Before organising a course or seminar, we listen to the real needs and objectives of each client, in order to adapt the training and get the most out of it. We tailor each course to your needs.
We are also specialists in 'in company' trainings adapted to the needs of each organisation, where the benefit for several attendees from the same company is much greater. If this is your case, contact us.
Ponemos a disposición también plataforma Cloud con todas las herramientas instaladas y configuradas, listas para la formación, incluyendo ejercicios, bases de datos, etc... para no perder tiempo en la preparación y configuración inicial. ¡Sólo preocuparos de aprender!
Ofrecemos también la posibilidad de realizar formaciones en base a ‘Casos de Uso’
Se complementa la formación tradicional de un temario/horas/profesor con la realización de casos prácticos en las semanas posteriores al curso en base a datos reales de la propia organización, de forma que se puedan ir poniendo en producción proyectos iniciales con nuestro soporte, apoyo al desarrollo y revisión con los alumnos y equipos, etc…
En los 10 últimos años, ¡hemos formado a más de 250 organizaciones y 3.000 alumnos!
Ah, y regalamos nuestras famosas camisetas de Data Ninjas a todos los asistentes. No te quedes si las tuyas
ETL Kettle Pentaho
ETL Kettle Pentaho
Goal
Extraction, transformation and loading (ETL) of data is the key to success in a BI system for managing the quality of the data properly.
In this course you will have some of the best practices that we recommend for the design of ETL processes such as:
Centralization of procedures so that the coherence and consistency of the exchanged data from different sources is ensured.
Avoid redundancy calculations: if there is data previously calculated in the operational databases should not return to the calculation performed in the extraction. This premise aims to achieve a double objective.
Establishment of points of "quality control" and validation.
Implement processes charging information for possible errors in the initial information.
Consider the possibility of using intermediate tables with the most atomic level of information to be treated.
In addition, we will review the most important and used ETL tool Pentaho elements: Pentaho Data Integration and Kettle.
Target audiences
Observations
Data Quality and Integration with Pentaho
Syllabus
Introduction Open Source Business Intelligence Platforms
- Architecture and features of Pentaho, SpagoBI , BIRT , Mondrian , Kettle , Talend , etc ...
- Development Tools.
ETL (Kettle)
- Good practices for ETL process definition.
- Functional Overview (work, Transformations, flow control)
- Parameterisation
- Environment Variables
- Parameterization of connections to databases. Shared connections.
- Parameterization of loads and load types
- Jobs
- Overview
- Steps types (Mail, File Managament, etc ...)
- Description of Steps
- Steps examples of more useful and common
- Transformations
- Overview
- Steps types (Input, Output, Tranform, etc ...)
- Description of Steps
- Steps examples of more useful and common
- Practical examples
ETL Kettle Pentaho
ETL Kettle Pentaho
Goal
Extraction, transformation and loading (ETL) of data is the key to success in a BI system for managing the quality of the data properly.
In this course you will have some of the best practices that we recommend for the design of ETL processes such as:
Centralization of procedures so that the coherence and consistency of the exchanged data from different sources is ensured.
Avoid redundancy calculations: if there is data previously calculated in the operational databases should not return to the calculation performed in the extraction. This premise aims to achieve a double objective.
Establishment of points of "quality control" and validation.
Implement processes charging information for possible errors in the initial information.
Consider the possibility of using intermediate tables with the most atomic level of information to be treated.
In addition, we will review the most important and used ETL tool Pentaho elements: Pentaho Data Integration and Kettle.
Target audiences
Observations
Data Quality and Integration with Pentaho
Syllabus
Introduction Open Source Business Intelligence Platforms
- Architecture and features of Pentaho, SpagoBI , BIRT , Mondrian , Kettle , Talend , etc ...
- Development Tools.
ETL (Kettle)
- Good practices for ETL process definition.
- Functional Overview (work, Transformations, flow control)
- Parameterisation
- Environment Variables
- Parameterization of connections to databases. Shared connections.
- Parameterization of loads and load types
- Jobs
- Overview
- Steps types (Mail, File Managament, etc ...)
- Description of Steps
- Steps examples of more useful and common
- Transformations
- Overview
- Steps types (Input, Output, Tranform, etc ...)
- Description of Steps
- Steps examples of more useful and common
- Practical examples
ETLs WITH TALEND
ETLs WITH TALEND
Goal
Talend is the European leader in data integration (ETL), Data Quality and Master Data Management.
It is backed by the creators and founders of Business Objects.
We are one of the first specialists in Talend in Spain.
Tibco did migration to Talend for Yell. We provide practical training, which lays the foundation of the Business Intelligence strategy, accompanied by participatory sessions, on real cases and business solutions.
Target audiences
Observations
Certification
All students receiving the course will be given a certificate of attendance.
Syllabus
1. Introduction
- Environments operations and integration with Business Intelligence
- Initial presentation of Talend Open Studio
2. Modeling jobs
- Using the Business Modeler
- Document management for the project
3. Using Job Designer to generate code
- Examples and exercises work designs
- Testing data sets
4. Components input / output
- Management access to XML files, delimited characters, etc ...
- Access Relational Databases
5. Metadata Repository
- Centralize connections
- Centralizing data flows and schemes
6. Data Transformations
- Using different components transformations
- Parameterization and mapping data using TMAP (join)
- Profiling data using filters
- Generation of different outputs and exception handling
- Practical exercises
7. Development Features
- Defining project environments (development, production)
- Inclusion of java code on jobs
- Set error handling
- Get statistics and logs of work
8. Debug and Deploy jobs
- Generation of technical documentation of work
- Using the Debug view
- Generate jobs and provide them as Web services
Machine Learning
Machine Learning
Goal
This course will understand the concepts needed to perform processes Machine Learning, this branch of artificial intelligence that aims to develop techniques that allow computers to learn.
Machine Learning projects create algorithms that can generalize and recognize behavior patterns from information provided by way of example ( training). Machine Learning techniques are used among others in the following areas: Medicine, Bioinformatics, Marketing, Natural Language Processing, Image Processing, Machine Vision, Spam Detection.
Target audiences
- ICT professionals: Consultants BI, Scientific Data.
- Professionals of Applied Sciences: Mathematics, Statistics, Physics.
Observations
- Methodology: The course intersperses theoretical parts where fundamental concepts are taught to understand the practical exercises taught.
- Requirements: Basics: Linear Algebra, calculus and probability theory.
Syllabus
Machine Learning with Scikit-Learn Data Science framework (Anaconda with Python 3)
1. Introduction to Machine Learning
- Techniques
- Classification
- Regression
- Clustering
- Preprocessing and dimensional reduction
- Attribute selection
- Performance evaluation
- Matrices de confusión
- KPIs R2, MAE, MSE
2. Regression (Prediction of continuous values)
- Algorithms
- Ordinal Least Squares
- Ridge Regression
- Laso Regression
- Elastic Net
- Examples
3. Classification (Identification of the category to which an object belongs)
- Algorithms
- Logistic Regression
- Support Vector Machines
- KNearest Neighbors
- Decision Trees
- Random Forest
- Multi-layer Perceptron
- Examples
4. Clustering (Grouping similar objects in sets)
- Algorithms
- KMeans
- Spectral Clustering
- DBSCAN
- Examples
Contacto
Ajustamos cada curso a sus necesidades.
Nuestra oficina en Madrid
- Avenida de Brasil 17. Planta 16
- 28046 Madrid
- info@stratebi.com
- Tlfno: +34 91.788.34.10
- Fax:+34 91.788.57.01
