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 (13)
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This is a great breakdown of the different skill layers! Data science can definitely feel like a heavy "sandwich" to digest sometimes. When I need a quick mental break from all the statistics and coding, I find it really helpful to reset with the coloring pages at YoloColoring.com. It’s a nice way to stay creative while letting the technical concepts sink in!
The idea of the “Data Science Sandwich” is a fun and memorable way to think about how different layers of skills come together in data science, from working with data and tools to applying analytical thinking and communication. Data science itself blends several disciplines—such as statistics, programming, data preparation, and visualization—to extract useful insights from data and support decision-making. Framing this process with a simple metaphor makes the learning path feel less intimidating for beginners who are trying to understand how the pieces fit together. Reading posts like this also reminds me that creativity plays a role even in technical fields, and sometimes I recharge that creative side by exploring visual ideas like Planse de colorat on Planse-Colorat.com, which offer a relaxing contrast to analytical thinking.
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Totally agree! The way The Data Science Sandwich breaks things down really does make complex concepts more digestible — kind of like assembling delicious meals layer by layer. Such a smart and relatable metaphor!
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