Analytical Engineering

Data & Automation

Module 5: Processing large datasets and automating repetitive workflows.

SYSTEM: CSV_PROCESSING

41 // Handling CSV Data

Most business data (Sales, User Lists, Logistics) is stored in CSV files. Python's built-in csv module allows us to read thousands of rows in seconds.

import csv

with open('sales.csv', mode='r') as file:
    reader = csv.DictReader(file)
    for row in reader:
        print(f"Product: {row['item']} | Price: {row['price']}")
  

SYSTEM: ANALYSIS_TOOLS

42-49 // Statistics & Patterns

Lesson 42: Math & Stats — Using the statistics module for averages and trends.

Lesson 43: List Filtering — Using filter() and map() to clean messy data.

Lesson 44: Introduction to NumPy — Understanding high-speed numerical arrays.

Lesson 45: Introduction to Pandas — The industry standard for Dataframes.

CAPSTONE_PROJECT_05

50 // Project: The Automated Sales Auditor

Create a script that reads a list of transactions, calculates the total revenue, and identifies the most expensive item sold.

Market insight: This is the foundation of "Business Intelligence." Companies pay premium rates for developers who can turn raw data into readable profit reports.

START FINAL MODULE: SYSTEM DEPLOYMENT →