– Focuses on data ingestion from SQL databases and CSVs, followed by data wrangling and cleaning using Pandas and NumPy .
Secondly, the course prioritizes . An automated script is useless if it requires a human to click "Run." DS4B 101-P introduces learners to scheduling, logging, and error handling. Students learn to use tools like prefect or airflow (contextually) to build Directed Acyclic Graphs (DAGs) that extract data from APIs, transform it, and load it into a database or dashboard—all while sending alerts if a step fails. This transforms Python from a calculator into a resilient, 24/7 data worker. DS4B 101-P- Python for Data Science Automation
In the evolving landscape of modern business, the ability to analyze data is no longer a luxury but a necessity. However, a significant challenge facing many organizations is not the lack of data, but the inefficiency of processing it. Traditional workflows often rely on manual inputs, fragile Excel spreadsheets, and repetitive point-and-click operations that consume valuable time and introduce human error. The course "DS4B 101-P: Python for Data Science Automation" addresses this critical bottleneck, serving as a bridge between basic Python programming and real-world business application. It represents a paradigm shift from manual data handling to streamlined, reproducible automation. – Focuses on data ingestion from SQL databases
: Users of Excel, Power BI, or Tableau looking to augment their analytical capabilities with programming. Data Analysts Students learn to use tools like prefect or
The DS4B 101-P course is divided into several modules, each covering a specific aspect of Python programming and data science automation. Here's an overview of the course modules:
DS4B 101-P: Python for Data Science Automation is a comprehensive course designed to teach individuals how to automate data science tasks using Python. The course covers the fundamentals of Python programming, data science libraries, and automation techniques. It's an ideal course for data scientists, analysts, and anyone who wants to automate their data science workflows using Python.