the innovative self-driving cars from Google and Tesla? Yes! They all utilize techniques involving machine learning to identify what you’re most likely to enjoy watching based on your past movies or series, or that product you’re about to buy and advertisements flood your screen, urging you to make the purchase.
Deep Learning: Deep learning is a subset of machine learning that allows for modeling and pattern recognition through highly advanced techniques. It uses algorithms with complex structures such as speech
and sound recognition, as well as image recognition. Its applications range from personal assistant robots – “Hey Alexa, turn on the lights” – to surveillance systems to prevent theft and fraud.
Bootstrap: The term “bootstrap” is derived from the phrase “to pull oneself up by one’s bootstraps,” which means improving something even when it seems impossible. Bootstrap is a powerful tool that uses resampling techniques to improve a population statistical result by rearranging and reusing samples in various ways, even when it appears impossible to enhance it.
With all these terms and concepts, statistics reaffirms its position as a profession of the future, immersed in an unimaginable amount of data that needs to be organized in the best possible ways to make the best decisions.
Adaptation and translation into english by Rubens Cortelazzi Roncato Original version (Portuguese) by Bianca da Silva, Décio M. Filho, and Lucas Perondi Kist.
