The course is structured to take you from zero to automated hero. Here is a deep dive into the core modules.
After completing the course, learners will be able to:
DS4B 101-P is not just an introduction to Python; it is a comprehensive training ground designed to transform analysts into automation engineers. Bridging the gap between theoretical data science and practical business application, this course teaches students how to build robust, automated data pipelines that save organizations hundreds of hours of manual work.
Moving beyond simple scripting, DS4B 101-P focuses on the "Automation Workflow"—a systematic approach that encompasses data extraction, cleaning, processing, and reporting. Students learn to leverage the power of the Python ecosystem, utilizing libraries such as Pandas for data manipulation, Matplotlib and Seaborn for visualization, and key automation libraries to integrate these processes seamlessly into business operations.
Key Learning Outcomes:
By the end of the course, participants will have moved past "one-off" analysis. They will possess the skills to build automated systems that continuously deliver value, allowing businesses to make data-driven decisions faster and with greater accuracy. DS4B 101-P is the essential first step for any professional looking to future-proof their career in the rapidly evolving landscape of business 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:
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? DS4B 101-P- Python for Data Science Automation
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
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)
The DS4B 101-P: Python for Data Science Automation course, offered by Business Science University, is designed to transform business analysts into data science "automation experts". Unlike generic intro courses, it focuses on converting repetitive manual business processes into automated Python workflows. Core Course Workflow
The curriculum is built around a specific three-step journey to automate complex business tasks like time-series forecasting and report generation: Data Analysis Foundations:
Tooling: Setting up a professional environment using VSCode.
Data Wrangling: In-depth training on Pandas and NumPy for manipulating tabular data.
Databases: Building and interacting with SQL (SQLite) databases. Time Series & Forecasting:
Learning to handle time-series data using sktime, a state-of-the-art library for forecasting in Python.
Developing reusable functions to simplify repetitive forecasting tasks. Reporting & Automation:
Visualization: Creating report-quality visuals with plotnine (a grammar-of-graphics library similar to R's ggplot2).
Automated Reports: Using Papermill to parameterize and run Jupyter Notebooks, generating production-ready HTML or PDF reports automatically. Key Benefits for Business
Reduced Errors: Replaces manual "copy-paste" spreadsheet work with standardized scripts.
Scalability: Allows teams to handle increasing volumes of data without adding more analysts. The course is structured to take you from
Professional Software Practices: Teaches students how to build their own custom Python packages to store and share automation functions.
Stakeholder Delivery: Focuses on delivering results on-demand through automated data products. Practical Highlights
Project-Based: Includes multiple real-world exercises and projects to practice the concepts.
Automation Bonuses: Teaches how to schedule these Python scripts using tools like Windows Task Scheduler and Mac Automator for true hands-off execution.
Business Science’s DS4B 101-P is a professional-grade course focused on Python for business automation and data science, designed to transition analysts from manual spreadsheets to automated workflows. The curriculum covers data manipulation with pandas, visualization, time series analysis, and functional programming within a business-centric framework. For more details, visit Business Science.
The DS4B 101-P: Python for Data Science Automation course, taught by Matt Dancho
at Business Science University, is a project-based program designed to transform how business analysts approach repetitive tasks. Instead of manual data crunching, the course focuses on converting business processes into automated, Python-based data products. Core Curriculum & Workflow
The course is structured around three streamlined phases that mirror a real-world business automation project:
Data Analysis Foundations: Mastering the core "bricks" of the Python data science ecosystem, including Pandas for data manipulation and NumPy.
Time Series Forecasting: Learning to build predictive models that help organizations anticipate future trends.
Reporting Automation: Creating automated delivery systems, such as reports and SQL database updates, to provide stakeholders with on-demand insights. Key Benefits for Professionals
End-to-End Skillset: You don't just learn to code; you learn to build a complete system, from connecting to a transactional database to outputting executive-ready deliverables.
No Prerequisites: The course is built for "serious beginners," meaning it teaches foundational programming logic specifically through the lens of data science automation.
Business Transformation: The primary goal is to help organizations reduce errors and improve scale by replacing fragile manual processes with robust Python scripts. Practical Project Focus
Unlike theoretical bootcamps, this course is highly practical. A central project involves building a Forecasting and Reporting System, which involves modularizing data preparation and specifying SQL data types for robust database writes. This approach ensures you finish with a portfolio-ready automation tool rather than just a certificate. DS4B 101-P is not just an introduction to
DS4B 101-P: Python для автоматизации обработки данных
Title: The Midnight Report
Lena stared at her screen. It was 11:47 PM, and her CFO wanted the quarterly logistics report by 8 AM. The data was scattered across three Excel files, two CSV exports from the warehouse, and a messy JSON from the ERP system.
She used to do this manually: open each file, copy-paste, write formulas, fix date formats, and cry over merged cells. But not anymore.
She opened Jupyter Lab and launched her DS4B 101-P toolkit.
Step 1 – Automate the messy imports.
import pandas as pd import glob
files = glob.glob("data/*.xlsx") df_list = [pd.read_excel(f, skiprows=2) for f in files] warehouse = pd.concat(df_list, ignore_index=True)
Step 2 – Clean with pipelines.
She wrote a reusable function to strip spaces, convert dates, and flag outliers — all from her automation module.
Step 3 – Enrich using APIs.
A quick requests.get() pulled live fuel surcharge rates into a new column.
Step 4 – Schedule the logic.
Using schedule and a simple logging function, she set the script to run every night at midnight. Tonight was just a test run.
At 11:59 PM, she ran the final cell. The script:
Lena closed her laptop at 12:08 AM. No caffeine. No rage. No manual VLOOKUP hell.
The CFO never knew how messy the data was. And that was the point.
Automation wasn’t just about saving time — it was about taking back her evenings.
End.
Most self-taught Pythonistas skip logging. DS4B 101-P dedicates serious time to it. You learn to set up logging systems that tell you why a script failed at 2:00 AM. You learn to write scripts that catch errors, retry failed API calls, and save "checkpoints" so you don’t have to start processing from scratch when something breaks.