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Course Outline
Day 1: Data Processing and Python Essentials
Session 1: Spark DataFrames and Basic Operations
- Utilizing Spark DataFrames for Implementing Basic Operations
- Groupby and Aggregate Functions
- Managing Timestamps and Dates
- Practical Exercise: Conducting Data Analysis Using Spark DataFrames
Session 2: Python Programming for Big Data
- Essential Python for Data Management, Including Variables, Lists, and Functions
- Working with Classes and File Operations
- Integrating APIs and External Data Sources
- Practical Exercise: Developing a Python Project to Process and Analyze Data Using PySpark
Day 2: Advanced PySpark and Machine Learning
Session 3: Machine Learning with PySpark
- Implementing Machine Learning Models with Spark MLlib, Including Linear and Logistic Regression
- Random Forest Classification Techniques
- Practical Exercise: Constructing and Evaluating Machine Learning Models Using PySpark
Session 4: Clustering and Recommender Systems
- K-means Clustering Theory and Practical Application
- Practical Exercise: Developing a K-means Clustering Model
- Recommender Systems: Building a Recommendation Engine with Spark MLlib
- Practical Exercise: Recommender System Project
Session 5: Spark Streaming and NLP
- Real-Time Data Streaming with Spark: Implementing Real-Time Data Processing Solutions for government
- Practical Exercise: Managing Streaming Data with Spark
- Natural Language Processing (NLP) with PySpark: Conducting Basic NLP Tasks
- Practical Exercise: Creating an NLP Pipeline Using PySpark
14 Hours
Testimonials (1)
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