/data-science-learning-path

Data Science Learning Path - A complete guide to learn data science for beginners

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Brief Introduction

A complete guide to learn data science for beginners.

This learning path is intended for everyone who wants to learn data science and build a career in data field especially data analyst and data scientist. In this guide, there is a corresponding link in each section that will help you to learn (at least to start) in each chapter.

Table of Contents

Table of Contents
  1. Programming
  2. Mathematics & Statistics
  3. Machine Learning
  4. Evaluation Metrics
  5. Deep Learning
  6. ML Applications
  7. Computer Vision
  8. NLP & NLU
  9. Speech Recognition
  10. Model Deployment
  11. Book References

Programming

  1. Basic Python
  2. Object-oriented Programming
  3. Intro to DBMS
  4. SQL Data Manipulation
  5. Git
  6. Code Versioning Platform: Github | Bitbucket | Gitlab
  7. Shell Script
  8. Competitive Programming: Hackerrank | Leetcode | Kattis

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Mathematics & Statistics

  1. Linear Algebra
  2. Calculus
  3. Descriptive Statistics
  4. Data Distributions
  5. Statistical Testing
  6. Exploratory Data Analysis
  7. Regression
  8. TOOLBOX: Pandas
  9. TOOLBOX: Numpy
  10. TOOLBOX: Matplotlib
  11. TOOLBOX: Seaborn

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Machine Learning

  • Supervised Learning

  1. K-NN (K-Nearest Neighbors)
  2. Naive Bayes
  3. Support Vector Machine
  4. Random Forest
  5. AdaBoost
  6. Gradient Boosting
  7. XGBoost
  8. CatBoost
  9. Bagging Classifier
  10. Voting Classifier
  11. Stacking Classifier
  12. TOOLBOX: Scikit Learn
  13. TOOLBOX: statsmodels
  14. CASE STUDY: House Pricing
  15. CASE STUDY: Titanic
  16. CASE STUDY: Credit Scoring

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  • Unsupervised Learning

  1. K-Means Clustering
  2. DBSCAN
  3. Hierarchical Clustering

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Evaluation Metrics

  • Supervised Learning

  1. Confusion Matrix
  2. Accuracy
  3. Precision
  4. Recall
  5. F Score
  6. Hamming Loss
  7. ROC (Receiver Operating Characteristic)
  8. ROC AUC (Area Under Curve)
  9. Top K Accuracy
  10. MAE
  11. MSE
  12. MRR
  13. DCG
  14. NDCG
  15. PSNR
  16. SSIM
  17. IoU
  18. Perplexity
  19. BLEU score

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  • Unsupervised Learning

  1. Elbow Method
  2. Silhouette Coefficient

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Deep Learning

  1. Activation Functions
  2. Linear Layer
  3. CNN (Convolutional Neural Networks)
  4. RNN (Recurrent Neural Networks)
  5. Optimization
  6. Loss Functions / Objective Functions
  7. Dropout
  8. Batchnorm
  9. Learning Rate Scheduler
  10. TOOLBOX: PyTorch
  11. TOOLBOX: Tensorflow
  12. TOOLBOX: Keras

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ML Applications

  1. Timeseries
  2. Recommendation System
  3. Netwok Analysis

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Computer Vision

  1. Image Classification
  2. Object Detection
  3. Object Segmentation
  4. Instance Segmentation

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NLP & NLU

  1. Tokenization
  2. Sequence
  3. Padding
  4. Stemming
  5. Lemmatization
  6. Feature Extraction
  7. Feature Selection
  8. Term Weighting
  9. Embedding
  10. Part of Speech Tagging
  11. Named Entity Recognition
  12. Popular NLP & NLU Architecture
  13. STUDY CASE: News Classification
  14. STUDY CASE: Sentiment Analysis
  15. STUDY CASE: Machine Translation

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Speech Recognition

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Model Deployment

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Book References

  1. Practical Deep Learning for Coders
  2. Dive Into Deep Learning
  3. Interpretable Machine Learning
  4. An Introduction to Statistical Learning with Applications in R
  5. Natural Language Processing with Python

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