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. | ||||
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) | ||
 |
HOW DO WE ADDRESS THE PAIN POINTS
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. |
OUR PAIN RELIEVERS
|
| Data Science Methodology & Tools | ||
 |
HOW DO WE ADDRESS THE PAIN POINTS
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. |
OUR PAIN RELIEVERS
|
| Data Science Assesments | ||
 |
HOW DO WE ADDRESS THE PAIN POINTS
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. |
OUR PAIN RELIEVERS
|
| Data Science Measurement Process & Tools | ||
 |
HOW DO WE ADDRESS THE PAIN POINTS
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. |
OUR PAIN RELIEVERS
|
| Training Programs | ||
 |
HOW DO WE ADDRESS THE PAIN POINTS
We support in providing tghe right skills and techniques for customer teams in solving problems through knowledge of Science, Technology and Management. |
OUR PAIN RELIEVERS
|
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.