Ds4b 101-p- Python For Data Science Automation |work| 📥 🔖

Title: The Midnight Report

Lena stared at her screen. It was 11:47 PM, and her CFO wanted the quarterly logistics report by 8 AM. The data was scattered across three Excel files, two CSV exports from the warehouse, and a messy JSON from the ERP system.

She used to do this manually: open each file, copy-paste, write formulas, fix date formats, and cry over merged cells. But not anymore.

She opened Jupyter Lab and launched her DS4B 101-P toolkit.

Step 1 – Automate the messy imports.

import pandas as pd
import glob
files = glob.glob("data/*.xlsx")
df_list = [pd.read_excel(f, skiprows=2) for f in files]
warehouse = pd.concat(df_list, ignore_index=True)

Step 2 – Clean with pipelines.
She wrote a reusable function to strip spaces, convert dates, and flag outliers — all from her automation module.

Step 3 – Enrich using APIs.
A quick requests.get() pulled live fuel surcharge rates into a new column. DS4B 101-P- Python for Data Science Automation

Step 4 – Schedule the logic.
Using schedule and a simple logging function, she set the script to run every night at midnight. Tonight was just a test run.

At 11:59 PM, she ran the final cell. The script:

Merged warehouse, cost, and vendor data.
Calculated late delivery penalties automatically.
Exported a clean summary to final_report.xlsx.
Sent a Teams alert via webhook: “Report ready. 0 errors.”

Lena closed her laptop at 12:08 AM. No caffeine. No rage. No manual VLOOKUP hell.

The CFO never knew how messy the data was. And that was the point. Title: The Midnight Report Lena stared at her screen

Automation wasn’t just about saving time — it was about taking back her evenings.

End.

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

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.

Overview

A comprehensive feature article introducing DS4B 101-P, a beginner-friendly course teaching Python for automating data-science tasks. Covers course purpose, target audience, curriculum breakdown, learning outcomes, instructional approach, hands-on projects, tools/libraries used, assessment, expected time commitment, pricing/packaging options, testimonials/examples of student outcomes, marketing hooks, and suggested media/assets. Step 2 – Clean with pipelines

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 requests and json to pull live data (e.g., Stock prices, Weather data).
Web Scraping: Using BeautifulSoup and Selenium to extract data from websites that lack APIs.
Databases: Writing SQL queries inside Python scripts to pull data from PostgreSQL or MySQL automatically.

2) Target audience

Early-career data analysts, business analysts, and aspiring data scientists.
Professionals who know SQL/Excel and want automation with Python.
Non-programmers wanting practical, job-ready Python skills for data workflows.

DS4B 101-P vs. Other Python Courses