Progressive pathway
Progress from understanding data to designing enterprise platforms.
Professional academy
Academy of data and analytics
Develop the skills to turn data into reliable information, analytical models, data products, and scalable enterprise architectures.
Academic approach
Comprehensive training to turn data into decisions and products
The academy connects business intelligence, advanced analytics, data science, engineering, architecture, and governance.
Business focus
Analyze data in relation to business questions, decisions, indicators, and organizational outcomes.
Technical practice
Use SQL, programming, visualization, pipelines, and modern data platforms.
Governance and quality
The pathway covers security, privacy, lineage, catalogs, quality, and responsible data management.
Professional pathway
Build your profile from data practitioner to enterprise data architect
Choose an analytical, scientific, engineering, or architecture focus.
After Business Intelligence Specialist, deepen your data science or data engineering skills before advancing to architecture.
Data practitioner
Fundamentals, quality, indicators, and data-driven decision-making.
Business intelligence specialist
Modeling, SQL, visualization, dashboards, and business analysis.
Analytics specialist
Applied statistics, segmentation, diagnostics, and experimentation.
Data scientist
Predictive models, programming, validation, and communication of results.
Data engineer
Pipelines, storage, integration, and data operations.
Enterprise data architect
Architecture, governance, security, and modern data platforms.
Learning plan
Six courses covering the data and analytics pathway
Each course can be taken independently or as part of a stackable certification.
Course 1
Data and business analytics fundamentals
An introduction to the data lifecycle, quality, metrics, visualization, and decision-making.
- Data types and sources
- Indicators and business questions
- Data quality and preparation
- Introductory analysis project
Course 2
Business intelligence and visualization
Build analytical models, queries, indicators, dashboards, and data narratives.
- SQL for analysis
- Dimensional modeling
- Dashboards and KPIs
- Data storytelling
Course 3
Advanced analytics and applied statistics
Apply statistics, segmentation, testing, experimentation, and diagnostic analysis.
- Descriptive and inferential statistics
- Segmentation and cohorts
- Hypothesis testing
- Experimental design
Course 4
Applied data science
Develop predictive and analytical models using programming, validation, and clear communication of results.
- Python for data science
- Supervised models
- Evaluation and explainability
- Predictive project
Course 5
Data engineering and modern platforms
Design, build, and operate pipelines, data lakes, warehouses, and data platforms.
- ETL and ELT
- Data warehouses and data lakes
- Batch and event processing
- Operations and observability
Course 6
Enterprise data architecture and governance
Design scalable, secure, governed data ecosystems aligned with strategy.
- Modern architectures
- Catalogs, lineage, and metadata
- Security and privacy
- Governance and operating model
Professional credentials
Six certifications aligned with professional roles
Credentials validate knowledge, technical practice, and the ability to solve real-world problems.
Data practitioner
Validates fundamental knowledge of data, quality, indicators, and decision-making.
Requires: Course 1 and a scenario-based exam.
Business intelligence specialist
Demonstrates the ability to query, model, visualize, and communicate business information.
Requires: Course 2 and a practical lab.
Analytics specialist
Validates skills in statistics, segmentation, diagnostics, and experimentation.
Requires: Course 3 and an applied case.
Data scientist
Certifies the ability to develop, evaluate, and explain analytical and predictive models.
Requires: Courses 3 and 4 and a project.
Data engineer
Validates data platform design, integration, processing, deployment, and operations.
Requires: Courses 2 and 5 and a project.
Enterprise data architect
Certifies the ability to design architectures, governance models, and enterprise platforms.
Requires: The complete pathway and a final project defense.
Technology ecosystem
Tools and platforms used during training
The academy takes a vendor-neutral approach and uses current technologies to develop transferable skills.
SQL
Power BI
Python
Pandas
Jupyter
Advanced Excel
Google BigQuery
Databricks
Apache Spark
dbt
Hands-on learning
Labs and projects that demonstrate competency
Activities can be adapted to each organization's industry, data sources, and indicators.
Executive dashboard
Build a model, indicators, and visualizations to support management decisions.
Customer segmentation
Exploratory analysis, clustering, and actionable customer profiles.
Churn prediction
Prepare data, model, evaluate, and explain results.
Data pipeline
Design an ETL or ELT flow to integrate, transform, and load information.
Enterprise data lake
Organize zones, formats, metadata, and access policies.
Governance model
Define domains, owners, quality rules, catalogs, and lineage.
Capstone project
Design an enterprise data and analytics platform
The final project integrates business needs, data sources, architecture, analytical models, security, governance, and operations.
Request project information1. Assess business needs and questions
2. Inventory and evaluate sources
3. Design the architecture and model
4. Build the analytical product
5. Present and defend the project
Entry profile
- Professionals in business, technology, finance, operations, marketing, or planning.
- Analysts, developers, consultants, and information managers.
- Basic knowledge of spreadsheets, indicators, and analytical logic.
- Prior technical experience is recommended for data science and engineering.
Graduate profile
- Data practitioner, BI specialist, analytics specialist, data scientist, or data engineer.
- Developer of dashboards, analytical models, and data products.
- Responsible for data quality, governance, architecture, or platforms.
- Consultant in data-driven strategy and transformation.
Frequently asked questions
Key information before you start
No. Each course can be taken independently. The full pathway is required only for the Enterprise Data Architect certification.
Introductory and business intelligence courses do not require advanced programming. Data science and engineering include Python, SQL, and technical tools.
The principles are vendor-neutral. Labs may use Power BI or other tools depending on the delivery format and cohort context.
Training and certification are managed separately. You may take the exam after the course or at a later date.
Yes. Cases, sources, indicators, labs, and the final project can be tailored to the organization's data and needs.
Start your professional pathway in data and analytics
Request information about upcoming cohorts, delivery options, pricing, certifications, and corporate programs.