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Reading list

Machine Learning Basics for a Newbie
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
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
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 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 the Difference Between Data And Information?

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Analytics Vidhya Last Updated : 12 May, 2023
4 min read

We generate around 328.77 million terabytes of data daily, and almost every day, millions of people are at stake because they are sharing valuable information with people knowingly or unknowingly. Using electronic gadgets and surfing the internet since childhood and yet needing clarification on the two terms: data and information? Nothing to worry about. Though these words find interchangeable usage among common people, they are different. Understanding the data and information differences is highly important to decide what to share with others wisely. This article will tell us Data vs Information – major differences between the two!

Table of contents

What is Data?

Data is an unsystematic or nonspecific description awaiting processing. Being unorganized, combining observations, numbers, images, symbols, facts, characters, and other entities requires interpretation via humans or machines to derive meaning and proper usage. 

What is Information? 

The output of processed data varies on the purpose of usage and environment, which is collectively termed as information. It is the structured and processed facts imparting meaning and sense and is ready to use. It might involve raw data manipulation during processing to improve reliability, connection, and logic.

Data vs Information: What is the Difference between Data and Information?

ParameterDataInformation
CompositionNumbers, character sets, lettersInference, results, ideas
About Qualitative and quantitative collection of variables Logical interpretation of data obtained after processing 
Available format Graph, table, data tree and othersSentences and presentations portraying thoughts and ideas 
DependenceIndependentDependent on data 
Public usageNot PermittedAvailable for sale
Knowledge levelLow levelHigh (second level)
UsageNo direct useOnly after data processing 
Organization UnorganisedOrganized
RoleNot enough for decision-makingDecisions are made based on information
ReliabilityOver data source (considered unreliable)Over processor who interprets the information
SpecificityUnspecificSpecific according to the requirement
Measuring unitsBits and bytesTime, quantity, and others 
Example numberSingle test scoreAn average test score of a student

Understanding Data vs Information with Examples 

Using the simple number 100, we can define and understand data and information differences. The number 100 is data that does not indicate any sense, context or relation. However, on data processing when we add the word ‘miles.’ Thus, 100 miles becomes an information. 

Taking another example, the following statement will be considered data:

Courses, placements, data science, excel, mentorship, career, 

When combining it into meaningful information, it will look as follows:

Excel your data science career with Analytics Vidhya. 

Understanding Data vs Information with Example 
Source: examples.yourdictionary

Another example to show difference between data and information – Consider a dataset containing a list of numbers: 2, 5, 7, 10, 3. This collection of numbers represents data. However, without any further context or interpretation, it doesn’t convey any specific meaning or provide actionable insights.

Now, let’s process and organize this data. If we calculate the average of these numbers and label it as “Average score,” we have transformed the data into information. The information “Average score is 5.4” provides meaningful insights and allows for better understanding or decision-making.

How Businesses Can Leverage Data and Information?

Data and information are the driving factors of businesses, helping them reach their goals and fulfill their objectives. They support them in decision-making. Being directly collected from the company, these are processed by the employees depending on the specific usage. Optimized storage and usage must undergo practices to leverage the data and information for efficient business functionality accompanied by smarter and swift decisions.

The next step is to create information from data depending on specific usage and requirement. Working on the same data redundantly, which might require repetitive or related information, leads to time wastage, lack of accuracy and reliability, and reduced efficiency. Instead, a centralized database will act with efficacy as a solution to the problem. 

How Businesses Can Leverage Data and Information 
Source: ProWebScrapper

End Note

We hope now you know how different data and information are! If someone misuses these terms near you, try to correct them. Want to learn more about Data and career options in this field? Explore our free data science course and learn more about the field.

Frequently Asked Questions 

Q1. What is the difference between digital data and information?

A. Companies collect the data through different modes, such as online and offline. Regardless of the type of data collection, it can be converted into digital format for better storage, processing, manipulation and interpretation of data. Whereas, information is an interpretation of data that is convenient with online storage. 

Q2. What are the business analytics tools require data?

A. Some commonly used tools include Board, Dundas BI, Sisense, Microstrategy and many more.

Q3. What are the methods of data collection?

A. Data collection happens through various sources such as surveys, observations, experiments, sensors, websites, digital interactions and other methods. Depending on the data collection source, it is  classified into primary and secondary data. The data collection can be technology-driven, automatic, or manual. Users generate around 70% of the world’s data.

Q4. How can data and information be protected?

A. Data and information security are crucial parts of any organization. Numerous laws are meant to protect the data, thus making it important. Several measures allow data protection, such as encryption, secure network infrastructure, access controls, regular backups and other related options. These protect sensitive data from breaches, data loss and theft. Around 81% of the users are aware of the risks and benefits of data collection.

Q5. What is the difference between file processing and DBMS?

A. File processing or system refers to management and data organization on a storage medium such as a hard disk or computer. DBMS or Database Management System is representative of software to retrieve and store the user’s data with efficient security and data processing. The file processing could be more efficient, less secure, and comprises redundant data.

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