Python For Data Science Automation: Ds4b 101-p-

The curriculum is streamlined into three primary steps designed for rapid skill acquisition:

: Users of Excel, Power BI, or Tableau looking to augment their analytical capabilities with programming. Data Analysts

A script that pulls ERP data, calculates KPIs, generates charts, and updates a SharePoint folder every Monday at 6:00 AM.

By breaking code down into reusable, well-documented functions, data professionals can build scripts that adapt dynamically to changing inputs. This modularity makes it possible to orchestrate workflows using scheduling tools down the line. 3. Business-Driven Exploratory Data Analysis (EDA) DS4B 101-P- Python for Data Science Automation

At the heart of any data automation workflow is . This library allows you to read, clean, merge, reshape, and filter tabular data programmatically. Instead of writing complex Excel formulas or dealing with software crashes on large datasets, Pandas handles millions of rows in seconds. Combined with NumPy , it provides the mathematical foundation needed to automate complex business logic and financial calculations.

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

Excel Files (Transactions, Products, Customers) → SQL Database (orderlines, bikes, bikeshops) → Python Data Collection & Cleaning (collect_data(), summarize_by_time()) → Time Series Modeling with sktime (arima_forecast(), plot_forecast()) → SQL Database (write_forecast_to_database(), read_forecast_from_database()) → Automated Reporting with Papermill (run_reports() generating HTML and PDF) The curriculum is streamlined into three primary steps

When transitioning from interactive data analysis to production automation, adopt these engineering practices to ensure your pipelines do not break under unexpected edge cases:

In modern enterprise environments, data is abundant, but time is scarce. Companies often lose thousands of hours to repetitive tasks. These include manual data entry, building spreadsheets, and generating PDF reports. Standard data science training focuses on predictive modeling and machine learning. However, the most immediate business ROI often comes from automation.

What do you primarily use? (SQL, APIs, local files?) This modularity makes it possible to orchestrate workflows

Manually downloading CSV files from databases or web portals ruins productivity. Python automates the Extraction, Transformation, and Loading (ETL) process.

DS4B 101-P is intentionally designed for three distinct groups of business analysts who need to master Python for automation and data analysis:

What is the you currently handle each week?

Exploiting Pandas internals to process millions of rows simultaneously.