Automation - Ds4b 101-p- Python For Data Science

| | Module | Key Topics | | :--- | :--- | :--- | | Part 1: Foundations of Data Analysis with Python | 1: Jumpstart | Sales Analysis (Time Series) with Pandas | | | 2: SQL Databases | Connecting Python to SQL databases and packages | | | 3: Pandas Core | Deep dive into Pandas core functions, data wrangling, and Challenge #1 to test skills | | Part 2: Time Series Forecasting Automation | 4: Time Series Fundamentals | Basics of time series data and analysis | | | 5: Functional Programming | Writing reusable functions, including outlier detection | | | 6: Sktime Forecasting | Introduction to the sktime library and building ARIMA forecast automation | | Part 3: Visualization & Report Automation | 7: Plotnine | Basics and in-depth exploration of plotnine for data visualization; includes a mini-challenge to restyle a Cyberpunk 2077 plot | | | 8: Debugging | Building and debugging a database read/write automation workflow | | | 9 & 10: Jupyter Automation | Automating Jupyter notebooks to generate HTML and PDF reports using papermill | | Bonus | Scheduling | BONUS section on scheduling Python scripts for production-grade automation |

[Part 1: Foundations & SQL] ➔ [Part 2: Time Series & Forecasting] ➔ [Part 3: Automated Reporting] Part 1: Data Analysis Foundations & Database Interactivity Python for Data Science Automation (Course 1)

: Integrating the forecasting results back into SQL databases to finalize the automation loop. Target Audience

Participants dive into advanced time series analysis using the state-of-the-art sktime library. The focus here is on building core software and custom functions to handle repetitive forecasting tasks automatically.

A hallmark of the DS4B 101-P approach is breaking down chaotic manual workflows into a structured, five-stage programmatic pipeline. DS4B 101-P- Python for Data Science Automation

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

Once the data is clean, the script applies specific business rules. This could involve segmenting customers into tiers based on lifetime value, calculating compound interest, flagging fraudulent transactions using statistical thresholds, or joining disparate datasets together to provide a unified view of corporate performance. Stage 4: Dynamic Reporting and Distribution

By the end of the DS4B 101-P course, students will be able to:

. The curriculum focuses on building a professional-grade Python toolchain to reduce errors, improve scale, and deliver data products on-demand. Core Curriculum Phases The course is structured into three streamlined stages: Data Analysis Foundations Pandas Mastery | | Module | Key Topics | |

The course includes detailed instructions for installing all required software on both Mac and Windows:

By breaking code down into reusable, well-documented functions, data professionals can build scripts that adapt dynamically to changing inputs. This modularity makes it possible to orchestrate workflows using scheduling tools down the line. 3. Business-Driven Exploratory Data Analysis (EDA)

DS4B 101-P: Python for Data Science Automation - Revolutionizing Business Workflows

Participants gain hands-on experience with an "enterprise-grade" tech stack: Data Manipulation A hallmark of the DS4B 101-P approach is

Manual processes do not scale; doubling the volume of business data usually requires doubling the headcount of analysts. Python scripts process 10,000 rows or 10,000,000 rows using virtually the same code, allowing businesses to scale operationally without a linear increase in overhead costs.

: Automating the execution and parameterization of Jupyter Notebooks. Software Engineering for Data Science : Setting up a professional environment with , and learning to build internal Python libraries. Who is it for?

The course is specifically crafted for several overlapping professional groups:

To help me tailor any specific code examples or technical architectures, could you tell me a bit more about:

Transitioning from manual data workflows to a Python-driven automation framework yields immediate, compounding returns for an organization.