Automation - Ds4b 101-p- Python For Data Science

DS4B 101-P: Python for Data Science Automation is a professional-grade course offered by Business Science University designed to transform data analysts into "automation heroes". Unlike standard "101" courses that focus solely on syntax, this program is project-based, teaching students how to build a complete end-to-end forecasting and reporting system. Core Course Objectives

The course is built on the principle that modern organizations are rapidly transitioning repetitive business processes into automations to reduce errors and improve scale. Students learn to:

Wrangle Large Datasets: Master the Pandas library with over five hours of in-depth training on data manipulation.

Automate Reporting: Use tools like Papermill to generate automated data products and reports for stakeholders.

Forecast Time Series: Integrate advanced libraries such as sktime to predict business trends.

Build Python Software: Transition from writing scripts to developing reusable Python packages and libraries. Key Modules and Curriculum

The curriculum is streamlined into three primary steps designed for rapid skill acquisition: DS4B 101-P- Python for Data Science Automation

Data Analysis Foundations: Deep dives into VS Code as a development environment, SQL database interaction (specifically SQLite), and advanced data wrangling.

Time Series Forecasting: Learning how to connect to transactional databases and apply time-series models to real-world business data.

Reporting Automation: Creating data products that provide on-demand results for executives. Who is This Course For?

Serious Beginners: Those with no prior Python experience who are committed to learning programming specifically for data science.

Data Analysts: Professionals looking to move beyond Excel or manual reporting by leveraging automation.

Business Leaders: Individuals who need to understand how to deliver data-driven results that improve organizational decision-making. Why It Stands Out DS4B 101-P: Python for Data Science Automation is

Most introductory courses leave students with "siloed" skills. DS4B 101-P focuses on the Workflow, ensuring that by the end of the program, you have a functional system you can deploy in a corporate environment. It is the entry point for the Business Science R-Track or Python-equivalent systems, emphasizing "full-stack" data science capabilities. Python for Data Science Automation (Course 1)

Use a 6-week instructor-led or 8-week self-paced schedule; example here is 6 weeks, twice-weekly lessons (12 sessions) plus projects.

Week 0 — Pre-course setup (self-paced)

Week 1 — Python fundamentals for data

Week 2 — Data ingestion & APIs

Week 3 — Data cleaning & transformation Use a 6-week instructor-led or 8-week self-paced schedule;

Week 4 — Automation & orchestration

Week 5 — Reporting & dashboards

Week 6 — ML pipelines, deployment & MLOps basics

Capstone Project (throughout final 2 weeks)

This course is ideal for:

Prerequisite: Basic Python knowledge (variables, data types, loops, functions) or completion of a Python introductory course.