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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

Why should Data Science Embrace Blockchain as its next Big Thing?

P
Phurba Last Updated : 20 Dec, 2023
5 min read

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

blockchain and data science

Introduction

Data science and blockchain technology are two of the most cutting-edge and disruptive technologies in the world today. Data science analyzes and interprets the raw data to understand how a system works. Blockchain technology is an innovative way of keeping track of transactions and storing financial information. The combination of these two concepts has led to incredible innovations in software development, finance, and more.

This article will explain data science and blockchain and how they work together to make a difference.

Definition of Data science and Blockchain Technology

Data Science is one of the rapidly-growing domains in technology today. Predictive analytics, Diagnostic analytics, and Descriptive analytics are just a few of the many subfields within science that are always evolving. The goal is to derive insights from existing data, whether structured or unstructured.

For example, Netflix Recommendations – Netflix can provide recommendations based on a user’s video viewing history and ratings. As a result, users can receive suggestions for new films and series relevant to their interests based on their preferences. This can boost the company’s revenue by keeping users engaged on such sites.

Blockchain is a decentralized digital ledger capable of storing any type of data. Blockchain technology is an encrypted database that multiple users share without an intermediary overseeing it. This allows for a tamper-proof system for storing information about transactions between parties.

For example, Cryptocurrencies – A cryptocurrency is a digital currency that uses blockchain technology to record and secure every transaction. Bitcoin, for example, can be used as digital cash to buy everything from groceries to cars.

Blockchain

It has several applications, including financial transactions, digital identity verification, and supply chain management. As such, data scientists have been tasked with improving the efficiency of these processes by identifying patterns in transaction data or predicting how particular actions will impact the system as a whole.

Implications of Data Science and Blockchain Technology

Data is the foundation of blockchain technology. Data also plays a critical role in addressing several critical pain points in the industry. For example, to improve transparency and mitigate fraud, we need to analyze patterns and trends of past user behaviors and correlate them with current activities.

Both have made significant contributions to the modern world. Data scientists have been investigating using the blockchain to store data for years. The most well-known example of this is Factom, which recently partnered with Microsoft on its Cocoa Framework project. This will allow companies to use the Blockchain to store their sensitive data on an enterprise level.

Data science has Impacted Blockchain Technology

  • In blockchain technology, data science ensures that transactions are secure and tamper-proof. It helps to maintain the integrity and security of blockchain transactions. In addition, it can be used to make sure that transactions are executed promptly.
  • Any suspicious activity on the blockchain network can be detected using data science. Additionally, it can categorize various transactions depending on their features, allowing for easier collection and analysis. This would make it easier for companies to track criminals using blockchain networks for nefarious purposes such as money laundering or terrorist financing activities.
  • Blockchain technology offers many benefits for businesses leveraging its decentralized features for authentication or record-keeping purposes. However, it also presents some challenges when it comes to analyzing the data stored on a blockchain network. The distributed nature of blockchains means that there are no centralized servers where one can run queries or perform statistical analysis on the data stored within them. To overcome these limitations, researchers have developed new techniques for performing analytics on blockchains by leveraging concepts from areas such as AI, machine learning (ML) and deep learning (DL).
Blockchain

Blockchain Uses Cases in Data science

  1. Data Integrity:

The quality of the data recorded on it ensures its reliability because it has undergone a rigorous verification process. Furthermore, since the activities and transactions that occur on the blockchain network can be traced, it provides transparency.

In most cases, data integrity is secured by storing and automatically verifying the origin and transactions of a data block on the blockchain.

  1. Ensures high-quality data and accuracy:

Each component of the blockchain’s digital ledger contains private and public data. Cross-checking and analysis are performed on the data before it is incorporated into different blocks at the entry point. Data Verification does not get any simpler than this.

  1. Allows Data Traceability:

It’s easier for people to form partnerships with each other using the blockchain. For example, if a published account fails to describe any technique adequately, any peer can analyze the entire process and conclude how the results were produced.

Using the ledger’s open channels, anyone can discover whether data is reliable, how to store it, how to update it, where it originates from, and how to utilize it properly. Ultimately, blockchain technology allows users to track data from the point of entry to the exit.

  1. Real-time analysis:

Real-time data analysis is extremely challenging. The best approach to identifying scammers is observing the changes in real-time. With blockchain’s distributed nature, businesses can discover any inconsistencies in their databases from the start.

A blockchain-enabled solution can help enterprises that require large-scale real-time data analysis. With blockchain, banks and other organizations can detect changes in data in real-time, enabling them to make prompt choices, such as blocking a suspicious transaction or monitoring aberrant behaviors.

  1. Making prediction (Predictive analysis) :

One of the simplest ways is through predictive analytics. Just like other types of data, blockchain data can be analyzed to get valuable insights into behaviors and patterns and to predict future events. In addition, blockchain delivers organized data collected from individuals or devices.

Data scientists use predictive analysis to accurately forecast social events, including consumer preferences, customer lifetime value, dynamic prices, and organizational churn rates. As a result, almost any occurrence can be predicted with the correct data analysis, whether it’s social attitudes or investment signals.

Conclusion

Both industries are relatively new, but they’re growing rapidly in tandem. Several companies can benefit from using these technologies together to examine blockchain networks for security purposes, determine more about their users, and begin making better decisions about the technology they produce. Overall, Data science has plenty of potential applications in this brave new world of blockchain technology, and we look forward to seeing what the future holds!

Key Takeaway:

  • Big data focuses on the quantity of data, whereas blockchain is concerned with quality.
  • Data Science is a field of study that uses diverse scientific methods, algorithms, and procedures to extract information from large volumes of data.
  • This technology is decentralized, distributed ledger that tracks the origin of a digital asset. The inherent security mechanisms and public ledger of blockchain make it an ideal tool for virtually every industry.
  • As the adoption of blockchain technology continues to rise, data scientists have begun building blockchain-based solutions.

Frequently Asked Questions

Q1.What is the salary of a blockchain data scientist?

The salary of a blockchain data scientist ranges from $80,000 to over $150,000 annually in the United States, depending on factors like experience and location. Salaries can be higher in high-demand industries and regions. Check recent industry reports for the latest figures.

Q2.How do blockchain and data science complement each other?

 Blockchain provides a secure and transparent decentralized ledger, while data science leverages algorithms and insights for analysis. Together, they enhance data integrity, transparency, and decision-making.

Q3.What role does data science play in blockchain technology?

Data science contributes by extracting valuable insights from the vast amounts of data stored on the blockchain. It helps analyze trends and patterns and optimize processes within the decentralized ecosystem.

Q4.How can blockchain benefit from data science applications?

Data science applications enhance blockchain functionality by providing predictive analytics, machine learning, and AI-driven solutions. This synergy enables more intelligent decision-making and efficiency across various blockchain use cases.

Connect with me on LinkedIn: https://www.linkedin.com/in/phurba-doma-sherpa-691848193

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

block chain technologyblockchainblockchain and data scienceblogathon
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Phurba

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