Diploma of Achievement
Advanced Analytics and Predictive Modeling with Artificial Intelligence
Issuing Body:
Data Pulse Learning
Verified by:
Data Pulse Analytics ®
More than a hunch it's Data
Grade:
Satisfactory
This diploma is proudly awarded to:
Rafael Rangel Ibarra
For the successful completion of the Advanced Analytics and Predictive Modeling with Artificial Intelligence
Intermediate Level program.
Completion Date: December 13th 2025
Program Overview:
This intensive intermediate program bridges the gap between foundational data science and advanced AI implementation. The curriculum begins with a rigorous mathematical review of Linear Algebra and Inferential Statistics, providing the basis for high-performance modeling. Students mastered the mechanics of Linear Regression (simple and multivariable) and Logistic Regression before advancing into the broader framework of Generalized Linear Models (GLMs), focusing on the Exponential Family and Link Functions.
The program also explored the architectural differences between Linear Generative and Discriminative Models, including Gaussian Discriminant Analysis (GDA), Naive Bayes, and Markov Chains. A core highlight was the Capstone Mini-Project: "Rain Prediction in CDMX," where students built a probability indicator for tourism using MLflow for experiment tracking and Flask for REST API deployment. The course culminated in Deep Learning (CNNs, RNNs/LSTMs) and Generative AI, featuring the Transformer architecture and the pre-training paradigms of BERT and Llama via HuggingFace.
Key Competency Areas:
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Foundational & Advanced Modeling: Mastery of Linear Regression, Binary and Multinomial (Softmax) Logistic Regression, and model selection via L2 Regularization.
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Generalized Linear Models (GLMs): Deep understanding of Random and Systematic components, Link Functions, and their application to non-linear hypotheses.
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Generative vs. Discriminative Frameworks: Comparative implementation of GDA, Naive Bayes, and Markovian processes.
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MLOps & API Engineering: Professional lifecycle management using MLflow and model serving via REST APIs with Flask.
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Deep Learning Architectures: Development of Dense Networks, CNNs (LeNet, VGG, ResNet), and sequence modeling with RNNs and LSTMs.
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Natural Language Processing & GenAI: Transformer mechanisms, Attention layers, and working with pre-trained models like BERT and Llama through HuggingFace.
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Applied AI Engineering: Implementation of Retrieval-Augmented Generation (RAG) and industrial-grade Python ETL using pandas, scikit-learn, and numpy.
Jonathan Domínguez Aldana
CEO & Founder of
Data Pulse Analytics.
In collaboration with:
In collaboration with:
Jonathan Domínguez Aldana
CEO & Founder of
Data Pulse Analytics.
