Ds4b 101-p- Python For Data Science Automation -
System schedulers like Windows Task Scheduler or Cron jobs run Python scripts at specific intervals.
The curriculum of Python for Data Science Automation is built on four functional pillars, designed to take a practitioner from foundational scripting to enterprise-grade automation. 1. Business-Centric Data Ingestion and Transformation
The course is structured around a real-world business project—typically developing a demand forecasting or customer churn prediction system—which is taken from raw data to a fully automated deployment. 1. Advanced Python for Data Manipulation
, providing the prerequisite knowledge for advanced topics like Machine Learning and API development. Business Science University DS4B 101-P- Python for Data Science Automation
Writing custom wrappers around REST APIs to pull real-time business data using the requests library.
Forecasting is a core business need, and Sktime—a scikit‑learn‑compatible library for time series analysis—is the tool of choice in this course.
databases and set up a professional development environment using Part 2: Time Series Forecasting : Introduces advanced time series analysis using System schedulers like Windows Task Scheduler or Cron
What (SQL, Salesforce, APIs, local files) do you work with most?
openpyxl and XlsxWriter let Python format sheets, add formulas, and generate charts programmatically. 4. Task Scheduling and Orchestration True automation requires hands-free execution.
| Feature | DS4B 101-P | DataCamp / Codecademy | Free YouTube (Corey Schafer) | | :--- | :--- | :--- | :--- | | | Business Automation | Syntax & Libraries | Theory & Isolated Scripts | | Project Structure | End-to-end (Scraping to Email) | Isolated Exercises | Tutorial-style | | Error Handling | Deep (Production level) | Minimal | Rare | | Orchestration | Airflow / Prefect | None | None | | Price | $$ (Premium) | $ (Subscription) | Free | learners will be able to:
: Communicate findings effectively to stakeholders. Key Skills : Interactive plotting with Plotly .
Here is the "story" or professional narrative of this course, following the journey from a manual analyst to an automation expert. 🏗️ The Problem: The "Excel Trap"
The course culminates in a real-world project: . Connect : Link Python directly to your data sources. Analyze : Automatically calculate KPIs and generate charts.
Theory without practice is limited. DS4B 101-P uses a realistic, engaging scenario: . Management has tasked the team with expanding forecast reporting capabilities by customers, products, and various time durations. This requires a level of flexibility not currently possible with manual business processes. Your mission is to learn Pandas and the Python ecosystem to automate this forecasting project.
After completing the course, learners will be able to:
