PySpark Online Course

Enroll to learn Scalable Machine learning on Big Data using PySpark and gain hands-on experience along with job opportunities.

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35 hours of comprehensive sessions, including industry-based projects + assessments.


6 Months from purchase




Rs. 11999/-


Any Graduates, Freshers, Engineers, Professionals, Tech Enthusiasts, or Entrepreneurs.


EdifyPath offers top-notch professional courses like PySpark. We bring your subject experts to give you a great learning experience. We come up with a strategic framework of study that can be practically applied in the real day world. We empower techies to achieve more than what they can because our courses are designed with high standards.

We all know that the enterprise industry is innovating focussed products and services which are chiefly driven by data in the backend. This resulted in causing more demand for areas of study such as Machine Learning. This Machine Learning is used to produce predictive insights, build up personalized recommendations, and many more.

Since the inception of the internet, we’ve seen the best advancements in digital technology that helps in transforming already existing business processes, technologies that capture and store, competitive hardware prices to store huge volumes and varied data. In this juncture, traditional single-node data science tools such as R programming and Python fall short to scale up to big data. Spark proves to be the effective solution for the Data Engineers and Data Scientists as a mighty unified engine that is both faster (100 times faster than Hadoop for large-scale data processing) and convenient to use. It also helps Data Practitioners to solve machine learning problems intractably at a greater scale. more...

Course Highlights

   Engaging e-learning platform
   Valued Certification
   Specially designed curriculum
   Top industry experts
   Internship & Placement opportunities
   Prestigious institutional collaborations

     One-on-one student guidance support
     All-time Academic support throughout the course
     Learn at your own pace
   Easy & Convenient learning style
     Hassle-free access to course

Who is this course for?

We welcome Graduates, Freshers, Engineers (any stream), Professionals, Tech Enthusiasts, Entrepreneurs and those who see future in Scalable ML on Big Data Using Pyspark industry. There’s a lot behind learning the science of Scalable ML on Big Data Using Pyspark, because of its vast scope and it is expected to be the most sustaining industry in the coming generations.

Course Objectives

Understand the demand for distributed computing.

Learn an overview of Apache architecture and the concept of Spark Ml pipelines.

Familiarise with Databricks platform and gain hands-on experience on scalable implementations of standard machine learning algorithms.

Learn Apache Spark SQL, Apache Spark DataFrame API and start using them for your big data analysis projects.

Learn to perform exploratory data analysis and feature engineering at scale.

Understand how to accelerate ML flow for managing end-to-end machine learning life cycle, and apply to track of the record, compare parameters and arrive at a final champion model.

Course Curriculum

Need For Distributing Computing
  • Need For Distributed Computing
  • Assessment of Need For Distributed Computing
Apache Spark Architecture
  • Timeline – Big Data Evolution
  • Assessment of Big Data Evolution
  • Apache Spark – Distributed Execution
  • Assessment of Distributed Execution
  • Apache Spark – Data Abstractions
  • Assessment of Data Abstractions
Introduction to Data Bricks
  • Introduction to Databricks
  • Assessment of Introduction to Databricks
  • Creation of Databricks Community Account
  • Assessment of Creation of Databricks Community Account
  • Databricks Workspace
  • Databricks File System (DBFS)
  • Managing Databricks Notebooks - 1
  • Managing Databricks Notebooks - 2
Spark SQL Module
  • Spark SQL - 1
  • Spark SQL – 2
  • Spark SQL – 3
  • Spark SQL – 4
  • Spark SQL – 5
  • Spark SQL – 6
  • Spark SQL – 7
  • Spark SQL – 8
  • Assessment of Spark SQL Module
Spark Data Frames
  • Create DataFrames - 1
  • Create DataFrames - 2
  • Create DataFrames - 3
  • Save DataFrames To Flat Files
  • Basic Operations on DataFrames
  • Advance Operations on DataFrames - 1
  • Assessment of Spark Data Frames Module
  • M-5,S-7
  • M-5,S-8
  • M-5,S-9
Exploratory Data Analysis
  • Exploratory Data Analysis | Session-1
  • Exploratory Data Analysis | Session-2
  • Exploratory Data Analysis | Session-3
  • Exploratory Data Analysis | Session-4
  • Exploratory Data Analysis | Session-5
  • Exploratory Data Analysis | Session-6
  • Mini Project-1
  • Mini Project-2
Feature Engineering
  • Feature Extractors
  • Feature Transformers & Feature Selectors



Hiring Partners

No 1

Online PySpark course with extensive teaching methodology


Specialized modules

Benefits of pursuing Spark ML

  • Become proficient in solving big data as it is compatible with multiple programming languages.
  • Now process large data sets with end-to-end machine learning implementations in simplest ways with remarkable end results.
  • Gain expertise on PySpark API that is required by the industries with respect to querying and data processing and generates statistical reports in a much advanced way.
  • Apache Spark helps data scientists to concentrate on data problems rather than being stuck with hassles around managing distributed infrastructure.
  • Adopt Spark for Data Science as there is a huge demand for Spark professionals due to widespread enterprise.

Mentor - Vijay Gowtham Reddy

Vijay is a Data Science Associate Architect and holds a B.Tech from Amrita University, Bangalore. With an overall experience of a decade in the IT industry, he has implemented advanced analytics projects in the areas of Machine Learning, Natural Language Processing, and Computer Vision. He also leads the AIML Lab and focuses on leveraging an open-source tech stack to build cost-effective tools which accelerate the data science project implementations. He is adept at identifying opportunities to extract value from datasets and solve focused analytical problems to produce innovative solutions. His experience ranges across various domains like Insurance, Telecom, Retail, and Public Sector.

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