Cursos de Análise Preditiva | Cursos de Predictive Analytics

Público

Se você tentar entender os dados aos quais tem acesso ou quiser analisar dados não estruturados disponíveis na rede (como o Twitter, Linked in, etc ...), este curso é para você.

É principalmente destinado a tomadores de decisão e pessoas que precisam escolher quais dados valem a pena coletar e o que vale a pena analisar.

Não se destina a pessoas que configuram a solução, essas pessoas irão se beneficiar do quadro geral.

Modo de entrega

Durante o curso, os delegados serão apresentados a exemplos de trabalho, principalmente de tecnologias de código aberto.

Aulas curtas serão seguidas de apresentação e exercícios simples pelos participantes

Conteúdo e Software utilizados

Todo o software utilizado é atualizado cada vez que o curso é executado, por isso, verificamos as versões mais recentes possíveis.

Abrange o processo de obter, formatar, processar e analisar os dados, para explicar como automatizar o processo de tomada de decisão com o aprendizado de máquina.

R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining.

R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining.

Predictive analytics is the process of using data analytics to make predictions about the future. This process uses data along with data mining, statistics, and machine learning techniques to create a predictive model for forecasting future events.

In this instructor-led, live training, participants will learn how to use Matlab to build predictive models and apply them to large sample data sets to predict future events based on the data.

By the end of this training, participants will be able to:

- Create predictive models to analyze patterns in historical and transactional data
- Use predictive modeling to identify risks and opportunities
- Build mathematical models that capture important trends
- Use data from devices and business systems to reduce waste, save time, or cut costs

Audience

- Developers
- Engineers
- Domain experts

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

Advances in technologies and the increasing amount of information are transforming how law enforcement is conducted. The challenges that Big Data pose are nearly as daunting as Big Data's promise. Storing data efficiently is one of these challenges; effectively analyzing it is another.

In this instructor-led, live training, participants will learn the mindset with which to approach Big Data technologies, assess their impact on existing processes and policies, and implement these technologies for the purpose of identifying criminal activity and preventing crime. Case studies from law enforcement organizations around the world will be examined to gain insights on their adoption approaches, challenges and results.

By the end of this training, participants will be able to:

- Combine Big Data technology with traditional data gathering processes to piece together a story during an investigation
- Implement industrial big data storage and processing solutions for data analysis
- Prepare a proposal for the adoption of the most adequate tools and processes for enabling a data-driven approach to criminal investigation

Audience

- Law Enforcement specialists with a technical background

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

This classroom based training session will contain presentations and computer based examples and case study exercises to undertake.

RapidMiner is an open source data science software platform for rapid application prototyping and development. It includes an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics.

In this instructor-led, live training, participants will learn how to use RapidMiner Studio for data preparation, machine learning, and predictive model deployment.

By the end of this training, participants will be able to:

- Install and configure RapidMiner
- Prepare and visualize data with RapidMiner
- Validate machine learning models
- Mashup data and create predictive models
- Operationalize predictive analytics within a business process
- Troubleshoot and optimize RapidMiner

Audience

- Data scientists
- Engineers
- Developers

Format of the Course

- Part lecture, part discussion, exercises and heavy hands-on practice

Note

- To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Portugal (online or onsite) is aimed at technical persons who wish to build machine learning models using algorithms such as GLM, Deep Learning and Random Forests.

By the end of this training, participants will be able to:

- Install and configure H2O.
- Create machine learning models using different popular algorithms.
- Evaluate models based on the type of data and business requirements.

This instructor-led, live training in Portugal (online or onsite) is aimed at data scientists and data analysts who wish to automate, evaluate, and manage predictive models using DataRobot's machine learning capabilities.

By the end of this training, participants will be able to:

- Load datasets in DataRobot to analyze, assess, and quality check data.
- Build and train models to identify important variables and meet prediction targets.
- Interpret models to create valuable insights that are useful in making business decisions.
- Monitor and manage models to maintain an optimized prediction performance.