Spark Predictive Analytics Professional (SPAP)
(40 Hours Program)
- Duration: 40 Hours (1 Week)
- Format: Hands-on + Project Based
- Outcome: Spark Predictive Analytics Professional
- Focus: Apache Spark, MLlib & Predictive Analytics
- Includes: Labs + Capstone Project + Certification Assessment
Module-wise Curriculum (40 Hours)
Module 1: Introduction to Big Data & Apache Spark (4 Hours)
- Big Data Concepts
- Hadoop vs Spark
- Spark Architecture
- Spark Components
- Spark Ecosystem
- Spark Installation
- Spark Cluster Modes
- Spark Shell
Understand distributed computing using Apache Spark.
Module 2: Spark Core (4 Hours)
- RDD Fundamentals
- Transformations
- Actions
- Lazy Evaluation
- Caching & Persistence
- Broadcast Variables
- Accumulators
- Hands-on Lab
Master Spark Core programming concepts.
Module 3: Spark SQL (4 Hours)
- SparkSession
- Reading CSV, JSON & Parquet Files
- DataFrame Operations
- SQL Queries
- Temporary Views
- Data Cleaning
- Schema Management
Work efficiently with structured big data using Spark SQL.
Module 4: Data Processing (4 Hours)
- Filtering
- Aggregation
- GroupBy Operations
- Window Functions
- Joins
- User Defined Functions (UDFs)
- Performance Optimization Lab
Process and transform large datasets efficiently.
Module 5: Spark MLlib Fundamentals (4 Hours)
- MLlib Overview
- Machine Learning Pipelines
- Transformers
- Estimators
- Feature Engineering
- Data Splitting
Build scalable machine learning pipelines using Spark MLlib.
Module 6: Predictive Analytics (4 Hours)
- Regression Models
- Classification Models
- Clustering
- Recommendation Systems Basics
- Cross Validation
- Hyperparameter Tuning
Develop predictive machine learning models with Spark.
Module 7: Model Optimization (4 Hours)
- Feature Selection
- Model Evaluation Metrics
- ROC & AUC
- Precision & Recall
- Confusion Matrix
- Model Persistence
Module 8: Real-Time Predictive Analytics (4 Hours)
- Spark Structured Streaming
- Stream Processing
- Real-Time Predictions
- Kafka Integration
- Batch vs Streaming
- Deployment Concepts
Prerequisites
- Basic Python Programming
- SQL Fundamentals
- Basic Statistics
- Introductory Machine Learning Concepts (Recommended)
Module 9: End-to-End Predictive Analytics Project (4 Hours)
- Business Problem Definition
- Data Collection
- Data Cleaning
- Feature Engineering
- Model Development
- Model Evaluation
- Prediction Pipeline
- Performance Tuning
Module 10: Certification Assessment (4 Hours)
- Practical Assessment
- Scenario-Based Questions
- Project Presentation
- Best Practices
- Interview Preparation
- Certification Examination
Hands-on Labs
- Customer Churn Prediction
- House Price Prediction
- Credit Risk Analysis
- Sales Forecasting
- Fraud Detection
- Recommendation System
- Predictive Maintenance
- Customer Segmentation
Learning Outcomes
- Understand Apache Spark architecture and distributed data processing.
- Build scalable data pipelines using Spark SQL and DataFrames.
- Perform feature engineering for predictive analytics.
- Develop, evaluate and optimize machine learning models using Spark MLlib.
- Implement batch and streaming predictive analytics workflows.
- Deliver end-to-end predictive analytics solutions for production environments.
Contact Us
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courses@metatorch.org
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