Data Analysis & Processing for Finance & Accounting Management

This course equips you with the foundational skills for data analysis and processing, the essential building blocks for any AI application. You'll learn how to wrangle, clean, explore, and analyze financial data to prepare it for further tasks like modeling, forecasting, and risk analysis. This course is crucial for the Finance & Accounting Management department, as it empowers you to unlock valuable insights hidden within your financial data. 

• Grasp the fundamental concepts of data analysis and processing within the financial domain.

• Understand the importance of data cleaning and wrangling for financial data analysis.

• Explore various techniques for data exploration and visualization relevant to financial datasets.

• Learn how to handle missing data, outliers, and data inconsistencies in financial datasets.

• Gain hands-on experience using popular data analysis tools like Python libraries (pandas) and spreadsheet software (Excel).

• Analyze and visualize financial data (e.g., stock prices, transaction records) to identify trends and patterns.

• Communicate data insights effectively using clear visualizations and reports tailored for financial audiences.

1. Introduction to Data Analysis & Processing for Finance:

• The importance of data analysis and processing in financial tasks (modeling, forecasting, risk management).

• Understanding the data analysis lifecycle: data acquisition, cleaning, exploration, and visualization.

• Exploring different types of financial data (numerical, categorical, time series) and their characteristics.

• Hands-on exercises: Introduction to Python libraries (pandas) and Excel for data manipulation.

• Real-world examples of data analysis in Finance & Accounting Management.

2. Data Cleaning, Exploration & Visualization for Financial Applications:

• Learning techniques for data cleaning in finance (handling missing data, outliers, inconsistencies).

• Exploring data visualization tools for financial data analysis (charts, graphs, dashboards).

• Understanding common financial data visualizations and their effectiveness in communication.

• Hands-on coding exercises: Cleaning and visualizing financial datasets (e.g., stock market data, transaction records) using Python and Excel.

• Developing financial reports and presentations with clear data visualizations and insights.

• Basic understanding of mathematics and statistics

• Familiarity with programming concepts and a language such as Python or R

• Knowledge of database systems and SQL is beneficial but not required