Roadmap

Data Analyst Roadmap

Maths & Stats
Excel
Python
SQL & Database
Data Visualization
Data Visualization (Power BI)
BI Tools (Tableau)
Data Preparation & Validation
Exploratory Data Analysis
Data Ethics & Privacy
Business Understanding
Data Presenting

Blockchain developer Roadmap

L1: Blockchain basics
L2: Git & Github
L3: HTML & CSS
L4: CSS framework
L5: Typescript/Javascript
L6: React
L7: Node.js
L8: web3.js/Ether.js
L9: Solidity
Ship projects after L3

Front-End developer Roadmap

AI Agentic Roadmap

Web3 developer Roadmap

HTML & CSS
Javascript/Typescript
Reactjs/Nectjs(JS Framework) + Web3.js/Ether.js
learn blockchain Basics
Solidity (Programming language)
hardhat /Truffle
Alchemy / Infura
make 4-5 Project (DApps)
Apply for a Job

Web Developer Roadmap

Web fundamental: GIT & Github, Terminal, Code Editor
Frontend: HTML, CSS, JavaScript
Framework: Bootstrap, React.js, Angular.js, Vue.js (Frontend), NodeJS (Backend)
Design: Figma, Photoshop, Adobe XD
Backend: SQL, Python, JavaScript, PHP, Rest API's
Extra: Git, Github, Media Query (Responsive), Library
Algorithm: Data Structure, Time & Space Complecity

Machine Learning Engineer Roadmap

1. Machine Learning Fundamentals

• Scikit-Learn: Core library for implementing ML algorithms.
• Supervised Learning: Predict outcomes using labeled data (e.g., regression, classification).
• Unsupervised Learning: Discover patterns in unlabeled data (e.g., clustering, dimensionality reduction).
• Reinforcement Learning: Train agents to make decisions by maximizing rewards.

2. ML Libraries and Tools

• ML Libraries: Scikit-learn, TensorFlow, PyTorch, etc., for building and deploying models.
• Non-ML Libraries: NumPy, Pandas, Matplotlib for data manipulation, analysis, and visualization.

3. Key ML Algorithms

• Linear Regression: Predict continuous outcomes.
• Logistic Regression: Classify binary or multi-class outcomes.
• K-Nearest Neighbors (KNN): Classify based on proximity to data points.
• K-Means: Cluster data into K groups.
• Random Forest: Ensemble method for robust classification/regression.
• And More: Explore SVM, Decision Trees, Neural Networks, etc.

Programming: Python, R, Java, SQL for data querying, Bash for automation, and C++ for performance-critical tasks. JavaScript enables web-based ML deployment, while Scala supports big data frameworks like Spark. Go is ideal for scalable microservices, and Julia suits research. Prioritize SQL and Bash, then focus on languages aligned with your role, such as C++ for production or JavaScript for web applications.
Download as image   Additional ML roadmap 1   Additional ML roadmap 2   Additional ML roadmap 3   Additional ML roadmap 4

Full Stack Roadmap

Day 1-20 learn HTML & CSS
Day 21-35 learn JavaScript
Day 36-46 learn Bootstrap or Tailwindcss
Day 47-60 learn React
Day 142-160 learn GIT and GITHUB
Day 121-141 learn MySql or MongoDB
Day 91-120 learn Django or Nodejs
Day 61-90 learn Python or Advance JavaScript
Day 161-172 learn Rest API or JSON API
Day 173-185 learn AWS or Google Cloud
Day 186-200 Revise
Start building projects

Full Stack Java Developer Roadmap

Frontend: Basics (HTML, CSS, JavaScript), Frontend Frameworks (Angular, ReactJS, VueJs), IDE (VS Code)
Backend Core Java, Restful Webservices (Springboot, Jersey Rest, RestEasy), JPA impl (Hibernate, EclipseLink, Spring security)
SQL Database: PostareSQL, MS SQL, MySQL, Oracle
No-SQL Database: MongoDB, ElasticSearch
Tools: Jenkins, Docker, GIT, Eclipse & IntelliJ Idea

11 steps to go from Junior to Senior Developer

• Collaboration Tools:
  Software development is a social activity. Learn to use collaboration tools like Jira, Confluence, Slack, MS Teams, Zoom, etc.
• Programming Languages:
  Pick and master one or two programming languages. Choose from options like Java, Python, JavaScript, C#, Go, etc.
• API Development:
  Learn the ins and outs of API Development approaches such as REST, GraphQL, and gRPC.
• Web Servers and Hosting:
  Know about web servers as well as cloud platforms like AWS, Azure, GCP, and Kubernetes
• Authentication and Testing:
  Learn how to secure your applications with authentication techniques such as JWTs, OAuth2, etc. Also, master testing techniques like TDD, E2E Testing, and Performance Testing
• Databases:
  Learn to work with relational (Postgres, MySQL, and SQLite) and non-relational databases (MongoDB, Cassandra, and Redis).
• CI/CD:
  Pick tools like GitHub Actions, Jenkins, or CircleCI to learn about continuous integration and continuous delivery.
• Data Structures and Algorithms:
  Master the basics of DSA with topics like Big O Notation, Sorting, Trees, and Graphs.
• System Design:
  Learn System Design concepts such as Networking, Caching, CDNs, Microservices, Messaging, Load Balancing, Replication, Distributed Systems, etc.
• Design patterns:
  Master the application of design patterns such as dependency injection, factory, proxy, observers, and facade.
• AI Tools:
  To future-proof your career, learn to leverage AI tools like GitHub Copilot, ChatGPT, Langchain, and Prompt Engineering.

Java Roadmap

JavaScript Roadmap

Data Science Roadmap

An essential step to becoming a scientist is to learn methods and protocols to avoid deluding yourself into believing false things. You learn that by doing a PhD and getting your research past your advisor and getting your publications to survive peer review. — Yann LeCun

A junior data scientist is learning how to make more complex models 🤯
A senior data scientist is learning how to make simpler models 💡

Month 1: Basic Python
Month 2: Stats & Probability
Month 3: Advanced Python
Month 4: Visualization
Month 5: Machine Learning
Month 6: Data Manipulation
Month 7: Deployment
Month 8: Deep Learning
Month 9: CV/NLP
Month 10: Interview preparation
Month 11: Projects & Resume Prep
Month 12: Success
See: A week in the life of an IBM data scientist

Python roadmap for data science

1. Learn the basics of Python programming: Control structures, Functions, Modules, Syntax, Data types
2. Familiarize yourself with essential data science libraries: Numpy, Pandas, Matplotlib
3. Learn statistics and mathematics
4. Dive into machine learning (Supervised learning, Unsupervised learning, Regression, Clustering, Classification)
5. Work on projects
6. Keep up with the latest trends and developments

DevOps Roadmap

DevOps Basics → Cloud Basics (Oracle / AWS) > Linux Basics → Git Basics → Jenkins CI/CD Pipelines → Ansible → Docker → Kubernetes → Terraform → Pythin Basics → AWS or Azure Pipelines (CI/CD) → Project Work → Hands-on Labs → CV Interview preparation.

Download DevOps RoadMap by © @VrashTwt
Download Technical DevOps RoadMap

Automation consulting

Programming in Java, J2EE, Servlet, EJB, WebServis, XML, Rest, Spring, Nodejs, SQL
Practical experience on WebSphere, Liberty, MQ and ECM
Good knowledge about Linux / Unix systems / Cloud architecture (such as C)ontainers, Kubernetes, Openshift and Devops Methodology)
Experience in integration and Agile

Quantum Computer Scientist

Proficiency in Python with good knowledge of Jupyter, Tensor, Numpy
Familiarity with Qiskit and quantum concepts and principles

Automation consulting

Preferred Technical and Professional Expertise: ISTQB certification and A4Q – Selenium certification
Experience of writing test automation scripts using Selenium, Protractor, Katalon, Cucumber.
Model office testing, experience in performing end to end testing and UAT testing, business testing.
Contemporary technology trends: Agile, DevOps, Containers

Site Reliability engineer

Software development and delivery role
Cloud/DevOps engineering and/or Linux administration
container orchestration on a public cloud provider or large scale private/hybrid cloud
modern configuration management framework (Puppet, Ansible, Chef, etc.)
Production experience with one or more monitoring frameworks (Nagios, Prometheus, etc.)
Strong scripting skills in at least one language (BASH, Python, Ruby, etc.)
Experience with source control management such (git, subversion, etc.)
software development life cycle and delivery process.