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.
Among the educational frameworks designed to bridge the gap between traditional data analysis and enterprise-grade automation, stands out as a definitive blueprint.
: Learn to work with transactional databases by creating and managing your own SQLite database.
fundamentally flips this script. Instead of viewing a Python script as a one-off tool to generate a static report, DS4B treats Python as an engine to build production-ready, automated data products. Core Pillars of the DS4B 101-P Framework DS4B 101-P- Python for Data Science Automation
: The course is built for "serious beginners," meaning it teaches foundational programming logic specifically through the lens of data science automation.
provides more than just a syntax guide; it provides a comprehensive operational methodology. By mastering data wrangling, programmatic system interaction, and automated delivery, you transform yourself into an invaluable asset capable of building autonomous data ecosystems that drive business value.
You work on a comprehensive case study from start to finish, building a portfolio piece. If you are a data professional ready to
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.
An automation script is only truly automated if you don't have to press "Run" every day. The final step involves deploying the script to run on a predictable schedule—whether that is hourly, daily, or at the close of every fiscal quarter. This is achieved using operating system schedulers (like Windows Task Scheduler or Linux Cron Jobs) or advanced orchestration engines like Apache Airflow or Prefect. Business Benefits of Data Science Automation
The final part focuses on creating the "data product" that stakeholders will interact with. fundamentally flips this script
The preprocessed data is fed directly into a pre-trained, serialized H2O machine learning model. The model scores the data, appending columns like Churn_Probability or Expected_Revenue_Loss to the records. Stage 4: Downstream Distribution
Capstone Project (throughout final 2 weeks)