Depoimentos de pessoas sobre Advanced Analytics e Business Optimization.

• O Cientista de Dados está atento ao excesso de informação (information overload) e consegue trabalhar todo o ciclo da informação: coleta, preparo dos dados, organização, recuperação, descoberta, análises (exploratória, inferencial, preditiva) e a visualização das informações analisadas. Nesse sentido, é necessário ter conhecimento estatístico; saber manipular e extrair do banco de dados (local ou cloud computing); programar em linguagens de programação como R e Python; e o mais importante, pensar “fora da caixa”, ser inovador.

  

  Rondon de Andrade Porto

  CEO na IOOP Online Information

• O grande problema que hoje todas as pessoas e empresas enfrentam é a grande quantidade de informações a que somos expostos diariamente, e o desafio é justamente fazer uma curadoria do que realmente importa para nossas vidas e nosso trabalho. O trabalho de um Cientista de Dados é justamente este: identificar fontes, coletar e organizar dados de modo a extrair insights que possam ajudar na tomada de decisão. Importante ressaltar que o Cientista de Dados além de ter uma sólida base técnica deve também se preocupar em desenvolver sua capacidade de visão de negócios.

  

  Edgar Muniz Moreira

  Diretor na MITI Inteligência

• Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician.

  

  Josh Wills

  Director of Data Engineering at Slack

• It’s popping up all over the sciences, social sciences, and humanities. Data science has the potential to transform science. We don’t exactly know where data science is going to go, but we know we want to be out in front of it, leading the way.

  

  Daniel Spielman

  Professor of Computer Sc., Statistics and Data Sc. at Yale’s Henry Ford II

• I think of data science as more like a practice than a job. Think of the scientific method, where you have to have a problem statement, generate a hypothesis, collect data, analyze data and then communicate the results and take action. If you just use the scientific method as a way to approach data-intensive projects, I think you’re more apt to be successful with your outcome.

  

  Bob Hayes

  Ph.D, Chief Research Officer at Appuri

• O Cientista de Dados utiliza todo o seu potencial para desenvolver análises preditivas e novos modelos visuais intuitivos para a compreensão de todos e traduz de forma simples o que é complexo para o senso comum. Portanto, não basta apenas o conhecimento técnico, ou utilizar “receitas de bolo” (cookbooks), de pacotes, mas ser criativo, inovar com as técnicas disponíveis de análise de dados, de machine learning, empreender e desenvolver seus próprios modelos preditivos e visuais, pois nem todas as soluções estarão disponíveis. E nesse momento, o Cientista de Dados se revela disruptivo, pois não apenas enquadra os seus dados em modelos, mas cria o próprio.

  

  Rondon de Andrade Porto

  CEO na IOOP Online Information

• In my view, success for data science professionals relies on becoming trained and able data scientists with the ability to perform data processing and computation at a massive scale. To achieve this, professionals must invest time in ongoing education through institutions with multidisciplinary programs that include elements from engineering, mathematical sciences, and social sciences. Converting big data into meaningful information begins with skilled professionals who are educated in all disciplines to be both data scientists and statisticians.

  

  Devavrat Shah

  Professor at MIT’s Dep. of Electrical Engineering and Computer Science

• A data scientist does model-driven analyses of our data; analyzes to improve our planning, increase our productivity, and develop our deeper levels of subject matter expertise. A data scientist works at the tactical, operational, and strategic levels, sharing insights with the business.

  

  Chris Pehura

  Practice Director, Management Consultant at C-SUITE DATA

• Data science is about more than just big data. It is about collaboration, analysis, and policy.

  

  Harrison Zhou

  Head Dep. of Data Science, Yale University

• What makes a good scientist great is creativity with data, skepticism and good communication skills. Getting all of that together in the same person is difficult – because traditionally, different people follow different paths in their careers – some are more technical, others are more creative and communicative. A data scientist has to have both.

  

  Monica Rogati

  Independent Data Science Advisor

• Big Data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.

  

  Dan Ariely

  Fuqua School of Business, Duke University

• With too little data, you won’t be able to make any conclusions that you trust. With loads of data you will find relationships that aren’t real. Big data isn’t about bits, it’s about talent.

  

  Douglas Merrill

  ZestFinance, Princeton University