Course Outline
Modular Structure
Module 1. Data Architecture and Fundamentals for Scalable Analytics
Topics:
- The evolution of data science toward scalable platforms.
- Differences between traditional, advanced, and scalable analytics. Fundamentals of scalable analytics.
- Ingestion and processing of structured, semi-structured, and unstructured data.
- ETL/ELT and pipeline design.
- Key roles in data teams and workflow.
- Data quality, validation, and optimization.
- Analytics modeling for BI.
- Metrics, KPIs, and SQL performance.
Module 2. Big Data: Distributed Processing
Topics:
- What is Big Data and why traditional methods are no longer sufficient as data grows excessively.
- Principles of distributed computing.
- Handling tools for Big Data environments.
- How to organize and transform large data volumes for better analysis.
- Partitioning strategies.
- Persistence, caching, and memory management.
- Integration of these solutions with cloud platforms, allowing scaling on demand.
- Real-time analytics and stream processing.
Module 3. Applied Statistics and Machine Learning at Scale
Topics:
- Review of inferential statistics for data science.
- Feature engineering on large data volumes.
- Variable selection and dimensionality reduction.
- Supervised and unsupervised models.
- Distributed training and scoring at scale.
- Validation, bias, overfitting, and drift.
- Model evaluation in production contexts.
- Introduction to ML pipelines and automation.
Module 4. Intelligent Data Visualization and Analytical Storytelling
Topics:
- Perceptual principles of visualization.
- Design of strategic, tactical, and operational dashboards oriented toward decision-making.
- Storytelling with data.
- Selection of charts based on analytical objectives.
- Design of executive narratives.
- Interactive and exploratory visualizations.
- Visual detection of patterns, anomalies, and trends.
- Common errors and visual biases.
Module 5. Advanced Data Science Techniques
Topics:
- Time series.
- Recurrent Neural Networks.
- Analysis via Hidden Markov Models.
- Bayesian Inference.
- Stratigraphic Correlation.
- Simulation via Monte Carlo, Bootstrap, and permutation methods.
- Dynamic Time Warping.
Deliverables
- Attendance List. Diagnostic and final evaluation (graded).
- Event Reaction Evaluation.
- Summary of attendance and grades (signed by the instructor).
- Digital manuals.
- Certificates as follows: Certificate of participation.
Delivery Conditions: To be sent electronically within a maximum of 5 business days after the course concludes.
Testimonials (3)
Learning that the QGIS and a tool that can used by other different professionals such land survey
Bame Duncan Koko - Bentel Technologies (Pty) Ltd
Course - QGIS for Geographic Information System
How to use open satellites data for real applications
Tshering Dorji - Druk Holding and Investments
Course - Advanced Geographic Information Systems (GIS)
Hands-on examples allowed us to get an actual feel for how the program works. Good explanations and integration of theoretical concepts and how they relate to practical applications.
