Big Data Analytics with AI — Hadoop, Spark & ML Pipelines
This 4‑week intermediate course teaches you how to leverage the power of Hadoop and Spark for advanced analytics and machine learning on massive datasets. You’ll learn to process, analyze, and build predictive models that scale to terabytes of data, bridging the gap between traditional ML and big data technologies.
- 4 Weeks
- Hadoop, Spark
- NSTC Verified Cert
- Scalable ML
Part of NanoSchool’s Deep Science Learning Organisation • NSTC Accredited
Hadoop/Spark & ML code preview
Skills You’ll Build:
What You’ll Learn: Big Data AI Fundamentals
You’ll go from understanding single-machine ML to building and deploying models that can process and learn from massive, distributed datasets.
Understand HDFS, YARN, and the core concepts of distributed storage and processing.
Learn RDDs, DataFrames, and Spark SQL for efficient large-scale data processing.
Apply machine learning algorithms to big data using Spark’s built-in ML library.
Deploy and run your big data pipelines on cloud platforms like AWS EMR or Databricks.
Who Is This Course For?
Ideal for data engineers, scientists, and developers ready to scale their AI workloads to handle big data.
- Data engineers wanting to add AI capabilities
- Data scientists needing to process large datasets
- Developers building scalable AI applications
Hands-On Projects
Log Analysis with MapReduce
Write a MapReduce job in Hadoop to analyze large server log files.
Customer Segmentation with Spark
Process a large customer dataset using PySpark and cluster customers using MLlib.
End-to-End ML Pipeline
Build a full pipeline: ingest data with Spark, train a model, and deploy it on a cloud platform.
4-Week Big Data Syllabus
~48 hours total • Lifetime LMS access • 1:1 mentor support
Week 1: Hadoop & MapReduce
- Introduction to Hadoop ecosystem (HDFS, YARN)
- Concepts of distributed computing and MapReduce
- Writing MapReduce jobs in Python (mrjob)
- Basic Hadoop cluster setup (local)
Week 2: Spark Fundamentals
- Introduction to Apache Spark and RDDs
- PySpark DataFrame API
- Transformations and actions
- Data ingestion from various sources (CSV, Parquet)
Week 3: Spark MLlib
- Machine learning with Spark MLlib
- Feature engineering using Spark
- Applying classification and regression models
- Evaluation metrics for big data models
Week 4: Advanced Pipelines & Cloud
- Building end-to-end ML pipelines with Spark
- Introduction to Delta Lake for data management
- Deploying Spark jobs to cloud (AWS EMR, Databricks)
- Capstone project: Full pipeline deployment
NSTC‑Accredited Certificate
Share your verified credential on LinkedIn, resumes, and portfolios.
Frequently Asked Questions
No, prior experience is not required. However, a solid understanding of Python, basic machine learning concepts, and familiarity with data manipulation libraries like Pandas is essential.
Yes! You will use Apache Spark to process large, real-world datasets (e.g., web logs, financial transactions) that cannot fit in memory on a single machine.
AI Mentors
Learn from data engineers and ML architects who build and manage large-scale analytics and AI pipelines for big tech companies and data-driven organizations.
What Learners Say
Real outcomes from students who’ve gained expertise in Big Data Analytics with AI in 4 weeks.