/DS-Curriculum

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..

Table of Contents

Table of Contents
  1. Programming
  2. Mathematics & Statistics
  3. Machine Learning
  4. Evaluation Metrics
  5. Deep Learning

Programming

  1. Python

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Mathematics & Statistics

  1. Descriptive Statistics
  2. Data Distributions
  3. Statistical Testing
  4. Exploratory Data Analysis
  5. TOOLBOX: Pandas
  6. TOOLBOX: Numpy
  7. TOOLBOX: Matplotlib
  8. TOOLBOX: Seaborn

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Machine Learning

  • Supervised Learning

  1. Linear Regression
  2. Logistic Regression
  3. Decision Tree
  4. K-NN (K-Nearest Neighbors)
  5. Naive Bayes
  6. Support Vector Machine
  7. Random Forest
  8. XGBoost
  9. TOOLBOX: Scikit Learn
  10. CASE STUDY 1:
  11. CASE STUDY 2:

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  • Unsupervised Learning

  1. K-Means Clustering

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Evaluation Metrics

  • Supervised Learning

  1. Confusion Matrix
  2. Accuracy
  3. Precision
  4. Recall
  5. F Score
  6. ROC (Receiver Operating Characteristic)
  7. ROC AUC (Area Under Curve)
  8. MAE
  9. MSE

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  • Unsupervised Learning

  1. Elbow Method

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Deep Learning

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

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Capstone mini Project---

Capstone major Project---