Excel for Data Analytics
Microsoft excel Key features:
- Spreadsheet Interface: Excel presents data in a grid format called a worksheet, which consists of rows numbered and columns lettered. The intersection of a row and a column is called a cell, where you can input data, formulas, or functions.
- Formulas and Functions: One of the most powerful aspects of Excel is its ability to perform calculations using formulas and functions. Formulas are expressions you create using mathematical operators, cell references, and constants, while functions are predefined formulas that perform specific tasks, like SUM, AVERAGE, COUNT, IF, and more.
- Data Analysis Tools: Excel offers a wide range of tools for data analysis, such as sorting, filtering, pivot tables, charts, and graphs. These tools help you explore and visualize your data in various ways to gain insights and make informed decisions.
- Conditional Formatting: You can format cells based on certain conditions, making it easy to highlight specific data points or trends within your spreadsheet.
- Data Validation: Excel allows you to set rules for data entry, preventing users from inputting incorrect or inappropriate data.
- Collaboration: Excel supports real-time collaboration, allowing multiple users to work on the same spreadsheet simultaneously. It's a useful feature for teams working on projects or budgets together.
- Import and Export Data: Excel can import data from various sources like text files, databases, and other spreadsheets. It can also export data to different file formats, making it compatible with other software.
- Macros and Automation: Advanced users can create macros, which are small programs that automate repetitive tasks in Excel.
- What-If Analysis: You can use Excel to perform what-if analysis by changing values in cells to see how it affects the outcome of formulas and functions.
Excel is available as part of the Microsoft Office suite for Windows and macOS and can also be accessed online through Office 365. It's commonly used in business and academic settings for financial modeling, budgeting, data analysis, and reporting, but its versatility extends to personal use as well.
Whether you're a beginner or an advanced user, Excel provides a wide range of capabilities to handle data efficiently, making it an essential tool for anyone dealing with numbers and spreadsheets.
Business Data Analytics
Table of contents
Syllabus
-
1.0
Introduction to Excel
- Microsoft excel key features
- Spreadsheet Interface
- Excel Formulas and Functions
- Data Analysis Tools
- keyboard shortcuts in Excel
- Conducting data analysis using data tables, pivot tables and other common functions
- Improving Financial Models with Advanced Formulas and Functions
-
2.0
Introduction to data analytics
-
3.0
Core application of data analytics
- Financial Accounting And Reporting
- Statement of Profit or Loss
- Statement of Financial Position
- Statement of Cash Flows
- Common Size Financial Statement
- Cross-Sectional Analysis
- Trend Analysis
- Analyse financial statements using ratios
- Graphs and Chats
- Prepare forecast financial statements under specified assumptions
- Carry out sensitivity analysis and scenario analysis on the forecast financial statements
- Data visualization and dash boards for reporting
- Financial Management
- Time value of money analysis for different types of cash flows
- Loan amortization schedules
- Project evaluation techniques using net present value - (NPV), internal rate of return (IRR)
- Carry out sensitivity analysis and scenario analysis in project evaluation
- Data visualisation and dashboards in financial management projects
4.0
Application of data analytics in specialised areas
- Management accounting
- Estimate cost of products (goods and services) using high-low and regression analysis method
- Estimate price, revenue and profit margins
- Carry out break-even analysis
- Budget preparation and analysis (including variances)
- Carry out sensitivity analysis and scenario analysis and prepare flexible budgets
- Auditing
- Analysis of trends in key financial statements components
- Carry out 3-way order matching
- Fraud detection
- Test controls (specifically segregation of duties) by identifying combinations of users involved in processing transactions
- Carry out audit sampling from large data set
- Model review and validation issues
- Taxation and public financial management
- Compute tax payable for individuals and companies
- Prepare wear and tear deduction schedules
- Analyse public sector financial statements using analytical tools
- Budget preparation and analysis (including variances)
- Analysis of both public debt and revenue in both county and national government
- Data visualisation and reporting in the public sector
5.0
Emerging issues in data analytics