Are you interested in learning more about the like sktime or plotnine used in this course? Python for Data Science Automation (Course 1)

To ensure the solution runs anywhere, the program teaches . Students learn to containerize their API and models, creating portable, consistent environments that eliminate the "it works on my machine" problem. 5. Automated Data Science & Deployment

To understand how a DS4B 101-P system functions under the hood, let us break down a standard automated predictive pipeline into its lifecycle stages.

: This course serves as the prerequisite for DS4B 201-P: Machine Learning & APIs, where you learn to predict the future, not just report the past.

Learn how to utilize the sktime library to create robust forecasts.

– Focuses on data ingestion from SQL databases and CSVs, followed by data wrangling and cleaning using Pandas and NumPy .

The foundational thesis of DS4B 101-P is simple:

Python connects directly to the ERP database using SQLAlchemy .

Furthermore, the course emphasizes the concept of reproducibility, a cornerstone of professional data science. In a manual workflow, if a mistake is found or new data arrives, the entire process must be redone from scratch. DS4B 101-P teaches students how to build automated pipelines that can be rerun with a single command. This includes integrating business logic, such as forecasting with Facebook Prophet, directly into the code. The result is a system that not only analyzes the past but predicts the future, delivering these insights via automated emails or interactive dashboards without human intervention.

This foundational module focuses on setting up your environment and mastering data manipulation.

: Python seamlessly glues together disparate systems. It can read an Excel file, query a SQL server, run a deep learning model, and send an alert to a Slack channel all within the same script.

: The course uses a "Bicycle Manufacturer" project where students expand reporting flexibility for executive decision-makers. Automation Efficiency

: Learning to interface with transactional databases to ingest business data directly. Advanced Visualization : Creating production-ready charts using (a Python implementation of the Grammar of Graphics). Workflow Automation Jupyter Notebooks : Using templatized reports for consistent documentation.

The DS4B 101-P curriculum is structured around a multi-tier pipeline designed to take raw organizational data and convert it into automated business intelligence.

Here is a comprehensive deep dive into how the DS4B 101-P framework transforms manual workflows into scalable, automated data science pipelines using Python. 1. The Core Philosophy of DS4B 101-P

Writing optimized SQL queries, understanding transactional database schemas, and avoiding data corruption during joins. 2. Manipulation: Advanced Wrangling with Pandas & NumPy