Road Map - Data Science

 

🎯 Ultimate Data Science Roadmap (2025)

From Beginner to Advanced – Tools, IDEs & Applications

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🟢 Stage 1: Foundation (Beginner Level)

Focus: Core Concepts, Basic Tools, and Initial Programming Skills

📘 Topics to Learn:

• What is Data Science? Roles & Applications

• Basics of Programming (Python or R)

• Statistics & Probability Fundamentals

• Linear Algebra & Matrices

• Data Types and Structures

• Git and Version Control

• Introduction to Data Manipulation using Pandas/Numpy

• Data Visualization Basics (Matplotlib, Seaborn)

🧰 Tools/IDEs:

• IDEs: Jupyter Notebook, VS Code, Google Colab

• Languages: Python, R (Choose one)

• Version Control: Git + GitHub

• Resources: Kaggle Datasets, Google Colab, Medium Blogs

🧪 Mini Projects:

• Exploratory Data Analysis (EDA) on Titanic Dataset

• COVID-19 Visualization Dashboard

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🟡 Stage 2: Core Skills (Intermediate Level)

Focus: Data Processing, Machine Learning, and Real Projects

📘 Topics to Learn:

• Data Cleaning & Wrangling

• Advanced Data Visualization (Plotly, Dash, Tableau)

• SQL for Data Science (Joins, Subqueries, Window Functions)

• Feature Engineering

• Introduction to Machine Learning:

  • Supervised Learning (Regression, Classification)

  • Unsupervised Learning (Clustering, PCA)

• Model Evaluation & Tuning (Cross-Validation, Grid Search)

• Introduction to APIs and Web Scraping (BeautifulSoup, Requests)

🧰 Tools/IDEs:

• IDEs: Jupyter, PyCharm, Google Colab

• Libraries: Scikit-learn, XGBoost, LightGBM

• Database: MySQL, PostgreSQL

• Visualization: Power BI, Tableau

🧪 Mini Projects:

• House Price Prediction (Regression)

• Customer Segmentation (Clustering)

• Sales Dashboard (Power BI / Tableau)

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🟠 Stage 3: Advanced Data Science

Focus: Deep Learning, Big Data, Deployment

📘 Topics to Learn:

• Deep Learning Fundamentals

  • Neural Networks (ANN, CNN, RNN)

  • Frameworks: TensorFlow, Keras, PyTorch

• Natural Language Processing (NLP):

  • Text Classification, Sentiment Analysis, Transformers (BERT)

• Time Series Forecasting

• Big Data Tools:

  • Hadoop, Spark, Hive

• Model Deployment:

  • Flask/Django for API

  • Streamlit, FastAPI, Gradio

• MLOps Basics (CI/CD, Docker, MLflow)

🧰 Tools/IDEs:

• Deep Learning: TensorFlow, PyTorch, Keras

• Big Data: Apache Spark, Databricks

• Deployment: Docker, Flask, FastAPI, Streamlit

• Cloud: AWS, GCP, Azure (S3, EC2, Vertex AI, SageMaker)

• Experiment Tracking: MLflow, Weights & Biases

🧪 Mini Projects:

• Image Classification with CNN (Cats vs Dogs)

• Sentiment Analysis using BERT

• Stock Price Forecasting (LSTM)

• End-to-End ML App with Streamlit + Flask API

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🔵 Stage 4: Specialization & Real-world Applications

Focus: Domain Knowledge, Capstone Projects, and Portfolio Building

📘 Specializations:

• Computer Vision

• NLP & Generative AI

• Time Series Forecasting

• Reinforcement Learning

• Bioinformatics / Health Data

• Finance & Risk Modeling

🌐 Applications:

• Fraud Detection Systems

• Chatbots & Virtual Assistants

• Recommender Systems (e.g., Netflix, Amazon)

• Image Recognition (Self-driving cars, OCR)

• AI for Healthcare (Diagnosis prediction)

• Customer Churn Prediction

🧰 Tools:

• AutoML: Google AutoML, H2O.ai, DataRobot

• AI Tools: OpenAI APIs, HuggingFace Transformers

• Project Hosting: GitHub, HuggingFace Spaces, Streamlit Cloud

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📁 Suggested Learning Path (Timeline)

Duration	Focus
Month 1-2	Python, Math, Stats, Git
Month 3-4	EDA, Pandas, SQL, Visualization
Month 5-6	ML Algorithms, Model Tuning
Month 7-8	Deep Learning, NLP, Time Series
Month 9-10	Big Data, Deployment, Cloud
Month 11-12	Capstone Projects, Portfolio

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📚 Recommended Resources

Courses:

• Free: Google Data Analytics (Coursera), Kaggle Courses

• Paid: Data Science Specialization by Johns Hopkins (Coursera), MITx MicroMasters (edX)

Books:

• “Python for Data Analysis” – Wes McKinney

• “Hands-On ML with Scikit-Learn & TensorFlow” – Aurélien Géron

• “Deep Learning” – Ian Goodfellow

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💼 Portfolio & Resume Boosters

• Build 3-5 major projects (end-to-end)

• Upload on GitHub with ReadMe

• Create a LinkedIn profile with regular posts

• Contribute to Kaggle Competitions

• Deploy ML Apps on Streamlit or HuggingFace