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
   • Supervised Learning
   • Unsupervised Learning
4. Evaluation Metrics
   • Supervised Learning
   • Unsupervised Learning
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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