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6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

What is Data Formatting in Excel?

mounish12439
mounish12439 28 Aug, 2024
4 min read

Introduction

Handling data efficiently in Excel can be challenging for analysts. Given that crucial business decisions hinge on accurate reports, formatting errors can lead to significant issues. This article will help you understand about data presentation and formatting in Excel. By the end of this article, you’ll be equipped to identify and correct common formatting errors in your reports.

Overview

  • Excel formatting essentials for accurate and visually appealing data presentation.
  • Guide to cell-level, conditional, and number formatting techniques in Excel.
  • Preventing common Excel formatting errors for reliable business reporting.
  • Comprehensive overview of Excel’s data formatting options and their applications.
  • Mastering Excel formatting to enhance data clarity and support informed decisions.

Table of contents

Excel Data Formatting: An Overview

Excel data formatting involves adjusting how data appears in your spreadsheets and altering its presentation and structure. Formatting can be applied based on how data looks or the type of data it represents.

With a grasp of the basics, you’ll be ready to apply formatting techniques effectively in real-world scenarios.

Cell-Level Formatting Techniques

Cell formatting encompasses adjusting text alignment, font styles, colors, and background hues. Let’s look at this table to get a better idea:

Formatting AspectDescriptionExample
Font CustomizationChoose different fonts, sizes, and styles.Arial, 12pt, Bold
Text ColorCustomize text color with various options.Blue, Red, Green
Cell Background ColorChange background colors for better visual appeal.Yellow, Light Grey
Cell BordersAdd borders around cells to enhance readability.Thin, Thick, Double
Clipboard and Paste OptionsUse clipboard for various paste functions.Paste Values, Paste Formatting

Conditional Formatting: Highlight cells based on specific conditions to improve data analysis through visual emphasis.

Also read: A Comprehensive Guide on Advanced Microsoft Excel for Data Analysis

Understanding Number Formatting

Number formatting ensures numerical data is displayed correctly and consistently. Missteps in number formatting can distort reports. For example, omitting the dollar sign from the highest transaction can lead to reporting errors.

Format TypeDescriptionExample
GeneralDefault format with no special formatting.1234
NumberFormat numbers as dates or times.1,234.56
CurrencyDisplays numbers with currency symbols.$1,234.56
AccountingSimilar to Currency but with more precise decimal placement.$1,234.56
Date and TimeFormats numbers as dates or times.08/28/2024, 14:30
PercentageConverts numbers to percentages.12.34%
FractionShows numbers as fractions.1 1/2
ScientificDisplays numbers in scientific notation.1.23E+03
TextTreats numbers as text without alterations.“1234”
SpecialFor symbols and custom formats.Phone numbers, ZIP codes

Also read: Best Resources to learn Microsoft Excel

Implementing Formatting in Excel

Let’s take a sample data and try out a few formatting options:

Sample Data
  1. Font Customization:
    • Select the “Product” column.
    • Go to the Home tab.
    • Change the font to Arial, size 12, and apply Bold style.
Font Customization
  1. Text Color:
    • Select the “Sales” column.
    • Click on the font color button in the Home tab.
    • Choose blue.
  1. Cell Background Color:
    • Select the “Profit” column.
    • Click on the fill color button in the Home tab.
    • Choose dark grey.
Cell Background Color
  1. Cell Borders:
    1. Select the entire table.
    2. Go to the Home tab.
    3. Click on the Borders dropdown and choose “Outer Borders”.
Cell Borders

Conditional Formatting

  1. Select the “Sales” column.
  2. Go to the Home tab and click on “Conditional Formatting.”
  3. Choose “Highlight Cell Rules” and select “Greater Than.”
  4. Enter 1500 and choose a formatting style (e.g., a green fill).
Conditional Formatting

Number Formatting

  1. Currency Formatting:
    • Select the “Sales” and “Profit” columns.
    • Go to the Home tab.
    • Choose “Currency” from the Number group.
Number Formatting
  1. Number Formatting
    • Select the “Date” column.
    • Right-click and choose “Format Cells”.
    • Select “Date” type
Number Formatting

Other Formatting Options

For more tailored formatting, Excel allows:

  1. Custom Formats: Create unique formats to meet specific needs.
  2. Table Formatting: Customize tables to enhance data presentation.

Also Read: Microsoft Excel for Data Analysis

Conclusion

Excel can help develop precise and understandable reports supporting well-informed business decisions. By using these formatting methods, number formatting you can ensure your data is visually attractive and accurately conveyed. These Excel formatting options will help you prevent common mistakes, such as formatting inaccuracies and misreadings, thereby enhancing the effectiveness and reliability of your data analysis. 

Frequently Asked Questions

Q1. What is needed for data formatting in Excel?

Ans. Data formatting in Excel helps to present data clearly and consistently, making reports easier to read and understand. It improves visual appeal and ensures accurate data representation.

Q2. How do I apply cell-level formatting in Excel?

Ans. You can apply cell-level formatting by selecting the desired cells and using options in the Home tab, such as font customization, text color, background color, and cell borders.

Q3. What are some common cell-level formatting techniques in Excel?

Ans. Common techniques include adjusting font styles and sizes, changing text and background colors, adding cell borders, and using clipboard options for pasting values and formats.

Q4. What is the difference between Currency and Accounting number formatting in Excel?

Ans. Currency formatting displays numbers with a currency symbol, while Accounting formatting aligns currency symbols and decimal points for improved readability.

data formattingdata formatting in excelExcel
mounish12439
mounish12439 28 Aug, 2024

I'm a tech enthusiast, graduated from Vellore Institute of Technology. I'm working as a Data Science Trainee right now. I am very much interested in Deep Learning and Generative AI.

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