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
Programming
 Basic Python
 Objectoriented Programming
 Intro to DBMS
 SQL Data Manipulation
 Git
 Code Versioning Platform: Github  Bitbucket  Gitlab
 Shell Script
 Competitive Programming: Hackerrank  Leetcode  Kattis
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Mathematics & Statistics
 Linear Algebra
 Calculus
 Descriptive Statistics
 Data Distributions
 Statistical Testing
 Exploratory Data Analysis
 Correlation
 Statistical Data Visualization
 Regression
 TOOLBOX: Pandas
 TOOLBOX: Numpy
 TOOLBOX: Matplotlib
 TOOLBOX: Seaborn
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Machine Learning

Supervised Learning
 KNN (KNearest Neighbors)
 Naive Bayes
 Support Vector Machine
 Random Forest
 AdaBoost
 Gradient Boosting
 XGBoost
 CatBoost
 Bagging Classifier
 Voting Classifier
 Stacking Classifier
 TOOLBOX: Scikit Learn
 TOOLBOX: statsmodels
 CASE STUDY: House Pricing
 CASE STUDY: Titanic
 CASE STUDY: Credit Scoring
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Unsupervised Learning
 KMeans Clustering
 DBSCAN
 Hierarchical Clustering
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Evaluation Metrics

Supervised Learning
 Confusion Matrix
 Accuracy
 Precision
 Recall
 F Score
 Hamming Loss
 ROC (Receiver Operating Characteristic)
 ROC AUC (Area Under Curve)
 Top K Accuracy
 MAE
 MSE
 MRR
 DCG
 NDCG
 PSNR
 SSIM
 IoU
 Perplexity
 BLEU score
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Unsupervised Learning
 Elbow Method
 Silhouette Coefficient
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Deep Learning
 Activation Functions
 Linear Layer
 CNN (Convolutional Neural Networks)
 RNN (Recurrent Neural Networks)
 Optimization
 Loss Functions / Objective Functions
 Dropout
 Batchnorm
 Learning Rate Scheduler
 TOOLBOX: PyTorch
 TOOLBOX: Tensorflow
 TOOLBOX: Keras
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ML Applications
 Timeseries
 Recommendation System
 Netwok Analysis
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Computer Vision
 Image Classification
 Object Detection
 Object Segmentation
 Instance Segmentation
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NLP & NLU
 Tokenization
 Sequence
 Padding
 Stemming
 Lemmatization
 Feature Extraction
 Feature Selection
 Term Weighting
 Embedding
 Part of Speech Tagging
 Named Entity Recognition
 Popular NLP & NLU Architecture
 STUDY CASE: News Classification
 STUDY CASE: Sentiment Analysis
 STUDY CASE: Machine Translation
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Speech Recognition
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Model Deployment
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