What do you primarily use? (SQL, APIs, local files?)
Automatically detecting missing values, structural anomalies, and outliers using predefined business logic rules. 3. Automated Predictive Modeling
to bridge the gap between traditional data analysis and software engineering
: Data scientists familiar with the R language (e.g., from the DS4B 101-R course) who need to learn Python for business integration.
System schedulers like Windows Task Scheduler or Cron jobs run Python scripts at specific intervals. DS4B 101-P- Python for Data Science Automation
: Connecting Python scripts directly to SQL databases to pull raw transactional data.
Developed by Business Science, this innovative course is designed to bridge the gap between traditional data analysis and advanced, automated Python-based systems. It empowers professionals to move beyond spreadsheets and create robust, automated data products. What is DS4B 101-P?
: 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.
Tasks that take humans hours run in seconds, freeing employees for strategic work. What do you primarily use
: You cannot scale your impact because you are buried in maintenance, leaving no time for actual insights. 🚀 The Transformation: The Automation Journey
Where do your stakeholders prefer to ? (Email, Slack, Excel, BI dashboards?) Share public link
Prerequisites: While it is a "101" course in the DS4B series, a fundamental understanding of Python (pandas, numpy) and basic statistics is recommended. Why Choose Python for Automation?
Whether you are a BI professional, an R user looking to add Python, or a complete beginner, . Automated Predictive Modeling to bridge the gap between
Create interactive, report-quality visualizations.
: Using Papermill to generate production-ready reports and automate repetitive delivery tasks. Key Skills & Tools Covered Data Wrangling : Cleaning and reshaping data using Pandas .
While Python is superior for data processing, Excel remains the universal language of business stakeholders. DS4B 101-P emphasizes programmatic Excel manipulation. Using openpyxl and xlwings , you can read data from spreadsheets without opening them, write data back into formatted corporate templates, apply complex conditional formatting, and generate native Excel charts automatically. 4. Database Integration (SQLAlchemy)
: Learning to interact with databases by creating and managing environments. Professional Environment : Setting up and using as a primary development environment. Time Series Forecasting