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Data Analysis and Decision Making in Finance and Business.

Name: Data Analysis and Decision Making in Finance and Business.
Rating: 4.5 (6 reviews)

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

Created byOsama Hassan

Last updated 10/2024

English

English [Auto],

What you'll learn

Make Confident Decisions.
Applied machine learning with python to solve business administration problems and support decision making.
Apply business statistics basic methods.
Solve linear problems in production management.
Fetch, transform, analyze and visualize stock prices of a particular company from stock market data source directly.
Predict expected amount of particular account by analyzing previous balance sheets.
Determine whether a relationship between tow accounts within historical balance sheets exists.
Analyse customers based on predefined constraints and extract sets of their characteristics.
Convert currencies based on European Central Bank latest prices.
Create dynamic reports reading data from external sources instantly.
Fetch, extract, transform and visualize historical population of a country against another.

Course content

6 sections • 27 lectures • 7h 32m total length

Describe and present data in the way it can be used to make decisions.2:21
Explore descriptive data analysis to describe data, use graphs to compare industries, assess dispersion and reliability with standard deviation and box plots, and base decisions on sample insights.
Historical Balance sheets analysis.14:36
Apply linear regression to historical balance sheets to predict future total deposits from securities. Import data from Ezekial server or Excel, transform numbers to billions, and visualize predictions with matplotlib.
Prepare value added taxes12:58
Learn to prepare a value added tax report in python by filtering orders with sales over 2000 and profit, calculating VAT, and exporting a Ministry of Finance report.
Assets Visualization17:37
Explore data visualization with a Venn diagram for set intersections and interactive bar and pie charts to compare banks and visualize assets, liabilities, and equity on a balance sheet.
Stock market instant analysis12:17
Connect to the stock market API using pandas data reader, transform data, visualize stock direction, and fetch prices for multiple companies at once.
Project Net Present Value5:41
Compare Project X and Project Y by calculating present value of six yearly installments against initial investment to determine net present value and which project yields higher value.

Fetch Data from European Central Bank.5:13
Establish a connection to the European Central Bank, fetch and transform USD/EUR rates into a data frame, then convert user-entered amounts for local systems.
Change base currency of exchange rates1:57
Connect to the European Central Bank API to retrieve historical exchange rates and fetch the latest rates with the US dollar as base, then parse and display the data.
Transform Data remotely36:27
Learn to transform data remotely via cross-platform remote functions, validate accounts with remote services, convert dictionaries to data frames, and build secure Python statements to update database servers.

Data Analysis Cycle6:50
Define the objective and collect data from digital sources as the data analysis cycle begins. Clean and analyze with methods like linear regression and R-squared, translating results into business decisions.
Countries Population Analysis31:13
Advance data visualization to support decision making by modeling and graphing data from sources through extraction, transformation, and loading into standard graphs, including population analysis.
Customer analysis13:22
Apply set theory in Python to analyze customers by calculating union, intersection, difference, and symmetric difference, visualize with a Venn diagram, and target marketing campaigns based on loans and employment.

Types of Digital Data3:26
Explore the differences between structured data and unstructured data, including metadata and examples like Excel files and video, and learn how file servers and relational databases support decision making.
Fundamentals of Business statistics19:50
Discover how to apply basics of statistics in Python by computing mean, median, mode, standard deviation, variance, and percentile with non-pay and states modules.
Predict optimal amount of raw materials.9:04
Formulate and solve linear programming problems in business by modeling constraints and an objective function, then optimize with Python using interior point or simplex methods.

Business Intelligence Context12:59
Explore the three layers of a business intelligence project—from data sources to transformation and loading—and use Python to support data analysis, visualization, and decision making.
Python essentials for beginners.21:36
Master Python essentials for beginners, covering variables, data types, lists, sets, dictionaries, booleans, control flow, loops, functions, modular programming, and common list operations for business intelligence.
Power BI create Excel query7:54
Load and transform data from Excel into Power BI Desktop using get data, connect to various sources, and visualize with bar and scatter charts.
Power BI create query from web source15:30
Connect to a web data source in Power BI Desktop, load tables from a web page with anonymous access, and create queries to analyze and relate data.
Power BI create SQL server query19:06
Connect to a secure sql server in Power BI by configuring the server name, database, and credentials, then compare import versus direct query and load data with a sql statement.
Extend Python script in Power BI5:52
Extend Power BI with Python scripting to transform data across multiple sources using pandas, multiplying salary by 10 for custom business intelligence analysis.

Descriptive Data Analysis2:06
Explore descriptive data analysis and descriptive statistics using bar graphs and box plots to compare industry types, assess usage levels, customer satisfaction, and inform corrective actions with data tables.
Venn analysis40:00
Explore building and interpreting a three-set Venn diagram with matplotlib to analyze bank loan applicants, government sector workers, and application status, including intersection and symmetric differences.
Connecting to stock market API29:42
Connect to Yahoo Finance via Pandas datareader to fetch stock data, transform dates, and visualize open and close prices across multiple companies with matplotlib.
Mean, Median, Percentile, ST, Variance40:18
Calculate mean, median, mode, standard deviation, variance, and percentile with Python to extract insights from data and support finance and business decision making.
Linear Regression41:00
Apply linear regression to balance sheet data to predict next year's deposits from historical securities, using Python and financial accounting to combine statistics, programming, and finance for business insights.
Linear Regression (2)23:32
Apply linear regression in finance to predict deposits from security investments, build a Python visualization of actual vs predicted values, and discuss decision-making implications.

Requirements

Completed course: "Foundational Python Programming For Finance." or familiar with python fundamentals

Description

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.

Who this course is for:

Accountants
Financial Analysts
Bankers
Business Managers
Students of Accounting and finance
Business analysts
Data analysts

Data Analysis and Decision Making in Finance and Business.

What you'll learn

Explore related topics

Course content

Finance6 lectures • 1hr 6min

Open Banking3 lectures • 44min

Marketing3 lectures • 51min

Operations Management3 lectures • 32min

Business Intelligence Tools6 lectures • 1hr 23min

Appendix:6 lectures • 2hr 57min

Requirements

Description

Who this course is for: