In a world where data drives decision-making, the ability to manage and leverage information has become an essential skill. With each passing year, efficient data management not only optimizes decision-making, but also drives business success.
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Our courses in Data & Analytics
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Our courses in Data & Analytics
Filter by area:
Agile
AI
Business Analysis
Change Management
Computational Thinking
Data & Analytics
Software Development
Project Management
Service Management
Innovation & Creativity
Microsoft®
No courses found in this area.
Main objectives
/01
Prepare, Explore, and Visualize Data
Prepare, explore, and visualize data to generate clear and actionable insights.
/02
Provide Insights
Provide insights derived from data analysis that translate into concrete actions and benefits for the organization.
/03
Act as a Bridge
Act as a bridge between technical data analysis and practical business needs.
/04
Train Teams
Train teams in the use of data analysis tools and techniques.
/05
Promote a Data Culture
Foster a data culture within the organization to improve data-driven decision-making.
/06
Develop Dashboards and Tools
Develop dashboards and visualization tools that support informed decision-making.
/07
Conduct Experiments and Analysis
Conduct experiments and analyses to uncover new insights and generate accurate forecasts.
Key Training Areas
Key Training Areas
Design Patterns: Learn how to use design patterns to develop efficient, reusable solutions.
Software Architecture: Design architectures that enable scalability and maintainability.
Databases: Manage SQL and NoSQL databases to handle large volumes of data.
Programming Principles: Apply principles such as SOLID, DRY, and KISS to write clean and maintainable code, following best practices such as those in “Clean Code.”
Programming Languages: Master key programming languages such as Java, Python, JavaScript, and others.
Frontend and Backend Development: Learn how to develop both the frontend and backend of applications to deliver complete solutions.
Agile Methodologies: Implements methodologies such as Scrum and Kanban to manage projects efficiently.
Testing and TDD: Ensure software quality with techniques such as Test-Driven Development (TDD).
Using Git: Collaborate on projects using version control systems such as Git.
Continuous Integration: Automate continuous integration and deployment (CI/CD) processes.
Security Testing: Identify and mitigate vulnerabilities in software.
Code Reviews: Ensure code quality with regular reviews and adherence to best practices.
Strategic Support and Decision-Making
Infrastructure and Technology Management
Predictive Modeling and Machine Learning
Data Analysis and Visualization
Data Process Optimization
Data Quality and Governance
Professional Profiles
Data Owner
Strategically owns the organization’s data, aligns decision-making with business objectives, allocates resources, and connects data to business actions.
Data Steward
Responsible for creating and maintaining a data governance framework, ensuring data quality, accuracy, and regulatory compliance.
Data Manager
Manages data operationally and strategically, overseeing input, storage, and retrieval. Coordinates data flow between systems, ensuring quality and integrity.
Data Analyst
Collect, process, and analyze data to describe the organizational situation. Clean data before analyzing it and use visualization tools.
Data Architect
Designs and manages data architecture, defines policies and standards, creates an integrated and secure framework, and monitors data flow. Requires knowledge of database modeling and management.
Data Engineer
Controls data flow, builds and maintains infrastructure and storage systems. It uses SQL, programming languages such as Java, Scala, or Python, and cloud solutions to ensure accessibility and organization.
Data Scientist
Develop predictive models using machine learning, uncovering insights and generating predictions. Handles large volumes of structured and unstructured data using SQL, Python, or R for preparation, exploration, visualization, and prediction.
Advantages of Training in Data & Analytics
Understand the need to design robust data infrastructures, ensure their quality and governance, and develop advanced machine learning models. Transform data into actionable insights, enhancing your ability to make informed strategic decisions, turning data into value.
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