Data Analysis and Decision Making in Finance and Business.

Data Analysis and Decision Making in Finance and Business. udemy course free download

Mastering Techniques and Tools for Extracting Valuable Insights and Developing Strategic Solutions Through Data Analysis

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.