Ds4b 101-p- Python For Data Science Automation

The course is led by , the founder of Business Science and creator of the popular tidyquant R package. With over 15 years of proven track record in developing and productionizing data products to grow revenue, Matt brings a wealth of practical, business-oriented expertise to the course. His teaching style is focused on solving real-world business problems, not just teaching syntax.

: Teaches students how to build their own custom Python packages to store and share automation functions.

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)

The DS4B 101-P framework addresses this systemic operational problem by treating data science not as an isolated research experiment, but as an on-demand business automation factory.

By the end of the DS4B 101-P course, students gain proficiency in: for Data Manipulation. SQL for Database Interaction. SKTime for Time Series Forecasting. Plotly for Data Visualization. Papermill & Jupyter for Process Automation. Why Choose DS4B 101-P? DS4B 101-P- Python for Data Science Automation

The curriculum is divided into specific phases that guide a student from environment setup to a finalized automation workflow: Data Foundations : Mastering for data manipulation and wrangling. Time Series & Forecasting

DS4B 101-P: Python for Data Science Automation - Revolutionizing Business Workflows

Week 1 — Python fundamentals for data

A Python script runs via a task scheduler at midnight on the first of the month. It queries the three databases via SQL, merges the data via Pandas, applies currency conversions, formats a beautiful Excel workbook with integrated executive summaries, and sends it directly to the leadership team's inboxes. Time saved: 40 hours per month. The course is led by , the founder

To bridge this gap, modern data professionals are turning to . This specialized educational framework transforms data scientists and business analysts from reactive report builders into proactive automation engineers. By leveraging Python’s robust ecosystem, professionals can eliminate repetitive tasks, streamline data pipelines, and unlock unprecedented operational efficiency. The Core Philosophy of DS4B 101-P

: Automating templatized Jupyter Notebook reports and converting them to HTML and PDF formats.

For robust, interpretable machine learning. Conclusion: Bridging the Gap to Production

If you are a data professional ready to take your skills to the next level and automate your business processes, DS4B 101-P is an ideal next step. : Teaches students how to build their own

: Integrating the forecasting results back into SQL databases to finalize the automation loop. Target Audience

DS4B 101-P: Python for Data Science Automation - A Detailed Guide

: One wrong formula or missed row can invalidate an entire executive report.

: Designed to take "serious beginners" through the entire process from scratch.

: Replaces manual "copy-paste" spreadsheet work with standardized scripts.