Data Streaming and NLP with PySpark explores streaming data processing and NLP using the power of distributed computing. This course equips learners with the skills to build scalable data-streaming applications and perform advanced NLP tasks on large datasets. Through hands-on labs, you will gain practical experience in processing streaming data and applying NLP techniques using PySpark.
Data Streaming and NLP with PySpark
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Data Streaming and NLP with PySpark
This course is part of PySpark for Data Science Specialization
Instructor: Edureka
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Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
15 hours to complete
3 weeks at 5 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Analyze streaming data to extract insights and trends in real-time applications.
Analyze real-time data streams and apply Spark Streaming techniques for efficient processing.
Develop robust streaming applications using Spark's Structured Streaming for fault-tolerant processing.
Implement NLP techniques to process and analyze textual data efficiently.
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October 2024
Assessments
16 assignments
Taught in English
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This course is part of the PySpark for Data Science Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 5 modules in this course
This module introduces the fundamentals of stream processing with Apache Spark. It covers how data is processed in real-time, the various models for handling data streams, and different architectures used in stream processing systems.
What's included
16 videos4 readings4 assignments1 discussion prompt
In this module, learners will explore Spark Streaming, its core concepts, and the evolution towards Structured Streaming. They will gain an understanding of the DStream abstraction, and the structure of Spark Streaming applications, and explore transformations alongside recent updates.
What's included
8 videos4 readings3 assignments1 discussion prompt
In this module, learners are introduced to the fundamental concepts of Structured Streaming in Spark, with a focus on its programming model, core operations, and management of streaming workflows. They will explore structured data processing in both batch and stream contexts.
What's included
33 videos4 readings4 assignments1 discussion prompt
This module covers the integration of PySpark with Deep Learning and Natural Language Processing (NLP), followed by optimization strategies for PySpark applications. Learners will explore the foundations of deep learning, NLP techniques, and best practices for performance tuning.
What's included
29 videos5 readings4 assignments2 discussion prompts
This module is meant to test how well you understand the different ideas and lessons you've learned in this course. You will undertake a project based on Spark NLP and complete a comprehensive quiz that will assess your confidence and proficiency in Data Streaming and NLP with PySpark.
What's included
1 video1 reading1 assignment1 discussion prompt
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Frequently asked questions
Yes, this course is designed to introduce NLP concepts, making it accessible for beginners. However, a basic understanding of NLP principles will be beneficial.
A basic understanding of Python programming, familiarity with data processing concepts, and some exposure to natural language processing (NLP) will help you succeed in this course.
This course is ideal for data professionals, aspiring data engineers, machine learning enthusiasts, and anyone interested in real-time data processing and NLP applications.