Spark and Python for Big Data with PySpark

EDUCBA via Coursera Specialization

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Provider

Coursera Specialization
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Duration & workload

4 weeks, 10 hours a week

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Overview

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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.

Syllabus

Course 1: PySpark & Python: Hands-On Guide to Data Processing
Course 2: PySpark: Apply & Evaluate Predictive ML Models
Course 3: PySpark: Apply & Analyze Advanced Data Processing
Course 4: Apache Spark with Scala: Master Data Building & Analysis
Course 5: Apache Spark: Design & Execute ETL Pipelines Hands-On
Course 6: Apache Spark: Apply & Evaluate Big Data Workflows

Courses

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8 hours 44 minutes
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This course provides a complete journey into Apache Spark with Scala, designed for learners who want to analyze, design, implement, and evaluate big data applications. Beginning with the foundations of Spark architecture and Scala programming, learners will explore variables, functions, collections, and advanced Scala concepts such as traits, abstract classes, and exception handling. The course then advances into Spark RDD operations, streaming, windowing, and checkpointing, helping learners apply distributed transformations and implement real-time data pipelines. Finally, learners will construct integrated projects using Maven, connect Spark to external systems like Twitter APIs, and evaluate the impact of Hadoop 1.x vs 2.x in managing resources for scalable applications. By the end of this course, participants will be able to apply Scala fundamentals, differentiate RDD transformations and actions, implement Spark Streaming with fault tolerance, and construct end-to-end real-time big data solutions—positioning themselves for roles in data engineering, big data analytics, and real-time application development.
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4 hours 23 minutes
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This course introduces beginners to the foundational and intermediate concepts of distributed data processing using Apache Spark, one of the most powerful engines for large-scale analytics. Through two progressively structured modules, learners will identify Spark’s architecture, describe its core components, and demonstrate key programming constructs such as Resilient Distributed Datasets (RDDs). In Module 1, learners will recognize the principles behind Spark’s distributed computing model and illustrate basic RDD transformations. In Module 2, they will apply advanced transformation logic, implement persistence strategies, and differentiate between file formats like CSV, JSON, Parquet, and Avro for efficient data handling. By the end of the course, learners will be able to analyze Spark applications for optimization, evaluate storage strategies, and develop scalable data processing workflows using core Spark APIs. The course blends conceptual clarity with hands-on examples to equip learners for real-world big data challenges.
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4 hours 28 minutes
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This hands-on course equips learners with the skills to design, build, and manage end-to-end ETL (Extract, Transform, Load) workflows using Apache Spark in a real-world data engineering context. Structured into two comprehensive modules, the course begins with foundational setup, guiding learners through the installation of essential components such as PySpark, Hadoop, and MySQL. Participants will learn how to configure their environment, organize project structures, and explore source datasets effectively. As the course progresses, learners will develop Spark applications to perform full and incremental data loads using JDBC integration with MySQL. Through practical examples, they will apply transformation logic using Spark SQL, filter data based on business rules, and handle common pitfalls such as type mismatches and folder structure issues during Spark deployment. By the end of the course, learners will be able to construct, execute, and optimize Spark-based ETL pipelines that are scalable and production-ready, empowering them to contribute effectively in real-world data engineering roles.
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5 hours 21 minutes
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This beginner-level course is designed to introduce learners to the powerful combination of Python and Apache Spark (PySpark) for distributed data processing and analysis. Through structured lessons and real-world examples, learners will recall foundational Python syntax, identify key elements of PySpark, and demonstrate the use of core Spark transformations and actions using Resilient Distributed Datasets (RDDs). As the course progresses, learners will apply advanced data handling techniques such as joins and data integration using JDBC with MySQL, and construct scalable data pipelines like word count using transformation chains. Each module emphasizes a blend of conceptual understanding and practical coding experience, enabling learners to analyze, debug, and evaluate their PySpark applications efficiently. By the end of the course, learners will have gained hands-on proficiency in building distributed data workflows and be prepared to advance toward more complex data engineering and big data analytics challenges.
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3 hours 2 minutes
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This course equips learners with the skills to apply and analyze advanced data processing techniques using PySpark, the Python API for Apache Spark. Designed for data professionals with foundational Python and PySpark knowledge, the course explores real-world use cases including customer segmentation, text mining, and stochastic modeling. Learners will begin by applying RFM (Recency, Frequency, Monetary) analysis and K-Means clustering to segment customers based on behavioral patterns. The course then advances to extracting textual data from images and PDFs using Optical Character Recognition (OCR) and PySpark’s DataFrame operations. Finally, learners will construct and interpret Monte Carlo simulations to model probability and uncertainty in data-driven scenarios. Throughout the course, students will engage in hands-on exercises, real-time demonstrations, and practical quizzes that reinforce both conceptual understanding and technical proficiency. By the end of this course, learners will be able to develop scalable, efficient data workflows using PySpark for business intelligence, analytics, and simulation modeling.
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5 hours 15 minutes
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This intermediate-level course empowers learners to apply, analyze, and evaluate machine learning models using Apache PySpark’s distributed computing framework. Designed for data professionals familiar with Python and basic ML concepts, the course explores real-world implementation of both regression and classification techniques, along with unsupervised clustering. In Module 1, learners will construct linear and generalized regression models, apply ensemble regressors such as Random Forests, and evaluate predictive performance using metrics like RMSE and R-squared. The module concludes with an in-depth look at logistic regression for binary classification tasks. Module 2 builds on these foundations to cover multi-class classification using multinomial logistic regression and decision trees. Learners will also evaluate ensemble models like Random Forests for robust classification, and explore K-Means clustering for unsupervised learning problems. Each concept is reinforced with guided PySpark code demonstrations, predictive workflows, and practical evaluations using large datasets. By the end of the course, learners will be able to design, execute, and critically assess machine learning models in PySpark for scalable data analytics solutions.