Course Code: pysparkbespoke
Duration: 21 hours
Prerequisites:
  • Basic Python programming and SQL knowledge
  • Understanding of DataFrames and data manipulation

Audience

  • Data scientists
  • Data analysts
  • Data engineers
Overview:

PySpark is the Python API for Apache Spark, an open-source distributed computing framework designed to process large datasets efficiently. PySpark allows developers and data scientists to leverage Spark's powerful data processing capabilities using Python, a widely used programming language for data analysis and machine learning.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data professionals who wish to strengthen and deepen their capabilities and knowledge in Python for data mining and analytics.

By the end of this training, participants will be able to:

  • Leverage PySpark for data mining, analysis, and database exploitation in Stratio.
  • Build and optimize PySpark pipelines for large-scale data processing.
  • Implement machine learning models for predictive analytics in banking.
  • Apply PySpark in real-world banking scenarios such as credit scoring, fraud detection, and customer segmentation.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.
Course Outline:

Introduction

Overview of the Stratio Platform

  • Introduction to Stratio for Government, Exploitation, Analysis, and Modeling
  • Stratio architecture and integration with PySpark

Introduction to PySpark

  • Understanding the Spark ecosystem
  • Core concepts of PySpark (RDD, DataFrames, Datasets)
  • Cluster computing and parallelism in PySpark

Setting Up PySpark in Stratio

  • Installing and configuring PySpark within the Stratio environment
  • Connecting PySpark to data sources (HDFS, databases, cloud storage)
  • Stratio integration with PySpark for data mining

Working with DataFrames

  • Loading data from multiple sources (CSV, JSON, Parquet, etc.)
  • Data exploration: schema, data types, and basic queries
  • Practical exercises: DataFrame manipulations in PySpark

Data Transformation Techniques

  • Filtering, grouping, and aggregating data
  • Handling missing data and data cleaning
  • Practical exercises: Transforming and analyzing datasets using PySpark

Advanced SQL Queries in PySpark

  • Introduction to PySpark SQL and SparkSession
  • Running SQL queries on structured data in PySpark
  • Joins, unions, and data combinations
  • Practical use cases: Database exploitation and integration

Introduction to Spark MLlib for Analytics

  • Overview of machine learning pipelines in Spark
  • Feature extraction and data preprocessing
  • Practical exercises: Building basic machine learning models with PySpark MLlib

Data Mining Use Case: Credit Risk Analysis

  • Practical walkthrough: Using PySpark for credit risk analysis
  • Analyzing historical data to develop credit scoring models
  • Real-time implementation with Stratio

Optimizing PySpark Jobs

  • Best practices for writing efficient PySpark code
  • Partitioning, caching, and broadcasting
  • Optimizing joins and shuffles for large datasets
  • Practical exercises: Tuning PySpark jobs for performance

Advanced Machine Learning with PySpark

  • Regression, classification, and clustering techniques
  • Model evaluation and selection (cross-validation, hyperparameter tuning)
  • Practical use case: Predictive modeling for loan default prediction

Real-time Data Streaming with PySpark

  • Introduction to PySpark Streaming
  • Setting up real-time data pipelines on Stratio
  • Practical exercises: Streaming data processing for real-time analytics

Data Mining Use Case: Customer Segmentation

  • Segmenting bank customers using clustering algorithms
  • Hands-on project: Implementing customer segmentation models in PySpark

Data Mining and Analytics in Banking

  • Defining business objectives and problem statements
  • Data extraction, transformation, and loading (ETL) with PySpark
  • Descriptive and prescriptive analysis in PySpark

Fraud Detection with PySpark

  • Identifying fraudulent transactions using machine learning models
  • Deploying PySpark models in Stratio for real-time fraud detection

Best Practices for PySpark Projects

  • Version control, collaboration, and documentation
  • Scaling PySpark jobs for large banking datasets
  • Error handling and troubleshooting in distributed environments

Summary and Next Steps