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Formulas and Cell References

Lesson 2: Formulas and Cell References

What are formulas?

Formulas perform calculations using values or cell references.

  • A formula begins with an equals sign (=).
  • It can use operators: + - * / ^
  • It can use functions: SUM, AVERAGE, IF, etc.

Example: If quantity is in C2 and unit price is in D2, =C2*D2 calculates line revenue.

Relative and absolute references

Reference type

Syntax

Behaviour when copied

Use case

RelativeA2Row and column both shiftRow-by-row calculations
AbsoluteA2Never changesA fixed tax rate or exchange rate
Mixed (column fixed)$A2Row shifts, column fixedCopying across columns
Mixed (row fixed)A$2Column shifts, row fixedCopying down rows against a header

Tip: In Excel, press F4 while editing a reference to cycle through these options.

Example

Cell

Content

Meaning

B110%Discount rate
C21,000Original price
D2=C2*(1-B1)Discounted price = 900
  • B1 stays fixed when you copy the formula down, so every row uses the same discount.
  • Format B1 as a percentage so the value is easy to read (10% is stored as 0.10).

How it works

  • The spreadsheet recalculates automatically when referenced values change.
  • This makes it easy to test scenarios. Change the discount in B1 and every price update.
  • It also means a wrong input or reference flows through every dependent result.

Common mistakes

Mistake

Example

Fix

Shifted reference after copyingDiscount moves from B1 to B2, B3…Use B1
Percentage vs whole numberTyping 10 instead of 10%Format the cell as a percentage
Overwriting formulas with valuesTyping 900 over a formulaProtect formula cells; use a different colour for inputs
Hard-coding numbers in formulas=C2*0.9Put assumptions in labelled cells
Circular referencesA formula refers to itselfTrace precedents and fix the logic

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Data Analytics Tutorial

Data Analytics Tutorial

MODULE 1 : Foundations of Data Analytics
lessonIntroduction - Foundations of Data Analytics
lessonWhat is Data Analystics
lessonTypes of Data Analytics
lessonThe Data Analytics Workflow
lessonData Types, Sources, and Structure
lessonTurning a Business Request into an Analytical Question
MODULE 2 : Spreadsheets and Practical Data Handling
lessoniIntroduction - Spreadsheets and Practical Data Handling
lessonUnderstanding Spreadsheet Structure
lessonFormulas and Cell References
lessonEssential Functions for Analysis
lessonSorting, Filtering, and Data Validation
lessonLookups and Pivot Tables
MODULE 3 : SQL for Data Analytics
lessonSQL for Data Analytics - Introduction
lessonDatabases and Your First SQL Query
lessonFiltering, Sorting, and Limiting Results
lessonAggregate Functions and GROUP BY
lessonJoining Related Tables
lessonCASE, Subqueries, and CTEs
MODULE 4 : Python for Data Analytics
lessonPython for Data Analytics Introduction
lessonPython Basics for Analysts
lessonConditions, Loops, and Functions
lessonReading and Inspecting Data with pandas
lessonCleaning and Transforming Data
lessonGrouping, Summaries, and Visualisation
MODULE 5 : Statistics for Data Analytics
lessonStatistics for Data Analytics Introduction
lessonDescriptive Statistics
lessonDistributions, Percentiles, and Outliers
lessonSampling and Uncertainty
lessonHypothesis Testing and Confidence Intervals
lessonCorrelation, Regression, and Causation
MODULE 6 : Data Visualization Reporting and Dashboards
lessonData Visualisation, Reporting, and Dashboards Introduction
lessonChoosing the Right Chart
lessonVisual Design and Avoiding Misleading Charts
lessonKPIs and Dashboard Planning
lessonBuilding Reports in BI Tools
lessonPresenting an Insight and Recommendation
MODULE 7 : Exploratory Data Analysis and Data Cleaning
lessonExploratory Data Analysis and Data Cleaning - Introduction
lessonStarting an Exploratory Data Analysis
lessonMissing Values and Their Meaning
lesson Duplicates, Inconsistent Labels, and Data Types
lesson Outliers and Validation Rules
lessonA Reproducible Cleaning Pipeline
MODULE 8 : Business Product and Marketing Analytics
lessonBusiness, Product, and Marketing Analytics Introduction
lessonSales and Revenue Analytics
lessonCustomer Retention and Churn
lessonMarketing Funnel and Campaign Analytics
lessonProduct Analytics and User Journeys
lessonOperational Analytics and Root-Cause Investigation
MODULE 9 : Advanced Analytics - Forecasting and Machine Learning Basics
lessonAdvanced Analytics, Forecasting, and Machine Learning Basics - Introduction
lessonForecasting and Time-Series Basics
lessonRegression and Classification
lessonClustering and Segmentation
lessonModel Evaluation and Choosing Metrics
lessonOverfitting, Leakage, and Responsible Modelling
MODULE 10 : Data Workflows - Warehousing and Professional Practice
lessonData Workflows, Warehousing, and Professional Practice - Introduction
lessonOperational Databases and Data Warehouses
lessonETL, ELT, and Data Pipelines
lessonAutomation, Scheduling, and Monitoring
lessonDocumentation, Version Control, and Collaboration
lessonPrivacy, Governance, and Ethical Practice
MODULE 11 : Final Lesson: Bringing It All Together
lessonFinal Lesson: Bringing It All Together

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Data Analysis and Processing with Python using Pandas - Data Science Course

5 hrs

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Data Analytics Tutorial

MODULE 1 : Foundations of Data Analytics
lessonIntroduction - Foundations of Data Analytics
lessonWhat is Data Analystics
lessonTypes of Data Analytics
lessonThe Data Analytics Workflow
lessonData Types, Sources, and Structure
lessonTurning a Business Request into an Analytical Question
MODULE 2 : Spreadsheets and Practical Data Handling
lessoniIntroduction - Spreadsheets and Practical Data Handling
lessonUnderstanding Spreadsheet Structure
lessonFormulas and Cell References
lessonEssential Functions for Analysis
lessonSorting, Filtering, and Data Validation
lessonLookups and Pivot Tables
MODULE 3 : SQL for Data Analytics
lessonSQL for Data Analytics - Introduction
lessonDatabases and Your First SQL Query
lessonFiltering, Sorting, and Limiting Results
lessonAggregate Functions and GROUP BY
lessonJoining Related Tables
lessonCASE, Subqueries, and CTEs
MODULE 4 : Python for Data Analytics
lessonPython for Data Analytics Introduction
lessonPython Basics for Analysts
lessonConditions, Loops, and Functions
lessonReading and Inspecting Data with pandas
lessonCleaning and Transforming Data
lessonGrouping, Summaries, and Visualisation
MODULE 5 : Statistics for Data Analytics
lessonStatistics for Data Analytics Introduction
lessonDescriptive Statistics
lessonDistributions, Percentiles, and Outliers
lessonSampling and Uncertainty
lessonHypothesis Testing and Confidence Intervals
lessonCorrelation, Regression, and Causation
MODULE 6 : Data Visualization Reporting and Dashboards
lessonData Visualisation, Reporting, and Dashboards Introduction
lessonChoosing the Right Chart
lessonVisual Design and Avoiding Misleading Charts
lessonKPIs and Dashboard Planning
lessonBuilding Reports in BI Tools
lessonPresenting an Insight and Recommendation
MODULE 7 : Exploratory Data Analysis and Data Cleaning
lessonExploratory Data Analysis and Data Cleaning - Introduction
lessonStarting an Exploratory Data Analysis
lessonMissing Values and Their Meaning
lesson Duplicates, Inconsistent Labels, and Data Types
lesson Outliers and Validation Rules
lessonA Reproducible Cleaning Pipeline
MODULE 8 : Business Product and Marketing Analytics
lessonBusiness, Product, and Marketing Analytics Introduction
lessonSales and Revenue Analytics
lessonCustomer Retention and Churn
lessonMarketing Funnel and Campaign Analytics
lessonProduct Analytics and User Journeys
lessonOperational Analytics and Root-Cause Investigation
MODULE 9 : Advanced Analytics - Forecasting and Machine Learning Basics
lessonAdvanced Analytics, Forecasting, and Machine Learning Basics - Introduction
lessonForecasting and Time-Series Basics
lessonRegression and Classification
lessonClustering and Segmentation
lessonModel Evaluation and Choosing Metrics
lessonOverfitting, Leakage, and Responsible Modelling
MODULE 10 : Data Workflows - Warehousing and Professional Practice
lessonData Workflows, Warehousing, and Professional Practice - Introduction
lessonOperational Databases and Data Warehouses
lessonETL, ELT, and Data Pipelines
lessonAutomation, Scheduling, and Monitoring
lessonDocumentation, Version Control, and Collaboration
lessonPrivacy, Governance, and Ethical Practice
MODULE 11 : Final Lesson: Bringing It All Together
lessonFinal Lesson: Bringing It All Together

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HCL GUVI (Grab Your Vernacular Imprint) Geek Network Private Limited is a leading online learning and skills development company, incubated by IIT Madras and IIM Ahmedabad.

Established in 2014 and acquired by the HCL Group in 2022, HCL GUVI is dedicated to providing effective and high-quality learning and skilling programs that transcend language barriers in technology education. HCL GUVI today is trusted by over 4.8 million users, 20 languages and 2500+ corporate partners.

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Refer & Earn

HCL GUVI (Grab Your Vernacular Imprint) Geek Network Private Limited is a leading online learning and skills development company, incubated by IIT Madras and IIM Ahmedabad.

Established in 2014 and acquired by the HCL Group in 2022, HCL GUVI is dedicated to providing effective and high-quality learning and skilling programs that transcend language barriers in technology education. HCL GUVI today is trusted by over 4.8 million users, 20 languages and 2500+ corporate partners.

app storeplay storeRefer & Earn
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