What You'll Learn
Prerequisites
- No prior programming experience needed
- Basic computer knowledge
- A laptop with internet access
- Enthusiasm to learn!
Tools & Setup
📖 Tutorial Chapters & Curriculum
Follow the structured learning path from beginner to advanced
1
Data Science Roadmap: What I Learned as a Beginner
A student-style roadmap to learn data science step by step without confusion.
2
Math for Data Science: What to Learn First (No Overwhelm)
A focused math path for data science: only what you actually need.
3
Statistics Basics: Mean, Variance, and Why It Matters
Learn core statistics concepts used daily in data analysis.
4
Python Setup for Data Science: VS Code, Jupyter, and Environments
Set up Python properly so your projects run smoothly.
5
Data Cleaning: Missing Values and What I Do First
Simple rules for handling missing values and duplicates safely.
6
Supervised Learning: Regression vs Classification (Simple)
Understand the two main types of supervised learning with examples.
7
Unsupervised Learning Explained: When Labels Do Not Exist
Learn when to use unsupervised machine learning and how it discovers hidden patterns.
8
Feature Engineering Intuition: Why Features Matter
Learn how better features improve machine learning models.
9
Deep Learning Introduction: Neural Networks Simplified
Learn neural network basics and how deep learning works.
10
NLP Basics: Tokenization and Text Processing
Learn how machines process and understand human language.
11
Computer Vision Basics: How Machines See Images
Learn how machines interpret images using computer vision.
12
Time Series Analysis Basics: Understanding Sequential Data
Learn how time series data works and why it is important in forecasting.
13
Machine Learning Model Deployment Introduction
Learn how trained ML models are deployed into production systems.
14
Business Case Studies in Data Science: How I Approach Real Problems
Learn a practical step-by-step approach to solve real business problems using data science.
15
Advanced Data Science Interview Plan: What to Study in 14 Days
A simple 14-day plan to revise data science topics for interviews.
16
Data Science Lifecycle: From Problem to Product
Learn the practical lifecycle used in real projects: framing, data, modeling, evaluation, deployment.
17
Vectors and Matrices for ML: Intuition + Quick Examples
Learn vectors/matrices with intuition so ML formulas feel simple.
18
Distributions: Normal, Skewed, and Long-Tail (With Intuition)
Understand distributions so you can interpret data correctly.
19
Pandas Basics: Read CSV, Filter, GroupBy (What I Use Most)
Practical pandas operations used in almost every real dataset.
20
Outlier Detection: IQR and Z-Score (Easy Way)
Learn outlier detection methods and when not to remove outliers.
21
Train-Test Split and Cross-Validation (Beginner Friendly)
Learn how to test your model properly and avoid fooling yourself.
22
K-Means Clustering Intuition: How Groups Are Created
Learn how K-Means clustering groups data points based on similarity.
23
Feature Scaling: Normalization vs Standardization
Understand feature scaling methods used before training models.
24
Activation Functions: ReLU, Sigmoid and Tanh
Understand how activation functions help neural networks learn complex patterns.
25
Word Embeddings: Word2Vec and GloVe Explained
Learn how word embeddings represent text numerically.
26
Image Processing with OpenCV
Learn basic image processing operations using OpenCV.
27
Time Series Components: Trend, Seasonality and Noise
Understand key components used in forecasting models.
28
Creating Machine Learning APIs using FastAPI
Learn how to serve machine learning models using APIs.
29
Recommendation System Case Study: Netflix/Amazon Style (Real World)
Understand how recommendation systems work in real products using simple examples.
30
SQL Interview Questions for Data Scientists (Advanced Set)
Practice high-frequency SQL interview questions used in analytics and DS roles.
31
Roles in Data Science: Data Analyst vs Data Scientist vs ML Engineer
Clear difference between common roles so you can pick your career path.
32
Dot Product and Cosine Similarity (Super Practical)
Understand similarity scoring used in recommendations and embeddings.
33
Probability Basics: Conditional Probability & Bayes Theorem
Learn probability rules and Bayes theorem in a student-friendly way.
34
NumPy Basics: Arrays and Broadcasting Explained
Understand NumPy arrays so ML math becomes easy.
35
EDA Checklist Before Modeling (My Practical Flow)
A simple EDA checklist to understand data before machine learning.
36
Linear Regression: Intuition, Metrics, and Common Pitfalls
Understand linear regression and evaluate it using MAE/RMSE.
37
Elbow Method: Choosing the Right Number of Clusters
Learn how to determine optimal cluster count using the elbow method.
38
Encoding Categorical Variables for Machine Learning
Learn how to convert categorical data into numerical features.
39
Backpropagation and Gradient Descent Explained
Learn how neural networks update weights during training.
40
Sentiment Analysis and Text Classification
Learn how models classify text sentiment.
41
Object Detection Algorithms: YOLO Explained
Learn object detection techniques used in real world AI systems.
42
Moving Average and Smoothing Techniques
Learn smoothing techniques used for forecasting.
43
Docker for Machine Learning Deployment
Learn containerization concepts for ML systems.
44
Fraud Detection Case Study: How ML Finds Suspicious Transactions
Learn how machine learning is used in fraud detection with practical thinking.
45
Machine Learning Interview Questions (Concept + Practical)
Prepare ML interview questions with simple explanation and where each algorithm is used.
46
Data Science Tools Stack: Python, SQL, Notebooks, Git
A practical overview of tools you will actually use in projects.
47
Gradients and Derivatives: Why Models Learn
Understand gradients in a simple way for gradient descent and learning.
48
Hypothesis Testing & P-Values (Beginner Friendly)
Understand p-values and hypothesis testing without memorizing formulas.
49
Data Visualization: Matplotlib Quick Starter (Student Friendly)
Create simple plots that immediately help in EDA.
50
Correlation vs Causation: Mistakes I Made as a Beginner
Learn the difference and avoid wrong conclusions in analysis.
51
Logistic Regression Simplified: Probability to Decision
Learn logistic regression for classification and confusion matrix basics.
52
Hierarchical Clustering Explained Simply
Understand hierarchical clustering and dendrogram visualization.
53
Feature Selection Techniques to Reduce Overfitting
Learn how feature selection improves model accuracy and reduces noise.
54
Convolutional Neural Networks Intuition
Learn CNN architecture used in image recognition.
55
Transformer Models Introduction: BERT and GPT
Learn transformer architecture used in modern NLP models.
56
Image Segmentation in Deep Learning
Learn how segmentation models divide images into meaningful regions.
57
ARIMA Model Explained for Beginners
Learn ARIMA model used in time series forecasting.
58
MLOps Pipelines and CI/CD for AI Systems
Learn continuous integration and deployment for machine learning.
59
Customer Churn Prediction Case Study: Retaining Users with Data
Learn churn prediction approach used by subscription and e-commerce businesses.
60
ML System Design Interview Basics: From Notebook to Production
Learn how to explain ML deployment, monitoring, and pipelines in interviews.
61
My First Data Science Portfolio: Simple Projects That Work
Beginner-friendly project ideas you can complete and showcase.
62
Optimization Basics: Gradient Descent in Plain Language
Learn the idea of minimizing loss and improving models step by step.
63
Confidence Intervals: Margin of Error Explained Simply
Learn how confidence intervals help estimate unknown values from samples.
64
Python Clean Code for Data Projects: Functions, Files, and Reuse
Write cleaner code so your notebooks become real projects.
65
Feature Distributions: Log Scaling and Transformations (Quick Guide)
Learn when to apply log scaling and other transformations.
66
Classification Metrics: Precision, Recall, F1 (No Confusion)
Learn metrics with an easy “spam filter” style explanation.
67
DBSCAN Clustering and Anomaly Detection
Learn density based clustering useful for anomaly detection.
68
Hyperparameter Tuning: Grid Search vs Random Search
Learn how to optimize machine learning models using hyperparameter tuning.
69
Recurrent Neural Networks for Sequence Data
Learn how RNN models process time series and text sequences.
70
Chatbots and Conversational AI Basics
Learn how chatbots are built using NLP techniques.
71
Face Recognition Systems Explained
Learn how face recognition systems identify individuals using AI.
72
LSTM Networks for Time Series Prediction
Learn how deep learning models forecast sequential data.
73
Monitoring Machine Learning Models in Production
Learn how to monitor model accuracy and drift in production.
74
Demand Forecasting Case Study: Retail Sales Prediction (Practical)
Understand retail demand forecasting workflow and model evaluation.
75
How I Explain My Data Science Projects in Interviews (Winning Format)
Learn a clean structure to explain projects: problem, data, approach, results, business impact.
🎯 Interview Preparation
Top Python interview questions organized by difficulty
Easy
60 Questions
Freshers / Entry Level
- Explain Hypothesis Testing concepts in depth and discuss how you would apply them in a real-world FAANG-scale data science problem. (Q1)
- Explain Linear Algebra concepts in depth and discuss how you would apply them in a real-world FAANG-scale data science problem. (Q2)
- Explain SQL & Data Manipulation concepts in depth and discuss how you would apply them in a real-world FAANG-scale data science problem. (Q3)
- Explain Feature Engineering concepts in depth and discuss how you would apply them in a real-world FAANG-scale data science problem. (Q4)
- Explain Regression Models concepts in depth and discuss how you would apply them in a real-world FAANG-scale data science problem. (Q5)
Medium
60 Questions
Experienced / Mid-Level
- Explain Hypothesis Testing concepts in depth and discuss how you would apply them in a real-world FAANG-scale data science problem. (Q61)
- Explain Linear Algebra concepts in depth and discuss how you would apply them in a real-world FAANG-scale data science problem. (Q62)
- Explain SQL & Data Manipulation concepts in depth and discuss how you would apply them in a real-world FAANG-scale data science problem. (Q63)
- Explain Feature Engineering concepts in depth and discuss how you would apply them in a real-world FAANG-scale data science problem. (Q64)
- Explain Regression Models concepts in depth and discuss how you would apply them in a real-world FAANG-scale data science problem. (Q65)
Hard
60 Questions
Senior / Lead Level
- Explain Hypothesis Testing concepts in depth and discuss how you would apply them in a real-world FAANG-scale data science problem. (Q121)
- Explain Linear Algebra concepts in depth and discuss how you would apply them in a real-world FAANG-scale data science problem. (Q122)
- Explain SQL & Data Manipulation concepts in depth and discuss how you would apply them in a real-world FAANG-scale data science problem. (Q123)
- Explain Feature Engineering concepts in depth and discuss how you would apply them in a real-world FAANG-scale data science problem. (Q124)
- Explain Regression Models concepts in depth and discuss how you would apply them in a real-world FAANG-scale data science problem. (Q125)
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Web Design
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