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Data Science Roadmap ডেটা সায়েন্স রোডম্যাপData Scientist & AI Engineer

22 branches
230 topics
9 months
About this learning path

A single, classroom-ready tree covering every topic from the 9-month Full Stack Data Scientist bootcamp curriculum and the AI & Data Scientist roadmap — from first principles in math and SQL, all the way to deep learning, computer vision, NLP, MLOps and AI engineering.

Complete Data Science Roadmap — 22 Branches, 230 Topics

This data science roadmap is a free, classroom-ready learning path for beginners through advanced learners. It covers every topic from the 9-month Full Stack Data Scientist bootcamp — math, SQL, statistics, machine learning, deep learning, computer vision, NLP, MLOps, and AI engineering.

ডেটা সায়েন্স রোডম্যাপ — ২২টি ব্রাঞ্চ ও 230টি টপিকে সাজানো সম্পূর্ণ শেখার পথ। গণিত, Python, SQL, মেশিন লার্নিং, ডিপ লার্নিং, NLP ও MLOps থেকে AI ইঞ্জিনিয়ারিং পর্যন্ত।

6. Data Visualization

  • Matplotlib
  • Seaborn
  • Univariate
  • Bivariate
  • Multivariate
  • Correlation
  • Heatmap
  • Pairplot
  • Boxplot

7. Statistics

  • Data Types
  • Frequency Distribution
  • Mean
  • Median
  • Mode
  • Variance
  • Standard Deviation
  • Quartiles
  • IQR
  • Normal Distribution
  • Correlation
  • Probability

8. Git & GitHub

  • GitHub Profile
  • Repository
  • Portfolio
  • Version Control

9. Machine Learning

  • ML Fundamentals
  • ML Lifecycle
  • Data Preparation
  • EDA
  • Feature Engineering
  • Feature Selection
  • Model Selection
  • Cross Validation
  • Hyperparameter Tuning
  • Regularization
  • Model Deployment Basics

10. Supervised Learning

  • Linear Regression
  • Logistic Regression
  • Naive Bayes
  • KNN
  • Decision Tree
  • Random Forest
  • Bagging
  • Boosting
  • AdaBoost
  • Gradient Boosting
  • XGBoost
  • Stacking

11. Model Evaluation

  • Train/Test Split
  • Confusion Matrix
  • Accuracy
  • Precision
  • Recall
  • F1 Score
  • ROC
  • AUC
  • Bias vs Variance
  • Overfitting
  • Underfitting

12. Unsupervised Learning

  • KMeans
  • Hierarchical Clustering
  • PCA
  • Dimensionality Reduction

13. Web Scraping

  • Requests
  • BeautifulSoup
  • Selenium
  • Scrapy
  • Automation Projects

14. Recommendation Systems

  • Popularity Based
  • Content Based
  • Collaborative Filtering
  • Cosine Similarity
  • Evaluation

15. Real ML Projects

  • End-to-end ML Project Portfolio

16. Deployment

  • Streamlit
  • Flask
  • REST API
  • Heroku
  • AWS
  • Azure

17. Research Skills

  • Reading Papers
  • Finding Papers
  • Scientific Writing

18. Deep Learning

  • ANN
  • Perceptron
  • Backpropagation
  • Optimizers
  • Activation Functions
  • TensorFlow
  • Keras
  • Regularization
  • Neural Network Projects

19. Computer Vision

  • Image Processing
  • CNN
  • LeNet
  • AlexNet
  • VGG
  • Inception
  • Transfer Learning
  • Object Detection
  • YOLO
  • SSD
  • OpenCV
  • CV Projects

20. Natural Language Processing

  • Text Preprocessing
  • Tokenization
  • Stopwords
  • Lemmatization
  • POS Tagging
  • BoW
  • TF-IDF
  • Word2Vec
  • GloVe
  • RNN
  • LSTM
  • Seq2Seq

21. Business Intelligence

  • Power BI
  • Power Query
  • DAX
  • Dashboard
  • Data Modeling
  • Reporting

22. Career

  • Portfolio
  • Kaggle
  • Resume
  • Interview
  • Internship
  • Freelancing
  • Job Preparation
START
FINISH — Data Scientist & AI Engineer

Data Science Roadmap — Frequently Asked Questions

What is a data science roadmap?

A data science roadmap is a structured learning path listing the skills, tools, and topics you need to become a data scientist or AI engineer — from math and programming through machine learning, deep learning, and deployment.

How long does it take to become a data scientist?

Most learners need 6–12 months of focused study for entry-level roles. This roadmap follows a 9-month intensive curriculum covering statistics, ML, deep learning, and MLOps.

What skills does a data scientist need?

Core skills include Python, SQL, statistics, data visualization, and machine learning. Advanced roles add deep learning, NLP, computer vision, and MLOps for production deployment.

ডেটা সায়েন্স শেখার ধাপ কী?

প্রথমে ডেটা সায়েন্সের মৌলিক ধারণা, তারপর গণিত ও পরিসংখ্যান, Python ও SQL, EDA ও ভিজুয়ালাইজেশন, মেশিন লার্নিং, ডিপ লার্নিং, এবং শেষে MLOps ও ক্যারিয়ার প্রস্তুতি — এই রোডম্যাপে ১৮টি ব্রাঞ্চে সাজানো আছে।

ডেটা সায়েন্স রোডম্যাপ কী?

ডেটা সায়েন্স রোডম্যাপ হলো একটি পরিকল্পিত শেখার পথ যেখানে প্রতিটি টপিক ক্রমানুসারে সাজানো — ফাউন্ডেশন থেকে শুরু করে AI ইঞ্জিনিয়ারিং ও ক্যাপস্টোন প্রজেক্ট পর্যন্ত।

What is the difference between a data scientist and an AI engineer?

Data scientists focus on analysis, modeling, and insights. AI engineers build and deploy production ML/AI systems including APIs, pipelines, and MLOps. This roadmap covers both tracks.

Is this data science roadmap free?

Yes. The Skillio data science roadmap is free to browse with 230 topics across 22 branches, an interactive progression view, and links to foundation lectures.

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