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.

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

Mathematics & Statistics

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

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

• Unsupervised Learning

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

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

• Unsupervised Learning

1. Elbow Method
2. Silhouette Coefficient

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

ML Applications

1. Timeseries
2. Recommendation System
3. Netwok Analysis

Computer Vision

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

NLP & NLU

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

Speech Recognition

Model Deployment

Book References

1. Practical Deep Learning for Coders
2. Dive Into Deep Learning
3. Interpretable Machine Learning