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SKODOR Learning Academy
Beginner to AdvancedFlagship CoursePractical / Project-Based

Data Science & AI Engineering

From mathematical foundations and predictive machine learning to deep learning, LLMs, AI agents, and production MLOps.

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Data Science & AI Engineering system overview
Duration

4–6 Months

Internship

3 Months Practical

Capstone Delivery

Verified Projects

Modules

5 Core Modules

Engineered for High Technical Competence

Data Science & AI Engineering bridges the critical gap between classical data science and modern AI software engineering. Learners master exploratory analytics, statistical modeling, machine learning, deep learning with PyTorch, and generative AI engineering—all backed by production-grade MLOps pipelines.

What You Will Master

  • Master exploratory data analysis, statistics, and hypothesis testing with Python, Pandas, and NumPy
  • Build, evaluate, and interpret supervised and unsupervised ML models with Scikit-learn and XGBoost
  • Develop deep neural networks for vision and NLP using PyTorch and Transformer architectures
  • Engineer advanced Generative AI applications with LLM APIs, LangChain/LlamaIndex, and Vector Databases
  • Implement autonomous AI agents using function calling and Model Context Protocol (MCP)
  • Package, deploy, and monitor scalable AI services using FastAPI, Docker, MLflow, and Cloud Platforms

Detailed Course Syllabus

All topics and modules are extracted directly from the official course specification.

5 Detailed Syllabus Modules

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01Introduction to Data Science, AI Landscape & Mathematical Principles
02Python Programming for Data Science: Variables, Data Structures & Modules
03NumPy for High-Performance Numerical Computing
04Pandas for Data Manipulation, Cleaning & Transformation
05Working with Structured & Semi-Structured Data: CSV, JSON & REST APIs
06Data Visualization & Storytelling with Matplotlib & Seaborn
07Exploratory Data Analysis (EDA) Frameworks
08Descriptive Statistics, Probability Distributions & Sampling Theory
09Correlation Analysis, Hypothesis Testing & A/B Testing Fundamentals
10Git & GitHub Collaborative Development Workflows

Tools & Technologies Taught

Directly aligned with production engineering environments.

PythonLanguage
NumPy & PandasData & BI
Scikit-learnFramework
PyTorchFramework
Hugging FaceAI & LLM
LLM APIsAI & LLM
LangChain / LlamaIndexAI & LLM
Vector DatabasesAI & LLM
FastAPIFramework
DockerDevOps & Tools
MLflowDevOps & Tools
AWS / Azure / GCPCloud
Git & GitHubDevOps & Tools

Capstone Projects

Five comprehensive hands-on milestone projects culminating in an end-to-end production AI engineering application.

Data Science Project

Sprint Project

End-to-end exploratory data analysis, hypothesis testing, and business impact modeling on complex real-world data.

Machine Learning Project

Sprint Project

Predictive model training, hyperparameter tuning, explainability, and containerized API deployment.

Deep Learning Project

Sprint Project

Computer vision or NLP application built using PyTorch and Transformer architectures.

GenAI Project

Sprint Project

Retrieval-Augmented Generation (RAG) assistant leveraging semantic search and vector stores.

Production AI Engineering Capstone

Sprint Project

Full production-ready AI application featuring LLM integration, agentic tool-use, FastAPI backend, Docker deployment, and MLflow monitoring.

3-Month Practical Internship

Structured internship focusing on real-world predictive modeling, deep learning architectures, and production AI application deployment.

Internship Highlights

  • Structured engineering sprint cadence
  • Peer code reviews and architecture defense
  • Portfolio deployment to production cloud instances

Verified Course Learning Outcomes

Analyze diverse datasets and extract actionable insights through rigorous statistical and exploratory methods.
Train, fine-tune, and evaluate robust machine learning and deep learning models with PyTorch and Scikit-learn.
Build context-aware Generative AI applications utilizing vector databases and Retrieval-Augmented Generation (RAG).
Design autonomous AI agents capable of function calling and tool usage using modern agent frameworks.
Ship containerized AI applications to the cloud with FastAPI, Docker, and comprehensive MLOps tracking.

Course Specific Questions

What background is recommended before enrolling?

Basic mathematics and fundamental programming exposure are recommended. Foundations in Python, statistics, and data structures are covered thoroughly in Module 1.

How does this course differ from a traditional Data Science program?

Unlike traditional programs that stop at Jupyter notebooks, this program integrates AI Engineering, Generative AI (LLMs, RAG, Agents), and MLOps deployment into production systems.

What projects will I build for my portfolio?

You will build 5 milestone projects including an end-to-end data science study, an ML predictive API, a PyTorch deep learning system, a RAG assistant, and a production-grade multi-tier AI application.

Earn Your Data Science & AI Engineering Certification

Every successful graduate receives an industry-recognized certificate demonstrating verifiable competence in real-world systems, architectures, and practical engineering execution.

Official SKODOR Academy Certificate of Excellence
Authenticated SKODOR Digital CredentialSample Certificate

Proof of Practical Engineering Autonomy

Unlike traditional course certificates awarded for passive video watching, SKODOR credentials prove that you built, debugged, and shipped commercial-grade systems.

Verifiable Credential

Includes unique cryptographic credential ID and instant QR code verification for recruiters and hiring managers.

Demonstrated Production Skills

Issued only upon completing all technical modules, code reviews, and defending live capstone deliverables.

Shareable Globally

Seamlessly add your verified SKODOR certificate to LinkedIn, personal engineering portfolios, and resumes.

Verified with immutable credential ID • Globally shareable on LinkedIn

Enroll in Data Science & AI Engineering

Cohorts have limited seating to maintain high mentor-to-student interaction and rigorous code reviews. Speak with an admissions advisor today.