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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

Become a Web 3 Developer in 2022

Parthiban Marimuthu
Parthiban Marimuthu Last Updated : 12 Jul, 2022
5 min read

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

Introduction

Web3 is a blockchain system that intends to deliver data and code decentralization and open sourcing. It is a cutting-edge technology that will transform our lives in a variety of ways. With the help of its smart contracts, Web3 is making an impact on society. Users will be able to acquire shares, transfer assets, and form businesses without the need for a middleman thanks to these contracts.

But, in 2022, how do you become a web3 developer? We’ve detailed some important measures you should follow before starting your career as a web3 developer in this blog post. Continue reading to find out everything you need to know!

If you want to work as a web3 developer in 2022, you must be familiar with the following three concepts:

  • Black chain
  • Decentralized applications
  • Smart contracts
Web 3.0

Web3’s Era

The web3 revolution is the next step in our society’s progress. Decentralized apps, data and code exchange, and other features are included. This transformation will undoubtedly alter the way we operate and perceive the world. To be a part of the web3 revolution, you must first understand what it is and what it has to offer us as individuals.

Web3 is a blockchain-based technology that is a new form of technology. Blockchain is a digital ledger that securely records all network transactions. Web3 can be created by combining smart contracts with blockchain. Smart contracts are computer programmes that run without human intervention when certain conditions are met or particular actions are made in connection to a contract between two parties.

What does it mean to be a Web3 Developer?

A web3 developer is someone who creates blockchain-based technologies, as the name implies. This individual is attempting to bring about a decentralized world, and it is not for the faint of heart! A web3 developer usually has a strong background in computer science and mathematics.
But how does one go about becoming a web3 developer?

You must be well-versed in blockchain technology in order to even contemplate being evaluated. But that’s only the beginning: you’ll also need exceptional problem-solving, creative, and communication abilities. You must be able to put together smart contract-based solutions, which necessitate an understanding of programming languages such as C++ or JavaScript.

A Web3 Developer’s Must-have Skills

This is not a list of talents that a web3 developer should possess in order to be successful. This is a list of abilities that any prospective web3 developer should have.
Web3 developers must be familiar with programming fundamentals as well as blockchain technologies. Solidity, Python, JavaScript, C++, and Go will be the key languages used by web3 developers. They must also have a strong understanding of data structures and algorithms.

You’ll need outstanding analytical skills to be a good web3 developer. Reading up on the newest industry advances and comprehending related topics like encryption and blockchain technology is a fantastic method to acquire these skills. You might also want to look at projects like Data Science for Dummies or The Data Science Handbook, which offer beginner-friendly insights into modern data science approaches.

What does it Take to Become a Web3 Developer?

There are three key steps you must take to become a web3 developer.

The first step is – A blockchain developer.

Learning about blockchain technology is the first step. This involves looking into cryptocurrencies like Bitcoin, Ethereum, and others. It also entails becoming familiar with the programming language that is used to create these virtual currencies.

The second step – Learning Programming
The next step is to learn how to use the programming language that comes with web3 platform development. There are several programming languages to learn, but Solidity and Python are two of the most popular. You’ll be ready to create your own smart contract once you’ve learnt one of these languages.

The third step – Create smart contracts
Finally, before deploying your smart contracts on the main network, test them on a live blockchain platform like Ethereum or Bitcoin’s Test Net. As a result, you’ll be able to avoid frequent blunders and protect yourself from flaws in your code that could result in money loss or data integrity difficulties.

Web 3 Developer
Source: Fabric Ventures

Solidity Programming Language

You should also learn how to create Ethereum DApps with Truffle, a popular development framework that makes decentralized app creation simple. This will assist you in avoiding frequent blunders when creating your own projects.

  • You must master the programming language used to create smart contracts in order to become a web3 developer. Solidity is what it’s all about.
  • Solidity is a similar object-oriented programming language to JavaScript. It’s a statically typed programming language for building smart contracts and decentralized apps (DApps).
  • Solidity was created with security in mind, so anyone can use it and it has a lower learning curve than other languages. In terms of complexity and learning time, it is comparable to Java or C++. Solidity, like those languages, has its own set of libraries that may be used to create DApps.
  • The easiest approach for developers to learn about the various Solidity libraries is to use tutorials and courses on their preferred platform. If you’re interested in learning more about Solidity programming from the perspective of someone else, there are courses available on Coursera and Udemy.

Learn About Data Structures

When programming, it’s critical to grasp the data structures you’ll be working with. These are the most important aspects of your project, and knowing them can help you write better code.

If you’re writing in Solidity, for example, you’ll need to know how smart contracts work. You may learn more about this by studying and experimenting with the documentation for the language: https://solidity.readthedocs.io/. Debugging and testing are made much easier when you understand data structures.

Cryptography

Learning about cryptography is one of the first stages toward becoming a web3 developer. Cryptography, as one of the most complicated areas in computer science, has virtually limitless applications. It can be used by governments, businesses, and individuals to ensure security by protecting information and allowing secure communication.

Understanding how blockchain transactions function necessitates an understanding cryptography, which necessitates knowledge of mathematics, statistics, and computer science.

Start learning about cryptography if you want to become a web3 developer, so you’ll be prepared when it comes to learning about blockchain technology.

Conclusion

The skills that a web3 developer will need to construct the next generation of software are outlined in this article. These abilities are highly diverse, and anyone interested in working in this profession can learn them over time.

There is a section at the conclusion of this article that provides resources that can assist you in learning the skills you need to become a web3 developer.

Key takeaways

You now know how to construct Web3 applications. It isn’t rocket science, and we believe that anyone who is interested in the technology and believes in it can become a Web3 developer. Furthermore, you’ve realized that there are a variety of routes you might follow on this voyage. With online education on the rise, you can choose from a variety of free and paid options. As a result, we’ll leave it up to you to determine what to do next. However, the following rules should be followed to get to the finish line with the least amount of resistance:

  • Learn JavaScript if you want to make a proper frontend.
  • The Ethereum blockchain can assist in the development of scalable applications.
  • If the need for one-of-a-kind smart contracts arises, learn how to make your own.
  • In the development phase, analyse and solve difficulties.
  • Enhance the development and implementation process
  • Adopt Web3 and blockchain development best practices.

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

Parthiban Marimuthu
Parthiban Marimuthu

I'm Parthiban and I have more than one year experience in Machine Learning and Deep Learning. A Mechanical Engineering enthusiast with the passion to learn more about Machine Learning and emerging technology in the world. Study and transform data science prototypes and Design machine learning systems. Research and implement appropriate ML algorithms and tools. Develop machine learning applications and select appropriate datasets and data representation methods. Perform statistical analysis and fine-tuning using test results, and extend existing ML libraries and frameworks.

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