🚀 Complete Data Science Roadmap (2026)

📍 Phase 1: Programming Fundamentals (Week 1–2)

• Python Basics

• Variables & Data Types

• Operators

• Strings

• Lists

• Tuples

• Sets

• Dictionaries

• Functions

• Loops

• Conditional Statements

• Exception Handling

• File Handling

• Modules & Packages

• Virtual Environments

•

Object-Oriented Programming (Basics)

Practice

•

50+ Python coding questions

• Mini Python projects

📍 Phase 2: Mathematics for Data Science (Week 3–4)

Statistics

• Mean, Median, Mode

• Variance

• Standard Deviation

• Percentiles

• Quartiles

• Skewness

• Kurtosis

• Normal Distribution

• Central Limit Theorem

• Hypothesis Testing

• Confidence Intervals

•

A/B Testing

Probability

•

Probability Basics

• Conditional Probability

• Bayes' Theorem

• Random Variables

• Probability Distributions

•

Expected Value

Linear Algebra

•

Vectors

• Matrices

• Matrix Operations

• Eigenvalues

•

Eigenvectors

Calculus (Basic)

•

Derivatives

• Gradients

• Partial Derivatives

📍 Phase 3: SQL for Data Science (Week 5)

SQL Basics

• SELECT

• WHERE

• ORDER BY

• LIMIT

•

DISTINCT

Intermediate SQL

•

GROUP BY

• HAVING

• CASE WHEN

• Joins

• UNION

•

Views

Advanced SQL

•

Subqueries

• CTEs

• Window Functions

• Ranking Functions

•

Recursive CTEs

Practice

•

200+ SQL interview questions

• Real-world business case studies

📍 Phase 4: Data Analysis with Python (Week 6–7)

NumPy

• Arrays

• Indexing

• Broadcasting

•

Vectorization

Pandas

•

Series

• DataFrames

• Reading Files

• Data Cleaning

• Missing Values

• GroupBy

• Merge

•

Pivot Tables

Data Visualization

•

Matplotlib

• Seaborn

•

Plotly

Exploratory Data Analysis (EDA)

•

Univariate Analysis

• Bivariate Analysis

• Multivariate Analysis

• Correlation Analysis

• Outlier Detection

📍 Phase 5: Data Preprocessing (Week 8)

• Missing Value Handling

• Duplicate Removal

• Outlier Detection

• Feature Scaling

• Encoding

• Date Feature Extraction

• Text Cleaning

• Data Transformation

• Data Validation

📍 Phase 6: Feature Engineering (Week 9)

• Feature Creation

• Feature Transformation

• Feature Scaling

• Feature Encoding

• Interaction Features

• Polynomial Features

• Binning

• Time-based Features

• Text Features

📍 Phase 7: Machine Learning Fundamentals (Week 10–12)

Supervised Learning

• Linear Regression

• Logistic Regression

• Decision Trees

• Random Forest

• KNN

• SVM

•

Naive Bayes

Unsupervised Learning

•

K-Means

• Hierarchical Clustering

• DBSCAN

• PCA

📍 Phase 8: Model Evaluation (Week 13)

• Accuracy

• Precision

• Recall

• F1 Score

• ROC-AUC

• MAE

• MSE

• RMSE

• R² Score

• Confusion Matrix

• Cross Validation

• Hyperparameter Tuning

• Grid Search

• Random Search

📍 Phase 9: Advanced Machine Learning (Week 14–15)

Ensemble Learning

• Bagging

• Boosting

• AdaBoost

• Gradient Boosting

• XGBoost

• LightGBM

• CatBoost

• Feature Importance

• Model Explainability (SHAP, LIME)

📍 Phase 10: Time Series Analysis (Week 16)

• Trend

• Seasonality

• Moving Average

• ARIMA

• SARIMA

• Prophet

• Forecast Evaluation

📍 Phase 11: Natural Language Processing (Week 17)

• Text Cleaning

• Tokenization

• Stop Words

• Stemming

• Lemmatization

• Bag of Words

• TF-IDF

• Word2Vec

• Sentiment Analysis

• Text Classification