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Reading list

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

A Guide to Locking Cells and Securing Your Data in Excel

Pankaj Singh
Pankaj Singh 12 Aug, 2024
5 min read

Introduction

Have you ever spent hours perfecting a spreadsheet, only to worry that someone else might unintentionally (or intentionally!) mess with your carefully crafted data? I’ve been there, and it’s not a fun place to be. Excel is an incredibly powerful tool that many of us rely on for everything from simple lists to complex data analysis. But with that power comes the need to protect your work. That’s where locking cells in Excel comes into play—a simple yet effective way to keep your data safe from unwanted changes.

Lock Cell in Excel

Overview

  1. Discover how locking cells in Excel can protect your carefully crafted data from unintended changes.
  2. Learn how to secure specific cells in Excel to safeguard critical information while collaborating with others.
  3. Understand the steps to fully protect your worksheet fully, ensuring that locked cells remain secure.
  4. Find out how to selectively lock essential cells and formulas to prevent accidental edits.
  5. Explore the importance of locking Excel cells to maintain data integrity and peace of mind in shared work environments.

Table of contents

The Basics of Locking Cells in Excel

Locking cells in Excel is like putting your work in a digital vault. It ensures that specific cells or even an entire sheet can’t be altered without your permission. Imagine you’re sharing a budget spreadsheet with a team and want them to update only the spending categories, not the formulas or totals. By locking certain cells, you’re safeguarding those critical calculations while still allowing for collaboration.

Here’s how you can lock cells in Excel:

  1. Select the Cells You Want to Lock: Name the cells you wish to protect. If you’re locking all cells, you can skip this step.
  2. Open the Format Cells Dialog: Right-click on the selected cells and choose “Format Cells,” or navigate to the “Home” tab and click on “Format” in the ribbon, then select “Format Cells.”
The Basics of Locking Cells in Excel
  1. Lock the Cells: Go to the “Protection” tab in the Format Cells dialog and check the box next to “Locked.” This action only marks the cells for locking; you’ll still need to protect the sheet to enforce it.
The Basics of Locking Cells in Excel

Also read: Microsoft Excel for Data Analysis

Protecting the Worksheet

Once you’ve marked cells as locked, the next step is to protect the worksheet. This is where the magic happens—the locked cells won’t be protected without this step.

To protect your worksheet:

Protecting the Worksheet
  1. Right-click the Sheet Tab: Right-click the tab of the sheet you want to protect and select “Protect Sheet” from the context menu.
Protecting the Worksheet
  1. Set a Password: Enter a password (make sure it’s something you’ll remember!), and select the actions you want to allow users to perform on your sheet. Users can select locked cells by default, but you can change this if needed.
  2. Finalize the Protection: Click “OK” to apply the protection. Now, the cells you marked as locked cannot be altered unless the user knows the password.
Protecting the Worksheet

Special Cases: Locking Formula Cells

For those of us who live and breathe formulas, protecting them from accidental changes is crucial. Locking formula cells follows a similar process but with an extra step to ensure only the cells containing formulas are locked.

  1. Unlock All Cells: Start by unlocking all cells as described earlier.
Special Cases: Locking Formula Cells
  1. Find and Select Formulas: Go to the “Home” tab, click “Find & Select,” then choose “Go To Special.” In the dialog box, select “Formulas” and click “OK.” Excel will automatically select all cells containing formulas.
Special Cases: Locking Formula Cells
  1. Lock the Selected Formula Cells: Right-click the selected cells, go to Format Cells > Protection, and check the “Locked” box.
Special Cases: Locking Formula Cells
  1. Protect the Worksheet: Protect the sheet to activate the locks, and your formulas are now safe!
Special Cases: Locking Formula Cells

Conclusion

Locking cells in Excel is one of those small actions that can have a big impact, especially when you’re sharing your work with others. Knowing that your data and formulas are protected gives you peace of mind, allowing you to focus on what really matters—analyzing and presenting your data, not worrying about accidental changes.

So, next time you’re working on an important Excel project, take a moment to lock those cells. You’ll thank yourself later when your spreadsheet remains intact, no matter how many hands it passes through. If you’re curious to explore more advanced Excel features or have any questions about this guide, drop a comment below—I’m here to help!

Here’s your Pathway to Mastering Microsoft Excel Formulas and Functions: Click Here

Frequently Asked Questions

Q1. What are the benefits of locking cells in Excel?

Ans. Locking cells in Excel helps protect critical data from accidental or unauthorized changes. It ensures that only specific users can modify important data, thereby maintaining the integrity and accuracy of the information in your spreadsheet.

Q2. How do I lock cells in Excel without locking the entire sheet?

Ans. To lock specific cells without locking the entire sheet, first, select the cells you want to lock and go to “Format Cells” > “Protection” and check the “Locked” option. Then, protect the sheet via the “Review” tab. Only the selected cells will be locked, allowing you to edit the rest of the sheet.

Q3. Can I restrict editing to only certain users in Excel?

Ans. You can restrict editing to certain users by using Excel’s “Allow Users to Edit Ranges” feature. This allows you to set specific permissions for different users, enabling them to edit certain cells or ranges while protecting the rest of the sheet.

Q4. How can I track changes made to a protected Excel sheet?

Ans. Excel provides a “Track Changes” feature that allows you to monitor any modifications made to a protected sheet. This feature logs all changes, including who made them and when, so you can review and approve or reject the modifications.

Q5. What precautions should I take before locking cells in Excel?

Ans. Before locking cells, complete all necessary data entry and formatting. Double-check that you’ve only locked the cells you intend to protect, and create a backup of your file. Also, consider sharing the password with trusted colleagues if access is needed in your absence.

Excellock celllock cells in ExcelMicrosoft Excelprotect sheet in excel
Pankaj Singh
Pankaj Singh 12 Aug, 2024

Hi, I am Pankaj Singh Negi - Senior Content Editor | Passionate about storytelling and crafting compelling narratives that transform ideas into impactful content. I love reading about technology revolutionizing our lifestyle.

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