Module Title: From VBA to Pandas: A Comprehensive Guide for Data Analysts

  Assessment: For this assessment, you will be provided with a large dataset containing sales transactions from a retail company. Your task is to perform various data analysis tasks using Pandas to provide insights into the company's sales performance. Tasks: Load the dataset into a Pandas DataFrame. Perform data cleaning and preprocessing as necessary. Calculate the total revenue for each month. Calculate the average revenue per transaction for each month. Calculate the total revenue for each product category. Identify the top-selling products and product categories. Create visualizations to present your findings. Dataset: The dataset contains the following columns: TransactionID: unique ID for each transaction CustomerID: ID for the customer who made the transaction ProductID: ID for the product sold ProductCategory: category of the product sold TransactionDate: date of the transaction TransactionAmount: total amount of the transaction You can download the dataset from the follow

  Introduction: As businesses grow, they generate more data. To gain insights from this data, it is crucial to have the right tools for analysis. Many businesses have been using VBA (Visual Basic for Applications) for automating and analyzing data in Microsoft Excel. However, as the amount of data and complexity of analysis increases, VBA can become limited. Pandas, on the other hand, is a popular open-source data analysis library for Python that provides powerful tools for data manipulation, analysis, and visualization. It can handle large datasets efficiently and provides a range of functions for data cleaning, data transformation, and data analysis. This training module aims to help VBA users make the transition to Pandas. What is Pandas? Pandas is an open-source data manipulation and analysis library for Python. It provides powerful tools for data cleaning, preparation, and analysis that are essential in data science and machine learning. Pandas has two main data structures - Serie