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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

Data Science in Web 3.0

Amey
Amey Last Updated : 23 Jun, 2022
5 min read

This article was published as a part of the Data Science Blogathon.

Introduction on Web 3.0

As a result of centralization, the Internet has been able to serve billions of users and build a solid foundation upon which it can continue to thrive. At the same time, the World Wide Web is controlled by a few central organizations, which arbitrarily decide what is and isn’t permitted.

This difficulty is solved with Web3. Rather than being controlled by a few major corporations, Web3 is a decentralized network established, maintained, and owned by its users. Individuals, not businesses, have sway on Web3. Let’s take a look at how we arrived at this point.

The Evolution of Web

Analyze how much the internet influences your day-to-day activities. It’s worth thinking about how the internet has impacted society. Platforms for social networking, in-app purchases, and the internet are undergoing yet another fundamental transition.

It’s common for people to conceive of the Web as a foundation of contemporary life that has been around since its inception. In contrast to this, today’s Web is substantially different from what was initially envisaged. This is best understood by breaking the brief history of the Web into two distinct periods-Web 1.0 and Web 2.0.

Web 1.0: Read-Only (1990–2004)

In the beginning, there was just one version of the web: Web 1.0. A large majority of participants were consumers of data, whereas the majority of the contributors were often developers who built websites that delivered information mostly in the form of text or graphics.

At the time, Tim Berners-Lee was working on the protocols for the World Wide Web at CERN, Geneva, 1989. He proposed the creation of open, decentralized protocols that would enable anybody on the planet to share knowledge.

‘Web 1.0’ was created by Berners-Lee in the 1990s. There was almost minimal interaction between users in Web 1.0, which led to it being dubbed the “read-only web” since there was so little user-generated material.

Web 2.0: Read-Write (2004-now)

The beginning of the Web 2.0 era coincided with the launch of the first major social media platform in 2004. Read-and-write functionality eventually replaced the web’s original read-only mode. Instead of providing information to consumers, corporations have begun to provide platforms where users may exchange user-generated content and connect with one another. Previously, companies just provided content to users. As more people started to use the internet, a smaller number of the most successful organizations began to control an increasingly disproportionate share of the web’s traffic and the value it created. The income model that is fueled by advertisements was also born out of Web 2.0. Users were able to contribute content, but they did not own it and could not profit from its commercialization in any way.

Web 3.0

The phrase “Web3” has come to represent a broad vision for the future of the internet. Blockchains, cryptocurrency, and NFTs are at the heart of Web3’s ownership-based model. P2P technologies like virtual reality, metaverse, and the internet of things may help make the internet more open and decentralized in the Web 3.0 era, which is now underway.

Web 1.0 users’ data were static, they could only read information, and firms were unaware of user activity. Web 2.0 allows people to read, write, and create online, helping corporations analyze user data. People may engage and exchange information via social media. In Web 3.0, algorithms will aim to analyze individual data and customize the internet.

Why is Web 3.0 important?

1. Ownership

Users in Web 3.0 will have total control over their data and the protection of encryption. Permission or case-by-case sharing of information is the only way it will be done.

2. Transparency

You can keep track of your own data using Web 3.0. Trust issues aren’t as prevalent as they were in Web 2.0, when large corporations controlled our personal data.

3. Native Payments

The payment infrastructure of Web2 is supported by financial institutions and payment processors. To enable users to transmit money directly inside the browser without the need for a trusted third party, Web3 makes use of tokens like ETH and SOL.

4. Privacy

Identity is handled quite differently in Web3 than it is now. Users’ wallet addresses are often used to link their online identities in web3 applications. New-age customers are wary of revealing personal information online because of the number of data breaches in the web 2.0 era. This problem will be solved by Web 3.0.

Data Science in WEB 3.0

Data Science in WEB 3.0
Source:https://tinker.ly/how-web-3-0-uses-ai-to-provide-relevant-content-for-its-users/

The World Wide Web of today is unrecognizable compared to the early days of the internet. If you look back even a few years, you’ll see that there have been significant changes. The beginnings of Web 3 are already starting to appear, although it is still unclear what its future will bring. As it plays out, it has the potential to significantly alter the way the internet functions, but it also has the potential to just result in tiny changes.

Web 3.0 is a far more significant advancement than just another technological iteration. It solves a variety of concerns that have yet to be handled with Web 2.0 technologies, such as security threats, poor customer experience, and a number of other conundrums that are currently being faced by browsers designed for the Web 2.0 standard. Companies in the data area are primarily concerned with the analysis and optimization of data and trends, the supply of data-driven solutions that result in data-informed choices, and the development of models that transform data insights into product suggestions.

Users will be able to sell their data directly to the companies, whichever they choose. These companies will then be able to deploy their data scientists to integrate and blend these new data sources with existing data sets to enhance learning models, and then sell the resulting insights. Data firms may build language models that provide the contextual understanding and then design solutions that are personalized for the user since data is connected up to user interaction on Web 3. Additionally, data firms are able to extract insights from raw data and then transform those insights into superior product suggestions. These enhanced product recommendations may then deliver a better customer experience that is mostly based on the expectations of the consumer.

Role of Data Scientist in WEB 3.0

With the introduction of Web 3.0, the job profile of a data scientist is set to change significantly over the coming years. As an aspiring professional in this field, you must be educated about all of these advancements in order to deal with them successfully and efficiently.

The expertise of a data scientist is crucial in the process of extracting insights from unstructured data. It is just a matter of time until open databases host an ever-increasing amount of information. Data scientists make raw data actionable. I can’t foretell the future, but a data scientist’s work will become harder as public datasets proliferate and consensus rises. This paper outlines the talent road map for a data scientist over the next few years and how firms may handle recruiting, training, and retention to remain competitive.

Data scientists will play a far greater role in the global economy in the Web 3 future. They will assist in the development of content or AI models that can organize and interact with other AI models to help organizations or enterprises tackle more complicated challenges or possible dangers.

Conclusion on Web 3.0

This technology is evolving and intriguing, yet it has significant flaws. Data Science will only advance with Web 3.0’s arrival. Data scientists should know how this new technology affects them. Web 3.0 promotes new technologies for better data collection, processing, and visualization. This will improve data science and make it simpler for non-technical folks to utilize. Web 3.0 will provide new chances and appealing models for machine users to be more engaged and involved in curating, creating, and sharing data, which will surely induce diverse commercial and economic situations as unanticipated trends.

The Cloud, IoT, machine learning, and analytics are delivering organizations new customer information and raising consumer expectations. Web 3.0 has immense promise for organizations and poses problems in utilizing real-time information and analysis to deliver a fantastic consumer experience.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

Amey
Amey

Data Analyst having 2 years of experience interpreting and analyzing data to drive successful business solutions. Proficient knowledge in statistics, mathematics, and analytics. If you like my content, let's connect on Linkedin.

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