Ds4b 101-p- Python For Data Science Automation [new] File

She wrote a reusable function to strip spaces, convert dates, and flag outliers — all from her automation module.

The course focuses heavily on the "production" side of data science—taking your messy notebook code and refactoring it into clean, repeatable, automated scripts.

[SQL Databases] ➔ [Pandas Wrangling] ➔ [Sktime Forecasting] ➔ [Papermill & Notebooks] ➔ [Automated Reports] 1. Ingestion: SQL Databases & Transactional Models

5. Web Scraping and API Consumption ( requests , BeautifulSoup ) DS4B 101-P- Python for Data Science Automation

: Students learn to ingest data from CSVs or databases, clean it, perform analysis, and write results back to a SQL database. Business Transformation

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)

Pandas is the cornerstone of Python data analysis. The course teaches you how to import, clean, transform, and analyze data using this powerful library. She wrote a reusable function to strip spaces,

Before executing any analytics, data must be retrieved safely. The course trains students on how to connect directly to relational databases using Python.

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An automated model is useless if its outputs are hidden. The final pillar ensures that insights reach decision-makers via the channels they use daily. Ingestion: SQL Databases & Transactional Models 5

The course places you inside a realistic scenario: you are part of a data science team for a . The team needs to expand their forecast reporting with new flexibility not currently available in their manual process.

The reporting system is parameterized with , enabling you to produce consistent reports for different parameter combinations with minimal manual intervention.