Dive into the layers of data science with Rocky Bhatia's comprehensive guide:
- Mathematics
- Linear Algebra
- Analytic Geometry
- Matrix Vector Calculus
- Optimization Regression
- Dimensionality Reduction
- Density Estimation
Classification Probability Statistics
Programming
Python SQL
Database (MySQL, MongoDB)
Data Structure
Web Scraping
Linux GIT
Machine Learning
Introduction
Model Mechanics
Basic Data Exploration
First ML Model
Model Validation
Underfitting & Overfitting
Random Forests (Python | R)
scikit-learn Intermediate
Handling Missing Values
Handling Categorical Variables
Pipelines
Cross-Validation (R)
XGBoost (Python | R)
Data Leakage
Deep Learning
Artificial Neural Network
Convolutional Neural Network
Recurrent Neural Network
TensorFlow
Keras
PyTorch
Single Neuron Deep Neural Network
SGD, Overfitting, Underfitting
Dropout, Batch Normalization
Binary Classification
Feature Engineering
Baseline Model
Categorical Encodings
Feature Generation
Feature Selection
Natural Language Processing
Text Classification
Word Vectors
Data Visualization Tools
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Excel VBA BI (Business Intelligence)
- Tableau
- Power BI
- Qlik View
- Qlik Sense
Deployment
Microsoft Azure
Heroku
Google Cloud Platform
Flask DJango
DataBricks
Other Points
Domain Knowledge
Communication Skill
Reinforcement Learning
Case Studies (Netflix, Flipkart, Credit Card Fraud Detection, Movie Recommendation, etc.)
Keep Practicing
"Practice makes a man perfect." Save for later and stay tuned for more insights!
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Top comments (1)
This guide is like the ultimate "data science sandwich" packed with everything from math foundations to real-world deployment tips. Whether you're just starting out or brushing up your skills, it’s a goldmine of info! If you’re building something and need expert support, you can also hire python developer to speed up your progress. Thanks for sharing this, definitely bookmarking.