Modules Outline

Module Organization for this Course

Module 1: Introduction to Robotic Process Automation (RPA) 

1. Introduction to RPA 

   - Understanding RPA and its significance in accounting and finance. 

   - Exploring the benefits of RPA in streamlining processes and enhancing efficiency. 

2. Challenges and Considerations in RPA Implementation 

   - Addressing potential risks in RPA implementation. 

   - Balancing human supervision and automation. 

   - Ethical considerations in automated decision-making. 

3. Utilizing ChatGPT for Enhanced Analysis 

   - Leveraging ChatGPT's language model for RPA, Automation and Data Exploration. 

   - Enhancing data exploration using interactive dialogue with ChatGPT. 

Module 2: Process Optimization for RPA 

1. Understanding Process Optimization 

   - Defining process optimization and its relevance in RPA. 

   - Examining the link between optimization and successful RPA integration. 

2. Identifying Inefficiencies 

   - Evaluating existing processes: Is the process still relevant? What value does it add? 

   - Analyzing process sequences and interactions for potential bottlenecks. 

3. Redesigning Processes 

   - Resequencing process steps for optimal flow. 

   - Eliminating redundancies and simplifying complex interactions. 

   - Ensuring a streamlined process ready for automation. 

4. The History of Spreadsheet Technology in Accounting: A Signature Storyline in Robotic Process Automation 

   - Tracing the evolution of spreadsheet technology in accounting. 

   - Recognizing the historical importance of spreadsheets in automation and optimization. 

Module 3 : Software Training: Python Automation 

1. Introduction to Python for RPA 

   - Basics of Python programming: Syntax, variables, data types, and control structures. 

2. Getting Started with Google Colab to develop Scalable Logic

   - Setting up and navigating the Google Colab environment. 

   - Collaborative coding and real-time interaction. 

- User-defined Functions (Encapsulating Scalable Algorithms)

- Numpy

- Data Retrieval using web APIs, automating data collection and using Github.

3. Data Manipulation with pandas and numpy 

    - Data loading and manipulation using pandas DataFrames. 

    - Automating Data Extraction. 

     - Calculations, filtering, and aggregation with numpy. 

- Analyzing the palmerpenguins dataset using pandas. 

4. Data Visualization with Matplotlib 

     - Creating Insightful Visuals for Financial Trends in RPA 

- Interactive Visualizations: A Gateway to Deeper RPA Exploration 

- Visualizing the Palmer Penguins Dataset: An RPA-Infused Practical Journey 

Module 4 : Software Training: Python-Excel Automation and Microsoft Power Automate 

1. Excel Automation with Python 

    - Integrating Python with Excel for efficient automation. 

2. Excel Automation with openpyxl 

- create workbooks to access individual cells and stylizing cells and reporting using openpyxl. 

3. Microsoft Power Automate

- Extract the data from different elements from the web page and insert it into excel spreadsheet. 

Module 5: Practical Applications and Cases 

1. Analyzing Financial Datasets 

    - Analyzing bank fines dataset: Understanding financial implications and trends using pandas and visualization tools. 

 2. Predictive Analytics in Finance 

     - Processing accounting ratios to predict bankruptcy using data from the Taiwan Economic Journal. 

- Building predictive models using machine learning techniques. 

Wrap up: Looking Ahead and Continuous Learning 

1. Exploring RPA Trends and Future Applications 

- Emerging trends in RPA and their implications for accounting and finance. 

- How RPA is evolving to handle complex tasks in the financial domain. 

2. Continuous Learning in the RPA Landscape 

     - The importance of staying updated with evolving technologies. 

- Resources and platforms for continuous skill development in RPA.