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
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
Shareable certificate
Add to your LinkedIn profile
Taught in English
Recently updated!
September 2025
38 practice exercises
Advance your subject-matter expertise
- Learn in-demand skills from university and industry experts
- Master a subject or tool with hands-on projects
- Develop a deep understanding of key concepts
- Earn a career certificate from EDUCBA
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
PySpark, Data Transformation, Python Programming, Data Processing, Apache Spark, Data Pipelines, SQL, MySQL, Debugging, Data Manipulation, and Distributed Computing
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
PySpark, Predictive Modeling, Random Forest Algorithm, Applied Machine Learning, Regression Analysis, Unsupervised Learning, Apache Spark, Supervised Learning, Statistical Machine Learning, Machine Learning Algorithms, Classification And Regression Tree (CART), Predictive Analytics, and Data Pipelines
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
PySpark, Text Mining, Advanced Analytics, Data Processing, Data Manipulation, Simulation and Simulation Software, Risk Analysis, Data Transformation, Unstructured Data, Apache Spark, Big Data, Statistical Modeling, Predictive Modeling, Marketing Analytics, Image Analysis, Customer Insights, Data Mining, and Customer Analysis
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
Apache Spark, Apache Maven, Scala Programming, Real Time Data, Object Oriented Programming (OOP), Systems Integration, Data Structures, Apache Hadoop, Data Processing, and Scalability
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
Apache Spark, Extract, Transform, Load, PySpark, Development Environment, Java Platform Enterprise Edition (J2EE), Apache Hadoop, Data Import/Export, Data Store, Data Transformation, Data Manipulation, MySQL, Data Pipelines, Software Installation, and System Configuration
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
Apache Spark, Data Processing, Distributed Computing, Data Transformation, PySpark, Data Manipulation, Big Data, JSON, Performance Tuning, and Data Pipelines
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
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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.
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Financial aid available,