Data Science

Data science as a whole reflects the ways in which this huge quantity of data is discovered, conditioned, extracted, compiled, processed, analyzed, interpreted, modeled, visualized, reported on, and presented regardless of the size of the data being processed. Big Data is a special application of data science. Data science uses Industrial Machine Learning, a known framework for analyzing   data, building algorithms, deploying them into production and generating continuous insights to  business problems.It’s a modern take on a very old idea: The Scientific Method. To create a successful business strategy, companies need a map that helps figure out where to focus, understand their business, how to proceed and why. A top Data Scientist with all the key skills can create and implement that map.


Nowadays, data is everywhere, and is found in huge and exponentially increasing quantities. There are many reasons for this information explosion. The most obvious is the increase of cheap and powerful technology tools. Data science creates a value chain of the most important insights needed for companies  to be more competitive. Starts with a hypothesis and collects data that can give high-quality answers to business problems. Building and executing data strategies is the key challenge for Data Scientists. Data science is a very complex framework,  it incorporates all the fields of Statistics, Econometrics, Engineering, Economics, Finance, Computer Science, Database Technologies and is highly applicable to Social Sciences, Engineering, Economics, Finance, and many more The Data Scientist is responsible for guiding a data science project from start to finish.Success in a data science project comes not from access to any one exotic tool, but from having quantifiable goals, good methodology, cross-discipline interactions, and a repeatable workflow.



The ability to process and manage large data volumes has been proven to be not enough to tackle the current challenges presented by “Big Data”. Deep insight is required for understanding interactions among connected systems, space  and time-dependent heterogeneous data structures.

Main challenges:

  • Create and implement complex scientific data management plans in practice.
  • Applying the Scientific Method in a business context – allowing for experimentation and failure.
  • Harness Data mining and Machine Learning methods to answer critical business questions from internal and external data sources.
  • Transforming  the Data Science capability as a strategic asset.
  • Breaking down Big data Analytics into manageable steps.
  • Bringing order to diverse, unstructured data sources.
  • How to tackle data integration issues, before they arise.
  • Integrating analytics into current business processes.
  • Striking a balance between individual data privacy and data usefulness.
  • Disseminating results to different types of stakeholders.
  • How to work with key partners to ensure the  data strategies works for the data science teams.
  • Demonstrating the business value of new data sources.
Expected Outcomes with Strategic Management:

  • New insights into Big Data practices in real world.
  • Faster and better decision making.
  • New algorithms, methodologies and solutions to solve Business Problems
  • New applications that impact society.
  • New digital economies created based on Big Data and Data Science methodologies.
  • New educational programs for students; cultivating leaders for the Data Science society and industry.
  • New Approaches to prioritize data science projects, making best use of limited resources and time.
  • New ways to protect security and privacy of Big Data relevant to individuals and organizations.
  • New methods to ensure companies have access to the data they need, when they need it.
  • Create competitive advantage from both structured and unstructured data.

Data Scientists must ensure that their work identifies, and meets, real business needs. The tools and techniques of data science must be applied in a way which generates maximum value. The Data Science potential must be understood as a strategic asset.


Problem  Solving Office Implemention (PSO)

We create specialized areas (Problem Solving office) who are responsible for supporting and ensuring the quality of the problem solving process. We identify and analyze the problems to be solved trough categorization of business problems by Pereira's Marketscan framework.


  • International best practices;
  • Business-driven identification of Data Science use cases;
  • Data Governance Planning;
  • Integration Strategy and Business Case Guidance;
  • Drive new business solutions;
  • Regulatory compliance;
  • Improve and automate business decisions.
Data Science Methodology & Tools

We apply advanced techniques from mathematics, statistics, computer science, and related fields to analyze large date sets. We develop and implemnet daa collection and data storage procedures and use the best tools and techniques for data transformation. We use start-of-the-arte Data Science technologies to discover the story that your data is telling using the Scientific Method in order to answer your principal business questions. thus, we gather insights into modern data visualization and optimization techniques to both analyze and optimize your business, wich results in the discovery of significant revenue gains for our clients.


  • Data Profiling and cleansing; Statistical Modeling;
  • Data Visualizations;
  • Data Mining and Machine Learning Techniques;
  • Scoring Models;
  • Customer Behavior and segmentation;
  • Cross selling/Recommendation algorithms;
  • Predictive Maintenance;
  • Chum prediction;
  • Fraud detection;
  • Data Scientist Outsourcing;
Data Science Assesments

Organizations teams and processes need to be proficient in dealing with Management, Science and Techonology tools, techniques, knowledge and methologies in order to extract important Business Insights from data.


  • Data Science Assessment;
  • Data Science Maturity;
  • Data Science Model Evaluation.
Data Science Measurement Process & Tools

Organizations need to start managing data through different sources, and integrating its usefulness via a tange of technologies in the market. We offer Data Science solutions and tools to manage and integrate large varieties of unstructured external data sources (Social Network data, etc,...) into business data in order to create new insights and gain competitive advantages. We create new revenue streams.


  • Matching rules and fuzzy logic;
  • Web Scraping Techniques
  • Index and link external unstructured data related to internal business data.
Training Programs

We support in providing tghe right skills and techniques for customer teams in solving problems through knowledge of Science, Technology and Management.


  • Advanced Data Science Certification Training programs;
  • Educational programs for students and business Analysts;
  • Advisory and in-house training;
  • Data Science Problem Solving Course;
  •  Data Science Foundation Courses;
  • Data Science Advanced Courses;
  • Data Science Expert Courses;
  •  Data Science Complete Courses (156h); 
  • Exel Advanced Courses; 
  • Google Analytics Advanced Courses.
  • (see our taining catalogue).


To become more competitive and more efficient, companies need to look at the broader set of related risks, incorporate more data sources, use better tools to allow them to move to real-time or near-real-time analysis.We’re focused on helping you identify high-value use cases that help you take the lead or close the gap with competitors.


Data Science Practices Quick Scans

Data Science Assessment:

Reviews current capabilities and makes recommendations for tools, teams, operating model and governance.

Join your existing teams to drive business outcomes.

Mapping existing and new technology assets to specific business goals.

Provide training programs and advanced Data Science Certification.


Data Science Maturity: 

Gathers insights from experiments and link those insights to current business challenges.

Operationalizes use cases by inserting models and visualizations into production.

Maintains and upgrades previously built models. Updates business with new patterns and insights.


Data Science Model Evaluation:

Evaluate if the predictive models created are accurate, meaningful representations that will prove valuable to your organization in the future fulfilling with the initial level of confidence and the initial goals.

In general, the assessment used should be closely matching the business objectives. Using the right metric can have more influence on your model performance than the algorithm you use.

We would love to hear from you. Challenge us!

Ricardo Santos
Head of Data Science Competency Center