Module 2.1: Data Extraction Techniques

  • Connecting to databases (SQL basics)
  • Working with APIs and web services
  • Extracting data from Excel and CSV files
  • Understanding EDI and data exchange formats
  • Automating data extraction processes

Module 2.2: Data Cleaning and Preparation

  • Handling missing values and outliers
  • Data normalization and standardization
  • Removing duplicates and errors
  • Data type conversion
  • Creating calculated fields and derived metrics

Module 2.3: Data Integration and ETL

  • Extract, Transform, Load (ETL) concepts
  • Combining data from multiple sources
  • Master data management principles
  • Data warehousing basics
  • Introduction to data pipelines

Hands-On Exercise:

  • Clean a messy dataset with missing values and errors
  • Integrate order data with inventory and shipping data
  • Build an ETL workflow using Excel/Power Query

Tools Covered: Excel Power Query, SQL, Python (pandas library)

PreviousNext