Here’s a professional course write-up for DS4B 101-P: Python for Data Science Automation, suitable for a syllabus, course catalog, or learning platform.
Module 2: Data Acquisition & Web Scraping
Data rarely lives in a perfect CSV file. In this module, you learn to automate data ingestion from:
- APIs: Using
requestsandjsonto pull live data (e.g., Stock prices, Weather data). - Web Scraping: Using
BeautifulSoupandSeleniumto extract data from websites that lack APIs. - Databases: Writing SQL queries inside Python scripts to pull data from PostgreSQL or MySQL automatically.
8) Prerequisites & assumed skills
- Basic statistics and familiarity with spreadsheets or SQL.
- No prior Python required; course includes quick Python ramp-up module.
16) Suggested assets & media
- Short promo video (2 min) showing dashboards, automated pipelines running, instructor walkthrough.
- Code repo template and Dockerized starter project.
- Walkthrough GIFs for key moments (scheduling, report generation).
- Slide deck and printable cheat-sheets (pandas, Prefect, Docker commands).
Final Verdict: Is DS4B 101-P Worth It?
Yes. If you are serious about data science as a career rather than a hobby, DS4B 101-P: Python for Data Science Automation is one of the highest ROI courses available.
Most bootcamps teach you how to explore data. DS4B 101-P teaches you how to deploy data. It transforms you from a "script runner" into a "process builder."
If you are tired of copying and pasting the same code, waking up early to click "Run," or manually emailing Excel sheets, invest in this course. The 20 hours you invest in learning automation will save you 200 hours of manual labor next year.
Ready to automate your workflow? Check out the official DS4B 101-P course page at Business Science to see current enrollment dates and discounts.
Disclaimer: This article is an independent review. Always check the official DS4B website for the most current curriculum and pricing.
Business Science University's DS4B 101-P course instructs professionals on automating business processes using Python, covering Pandas, SKTime, and Plotnine for data analysis and visualization. The 30-hour curriculum focuses on building automated reporting systems, culminating in a comprehensive business process automation project. For more information, visit Business Science University Business Science University
DS4B 101-P: Python for Data Science Automation is a project-based course from Business Science University designed to teach data analysts how to convert manual business processes into automated Python workflows. The course follows a hypothetical bicycle manufacturer's data team to build a large-scale forecasting and reporting system. Core Curriculum Structure The course is simplified into three primary modules: Data Analysis Foundations
Pandas in Depth: Over 5 hours of training focused on complex data wrangling.
SQL Databases: Learn to work with transactional databases by creating and managing your own SQLite database.
Workflow Design: Using VSCode as a professional development environment to build custom Python packages that house your automation functions. Time Series Forecasting
Sktime Library: Utilizing state-of-the-art forecasting tools to handle complex time-series data.
Automation Logic: Developing reusable functions that simplify repetitive forecasting tasks. Reporting Automation
Visualizations: Creating report-quality plots using the plotnine library.
PaperMill: Automating templatized Jupyter Notebook reports and converting them to HTML and PDF formats.
End-to-End Workflow: Integrating the forecasting results back into SQL databases to finalize the automation loop. Target Audience
BI Professionals: Users of Excel, Power BI, or Tableau looking to scale their capabilities.
R Users: Data scientists familiar with the R language (e.g., from the DS4B 101-R course) who need to learn Python for business integration.
Beginners: Analysts new to Python who want a business-focused introduction rather than a general computer science approach. Key Features
Project-Based Learning: Students build a real-world enterprise-grade software package.
Bonus Modules: Often includes instruction on automating scripts with Windows Task Scheduler and Mac Automator.
No Prerequisites: Designed to take "serious beginners" through the entire process from scratch.
10) Pricing & packaging suggestions
- Individual self-paced: $149–249 (one-time).
- Instructor-led cohort: $599–999.
- Corporate license: custom pricing per seat; include private Slack/office hours and project reviews.
- Add-ons: resume review, 1:1 mentorship, advanced MLOps module.
Module 6: Scheduling & Orchestration
You have the script; now you need the robot to run it. This module covers three levels of scheduling:
- Local: Cron jobs (Mac/Linux) and Task Scheduler (Windows).
- Workflow: Introduction to
PrefectandApache Airflowfor Directed Acyclic Graphs (DAGs). - Cloud: Deploying automation scripts to AWS Lambda or EC2 instances.
9) Time commitment & format options
- Instructor-led: 6 weeks, 2 sessions/week (90 min each), ~6–8 hours/week including exercises.
- Self-paced: 40–60 hours total, modular videos, exercises, and project milestones.
- Corporate bootcamp variant: condensed 3-day hands-on workshop + follow-up capstone.
