This specialization provides a complete learning pathway in Apache Spark and Python (PySpark) for big data analytics, machine learning, and scalable data processing. Learners will begin with foundational Python and PySpark techniques, advance to predictive modeling and clustering, and explore advanced data workflows including ETL pipelines, streaming, and real-time processing. By the end, participants will be equipped with practical skills to design, build, and optimize distributed applications for data engineering, analytics, and business intelligence.
Spark and Python for Big Data with PySpark Specialization
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Spark and Python for Big Data with PySpark Specialization
Spark and Python for Big Data with PySpark. Build scalable data workflows and predictive models using Spark and Python.
Instructor: EDUCBA
Included with 
Get in-depth knowledge of a subject
Beginner level
Recommended experience
1 month at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
Get in-depth knowledge of a subject
Beginner level
Recommended experience
1 month at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
What you'll learn
Apply PySpark to build, optimize, and evaluate distributed data processing workflows.
Design and execute predictive machine learning models for large-scale analytics.
Construct ETL pipelines, real-time streaming applications, and advanced big data solutions with Spark.
Overview
Skills you'll gain
What’s included
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Taught in English
Recently updated!
September 2025
38 practice exercises
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Specialization - 6 course series
What you'll learn
Recall Python syntax and identify key PySpark components for data processing.
Apply RDD transformations, joins, and JDBC integration with MySQL.
Build scalable pipelines like word count and debug PySpark applications.
Skills you'll gain
Category: PySpark
Category: Python Programming
Category: Data Transformation
Category: Data Processing
Category: MySQL
Category: SQL
Category: Distributed Computing
Category: Data Pipelines
Category: Programming Principles
Category: Data Manipulation
Category: Debugging
Category: Apache Spark
What you'll learn
Build and evaluate regression models in PySpark using linear, GLM, and ensemble methods.
Apply logistic regression, decision trees, and Random Forests for classification.
Implement K-Means clustering and assess scalable ML workflows with PySpark.
Skills you'll gain
Category: Machine Learning Algorithms
Category: Predictive Modeling
Category: PySpark
Category: Data Pipelines
Category: Applied Machine Learning
Category: Regression Analysis
Category: Statistical Machine Learning
Category: Unsupervised Learning
Category: Supervised Learning
Category: Classification And Regression Tree (CART)
Category: Predictive Analytics
Category: Apache Spark
Category: Random Forest Algorithm
What you'll learn
Apply RFM analysis and K-Means clustering for customer segmentation.
Extract and analyze textual data using OCR with PySpark DataFrames.
Build and interpret Monte Carlo simulations for uncertainty modeling.
Skills you'll gain
Category: Text Mining
Category: Customer Insights
Category: Marketing Analytics
Category: Customer Analysis
Category: Data Manipulation
Category: Predictive Modeling
Category: Data Processing
Category: Big Data
Category: PySpark
Category: Image Analysis
Category: Unstructured Data
Category: Data Transformation
Category: Data Mining
Category: Simulation and Simulation Software
Category: Apache Spark
Category: Statistical Modeling
Category: Risk Analysis
Category: Advanced Analytics
What you'll learn
Apply Scala fundamentals including variables, functions, and advanced concepts.
Implement Spark RDD operations, streaming, and fault-tolerant pipelines.
Build real-time big data solutions integrating Spark with external systems.
Skills you'll gain
Category: Data Structures
Category: Scalability
Category: Data Processing
Category: Object Oriented Programming (OOP)
Category: Apache Hadoop
Category: Scala Programming
Category: Systems Integration
Category: Real Time Data
Category: Apache Maven
Category: Apache Spark
What you'll learn
Install and configure PySpark, Hadoop, and MySQL for ETL workflows.
Build Spark applications for full and incremental data loads via JDBC.
Apply transformations, handle deployment issues, and optimize ETL pipelines.
Skills you'll gain
Category: Apache Spark
Category: Extract, Transform, Load
Category: PySpark
Category: Data Store
Category: System Configuration
Category: Data Pipelines
Category: Development Environment
Category: Java Platform Enterprise Edition (J2EE)
Category: Data Manipulation
Category: MySQL
Category: Data Transformation
Category: Apache Hadoop
Category: Software Installation
Category: Data Import/Export
What you'll learn
Describe Spark architecture, core components, and RDD programming constructs.
Apply transformations, persistence, and handle multiple file formats in Spark.
Develop scalable workflows and evaluate Spark applications for optimization.
Skills you'll gain
Category: Performance Tuning
Category: PySpark
Category: Data Processing
Category: Data Manipulation
Category: Data Transformation
Category: Data Pipelines
Category: Distributed Computing
Category: Apache Spark
Category: JSON
Category: Big Data
Category: Scala Programming
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Frequently asked questions
Learners can expect to complete the Specialization in approximately 11 to 12 weeks, dedicating 3–4 hours per week. This flexible pace is designed to accommodate working professionals and students alike, allowing steady progress through foundational Python and PySpark skills, advanced data processing, predictive machine learning, and real-world ETL pipeline development. By the end of the program, learners will have gained both conceptual understanding and hands-on experience, ensuring they are well-prepared to tackle real-world big data challenges.
Learners should have a basic understanding of Python programming and foundational concepts in data analysis. Prior exposure to databases or machine learning will be helpful but is not mandatory.
Yes, it is recommended to follow the courses in sequence. The curriculum is structured to build progressively—from core Python and PySpark foundations to machine learning, advanced data workflows, and real-world big data applications—ensuring a smooth learning journey.
More questions
Financial aid available,