Python and Spark for Big Data for Banking (PySpark) Training Course

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