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Course Outline
Day 1: Data Processing and Python Essentials
Session 1: Spark DataFrames and Basic Operations
- Working with Spark DataFrames Implementing Basic Operations
- Groupby and Aggregate Operations
- Handling Timestamps and Dates
- Hands-on Exercise: Data analysis using Spark DataFrames
Session 2: Python Programming for Big Data
- Core Python for Data Handling Using Variables, Lists, and Functions
- Working with Classes and Files
- Integrating APIs and External Data
- Hands-on Exercise: Building a Python project that processes and analyzes data with PySpark
Day 2: Advanced PySpark and Machine Learning
Session 3: Machine Learning with PySpark
- Implementing Machine Learning with Spark MLlib Linear and Logistic Regression
- Random Forest Classification Models
- Hands-on Exercise: Building and evaluating machine learning models using PySpark
Session 4: Clustering and Recommender Systems
- K-means Clustering Theory and Practical Implementation
- Hands-on Exercise: Building a K-means clustering model
- Recommender Systems Building a recommendation engine with Spark MLlib
- Hands-on Exercise: Recommender system project
Session 5: Spark Streaming and NLP
- Real-Time Data Streaming with Spark Implementing real-time data processing
- Hands-on Exercise: Streaming data with Spark
- Natural Language Processing (NLP) with PySpark Implementing basic NLP tasks
- Hands-on Exercise: NLP pipeline using PySpark
14 Hours
Testimonials (1)
practice tasks
