Data Analysis and Decision Making in Finance and Business.

This comprehensive course provides a deep dive into the world of business analytics and intelligence, equipping students with essential skills to make informed decisions and drive strategic initiatives across various business domains. Through a series of engaging lectures and practical exercises, participants will explore key concepts and tools spanning finance, open banking, marketing, operations management, and business intelligence.

Section 1: Finance

Lecture 1: Data-Driven Decision Making: Learn to describe and present data effectively for informed decision-making in finance.

Lecture 2: Historical Balance Sheets Analysis: Delve into the analysis of historical balance sheets to glean insights into financial performance and trends.

Lecture 3: Value Added Taxes Preparation: Master the preparation of value-added taxes to ensure compliance and optimize financial operations.

Lecture 4: Assets Visualization: Explore techniques for visualizing assets data to enhance understanding and facilitate decision-making.

Lecture 5: Stock Market Instant Analysis: Gain the skills to conduct rapid analysis of stock market data for timely decision-making.

Lecture 6: Project Net Present Value: Learn how to calculate and evaluate the net present value of projects to assess their financial viability.

Section 2: Open Banking

Lecture 7: Data Retrieval from European Central Bank: Explore methods for fetching data from the European Central Bank for analysis and insights.

Lecture 8: Exchange Rate Base Currency Conversion: Learn to change the base currency of exchange rates to facilitate cross-border transactions and financial analysis.

Lecture 9: Remote Data Transformation: Acquire techniques for transforming data remotely to meet specific business requirements.

Section 3: Marketing

Lecture 10: Data Analysis Cycle: Understand the iterative process of data analysis and its application in marketing strategies.

Lecture 11: Population Analysis of Countries: Analyze population data of different countries to inform marketing strategies and target demographics effectively.

Lecture 12: Customer Analysis: Learn to analyze customer data to identify patterns, preferences, and behavior for targeted marketing campaigns.

Section 4: Operations Management

Lecture 13: Types of Digital Data: Explore various types of digital data and their significance in operations management.

Lecture 14: Fundamentals of Business Statistics: Gain a foundational understanding of business statistics and its role in decision-making.

Lecture 15: Optimal Raw Material Prediction: Learn to predict the optimal amount of raw materials required for efficient operations management.

Section 5: Business Intelligence Tools

Lecture 16: Business Intelligence Context: Understand the role of business intelligence in enhancing organizational decision-making and performance.

Lecture 17: Python Essentials for Beginners: Introduction to Python programming language for data analysis and manipulation.

Lecture 18: Power BI Excel Query Creation: Learn to create queries in Power BI using Excel data sources.

Lecture 19: Power BI Web Source Query Creation: Explore the process of creating queries in Power BI from web sources.

Lecture 20: Power BI SQL Server Query Creation: Master the creation of queries in Power BI using SQL Server data sources.

Lecture 21: Extending Python Scripts in Power BI: Learn advanced techniques for extending Python scripts within Power BI for enhanced data analysis.

Section 6: Appendix

Lecture 22: Descriptive Data Analysis: Explore techniques for descriptive data analysis to gain insights into business operations.

Lecture 23: Venn Analysis: Understand the application of Venn analysis in identifying relationships and intersections within datasets.

Lecture 24: Stock Market API Integration: Learn to connect and retrieve data from stock market APIs for real-time analysis.

Lecture 25: Statistical Measures: Gain proficiency in statistical measures such as mean, median, percentile, standard deviation, and variance for data analysis.

Lecture 26: Linear Regression: Explore the fundamentals of linear regression analysis and its application in predictive modeling.

Lecture 27: Advanced Linear Regression: Dive deeper into the concepts of linear regression for more complex predictive modeling scenarios.

This course offers a holistic approach to business analytics and intelligence, empowering participants with the knowledge and skills to drive organizational success through data-driven decision-making and strategic insights.