Massive Storage & Big Data
with Deep Learning, AI & Cloud Computing — Updated 2026
🏆 DenverEdu Certified
📅 8 Weeks
📍 Downtown Denver, CO
💻 Python · Spark · Cloud
🎓 All Levels Welcome
📖 Course Description
In an era where data is generated faster than ever, the ability to store, process, and extract intelligence from massive datasets is one of the most valuable skills in the workforce. This course introduces the modern Big Data ecosystem — from hardware and distributed storage to Apache Spark, cloud platforms, and the cutting edge of Deep Learning.
You will gain hands-on experience with real-world tools used by companies like Google, Amazon, and Netflix. No programming background is required — we provide live-coding sessions in Python from the ground up. By the end of the program, you will have a portfolio of projects including an AI model, a data pipeline, and a technical presentation — ready to showcase to employers.
🎯 Course Outcomes
- Understand the fundamentals of Massive Storage, Big Data Architecture, and distributed systems
- Build Python and Apache Spark skills for processing large-scale datasets
- Deploy data pipelines on cloud platforms (AWS, GCP, Azure)
- Apply Deep Learning to solve real-world challenges in Computer Vision and NLP
- Build and train Deep Neural Networks for autonomous driving, load forecasting, and text analysis
- Use modern AI tools including Large Language Models (GPT-4, Llama), transformers, and generative AI
- Present technical findings and write clear, industry-quality technical reports
- Earn a DenverEdu certification demonstrating job-ready Big Data & AI skills
📅 8-Week Course Schedule
| Week | Topic | Hands-On Component | Type |
|---|---|---|---|
| Week 1 |
Big Data Fundamentals & Storage Architecture
Hardware overview, SSDs, NVMe, RAID; Warehouse-Scale Computers (WSCs); Performance & Efficiency metrics
|
Python I — Environment setup, data types, loops, functions | Lecture Lab |
| Week 2 |
Databases: SQL, NoSQL & NewSQL
Relational vs. document vs. key-value stores; PostgreSQL, MongoDB, Redis; Schema design for big data
|
Python II — Pandas, data loading, cleaning, and querying databases | Lecture Lab |
| Week 3 |
Big Data Processing — MapReduce & Apache Spark
MapReduce paradigm, Hadoop HDFS; Apache Spark Core, RDDs, DataFrames; Spark SQL for large-scale querying
|
Spark I — Processing a real-world dataset (NYC Taxi, Twitter data) | Lecture Lab |
| Week 4 |
Cloud Computing & Real-Time Streaming
AWS S3, GCP BigQuery, Azure Data Lake; Apache Kafka for real-time event streaming; Building data pipelines end-to-end
|
Spark II — Cloud deployment, real-time streaming pipeline project | Lecture Project |
| Week 5 |
Artificial Neural Networks (ANN) & Deep Learning Foundations
Perceptrons, activation functions, backpropagation; Building your first neural network; Overfitting, regularization, dropout
|
ANN Lab — MNIST digit recognition from scratch in Python/TensorFlow | Lecture Lab |
| Week 6 |
Computer Vision & Convolutional Neural Networks (CNN)
CNN architecture, filters, pooling; Transfer learning with ResNet, EfficientNet; Real-world applications — traffic sign recognition, autonomous driving
|
CV Project — Traffic Sign Classification & Autonomous Driving Dataset | Lecture Project |
| Week 7 |
Time Series Analysis & NLP with Transformers
LSTMs, GRUs for temporal data; Electrical load forecasting; Transformers, BERT, GPT; Twitter sentiment analysis; Intro to LLMs & Generative AI
|
NLP Lab — Twitter Sentiment Analysis; Time Series Forecasting with real energy data | Lecture Lab |
| Week 8 |
Capstone Project — Presentations & Certification
Final project presentations; Peer Q&A (up to 15 min + 5 min Q&A); Written technical report submission; DenverEdu certification ceremony
|
Capstone Demo Day — Present your end-to-end Big Data or Deep Learning project | Capstone |
🛠️ Capstone Project Tracks & Discussion Topics
Choose Your Capstone Track
- AI — Autonomous Driving Vision System
- AI — Facial Recognition & Privacy Analysis
- Big Data — Real-Time Analytics Pipeline
- IoT — Smart Sensor Data Processing
- NLP — Social Media Trend Analysis
- Security — Anomaly Detection System
Paper Discussion Topics
- Big Data & Distributed Systems
- Deep Learning Architectures
- Computer Vision Advances (2025–2026)
- Time Series & Forecasting
- Reinforcement Learning
- Generative AI & LLMs
📊 Evaluation & Assessment
| Component | Description | Weight |
|---|---|---|
| Weekly Labs | Hands-on Python, Spark, and deep learning exercises with written reflections | 30% |
| Paper Presentation | 15-minute group presentation on a cutting-edge research paper + Q&A session | 25% |
| Capstone Project | End-to-end project: data pipeline or deep learning model, live demo, and technical report | 35% |
| Participation | Attendance, in-class engagement, peer code review contributions | 10% |
⚙️ Tools & Technologies
Big Data Stack
- Python 3.12 + Jupyter Notebooks
- Apache Spark 3.5 & PySpark
- Hadoop HDFS + YARN
- Apache Kafka (streaming)
- SQL (PostgreSQL) & NoSQL (MongoDB)
- AWS S3 / GCP BigQuery / Azure Synapse
AI & Deep Learning Stack
- TensorFlow 2.x & Keras
- PyTorch 2.x
- Hugging Face Transformers
- OpenAI API / LangChain
- Scikit-learn, NumPy, Pandas
- Matplotlib, Seaborn, Plotly
📋 Course Policies
- No prior programming experience required — live-coding sessions bring everyone up to speed
- Assignments must be submitted by the deadline to receive full feedback; late submissions accepted with 1-day grace
- All work must be original. AI tools (like ChatGPT) may be used as learning aids but must be disclosed
- Participation and respectful collaboration are expected in every session
- Students who complete all requirements earn the official DenverEdu Big Data & AI Certificate