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Mastering Big Data Analytics: PySpark, Scala, AWS, Web Scraping In Islamabad, Pakistan

Mastering Big Data Analytics: PySpark, Scala, AWS, Web Scraping

Learn, build, and execute big data strategies with Scala and Spark, PySpark and AWS, data scraping and data mining with Python, and master MongoDB .


What you’ll learn

  • Introduction and importance of this course in this day and age
  • Approach all essential concepts from the beginning
  • Clear unfolding of concepts with examples in Python,Scrapy, Scala, PySpark and MongoDB
  • All theoretical explanations followed by practical implementations
  • Data Scraping & Data Mining for Beginners to Pro with Python
  • Master Big Data with Scala and Spark
  • Master Big Data With PySpark and AWS
  • Mastering MongoDB for Beginners
  • Building your own AI applications

Course content

Module 1: Data Scraping & Data Mining with Python

  • Introduction to Data Scraping
  • Requests Library and Extracting Authors
  • Beautiful Soup 4 (BS4) Introduction
  • Extracting Quotes from a Website
  • CSS Selectors for Data Extraction
  • Scrapy Framework Introduction
  • Running and Writing Spiders in Scrapy
  • Exporting Extracted Data
  • Handling Pagination and Next Page URLs
  • Working with Forms and Logins in Scrapy

Module2: Scala & Spark- Master Big Data with Scala and Scarp

  • Introduction to Scala and Spark
  • Variables, Arithmetic Operations, and Data Types in Scala
  • Control Statements and Loops
  • Functions and Classes in Scala
  • Introduction to Spark RDDs
  • Transformations and Actions on RDDs
  • Introduction to Spark DataFrames
  • Operations on Spark DataFrames
  • Spark DF Aggregations and Group By
  • Introduction to Spark SQL

Module3: PySpark &AWS: Mater Big Data With Pyspark and AWS

  • Introduction to Big Data and PySpark
  • Setting up PySpark and AWS Environment
  • Overview of Hadoop and Spark Ecosystems
  • Running PySpark on Local and Cluster Modes
  • Working with RDDs and DataFrames in PySpark
  • Spark Streaming and Real-time Data Processing
  • ETL Pipeline using PySpark and AWS Services
  • Collaborative Filtering for Recommendations
  • Change Data Capture and Replication in AWS
  • Building an End-to-End Data Pipeline

Module5: Final Project – Building a Recommender System with PySpark, AWS, and MongoDB

  • Understanding Collaborative Filtering
  • Explicit vs Implicit Ratings
  • Expected Results and Dataset Overview
  • Launching an EC2 Instance
  • Installing Necessary Packages and Libraries
  • Configuring Spark and PySpark on AWS
  • Extracting and Transforming Data
  • Loading Data into MongoDB for Storage
  • Handling Data Anomalies and Missing Values
  • Overview of Collaborative Filtering
  • Implementing ALS Algorithm with PySpark
  • Hyperparameter Tuning and Cross-Validation
  • Splitting Data for Training and Testing
  • Evaluating Model Performance
  • Generating Recommendations for Users
  • Storing User and Item Profiles in MongoDB
  • Retrieving User Information for Personalized Recommendations
  • Deploying the Recommender System on AWS
  • Handling Scalability and Performance Optimizatio

 

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KEY FEATURES

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Flexible Classes Schedule

Online Classes for out of city / country students

Unlimited Learning - FREE Workshops

FREE Practice Exam

Internships Available

Free Course Recordings Videos

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