PySpark
- Offered byThe knowledge academy
PySpark at The knowledge academy Overview
Duration | 1 year |
Start from | Start Now |
Total fee | ₹1.01 Lakh |
Mode of learning | Online |
Official Website | Go to Website  |
Credential | Certificate |
- Overview
- Highlights
- Course Details
- Curriculum
- Admission Process
PySpark at The knowledge academy Highlights
- Earn a certificate after completion of course
- Engage in activities, and communicate with your trainer and peers
PySpark at The knowledge academy Course details
Data Engineers
Big Data Analysts
Data Scientists
Machine Learning Engineers
Software Developers
Python Developers
Solution Architects
System Administrators
Database Administrators
To provide a comprehensive understanding of PySpark fundamentals
To cover advanced topics such as big data analytics using PySpark
To offer hands-on experience in applying PySpark for data processing and analytics
To equip professionals with the skills to efficiently handle large-scale data processing tasks
To empower delegates to leverage PySpark for machine learning applications
PySpark Training in India is a crucial component in the arsenal of data scientists, business analysts, and professionals across various industries
PySpark, a Python API for Apache Spark, is a powerful framework for big data processing and analytics
Its relevance lies in its ability to handle large-scale data processing tasks efficiently, making it an essential skill for those navigating the dynamic landscape of data science
Professionals aiming to master PySpark include data scientists, data engineers, and analysts dealing with big data
PySpark at The knowledge academy Curriculum
Module 1: Introduction to PySpark
What is PySpark
Environment
Spark Dataframes
Reading Data
Writing Data
MLlib
Module 2: Installation
Using PyPI
Using PySpark Native Features
Using Virtualenv
Using PEX
Dependencies
Module 3: DataFrame
DataFrame Creation
Viewing Data
Applying a Function
Grouping Data
Selecting and Accessing Data
Working with SQL
Get () Method
Module 4: Setting Up a Spark Virtual Environment
Understanding the Architecture of Data-Intensive Applications
Installing Anaconda
Setting a Spark Powered Environment
Building App with PySpark
Module 5: Building Batch and Streaming Apps with Spark
Architecting Data-Intensive Apps
Build a Reliable and Scalable Streaming App
Process Live Data with TCP Sockets
Analyzing the CSV Data
Exploring the GitHub World
Previewing App
Module 6: Learning from Data Using Spark
Classifying Spark MLlib Algorithms
Spark MLlib Data Types
Clustering the Twitter Dataset
Build Machine Learning Pipelines
PySpark at The knowledge academy Admission Process
Important Dates
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