Data Science Roadmap

Introduction to Data Science

What is data science, roles, lifecycle, real-world applications.

Types of Data Problems

Classification, regression, clustering, recommendation.

Checkpoint — Data Science Basics

Mathematics Foundations

Linear algebra, probability, statistics.

Programming Basics

Python basics, functions, OOP, scripting.

Checkpoint — Strong Foundations

Python for Data Science

Data Manipulation

NumPy, Pandas, dataframes, indexing, filtering.

Data Visualization

Matplotlib, Seaborn, exploratory visual analysis.

Checkpoint — Python Mastery

Data Handling

Data Collection

CSV, databases, APIs, web scraping.

Data Cleaning

Missing values, duplicates, outliers, transformation.

Checkpoint — Clean Data

Exploratory Data Analysis

Distributions, correlations, feature understanding.

Statistical Analysis

Hypothesis testing, confidence intervals, regression.

Checkpoint — Data Insights

Machine Learning

Supervised Learning

Linear regression, logistic regression, decision trees.

Unsupervised Learning

K-means clustering, PCA, anomaly detection.

Checkpoint — ML Foundations

Model Evaluation

Train/test split, cross-validation, metrics.

Feature Engineering

Scaling, encoding, feature selection.

Checkpoint — Model Optimization

Deep Learning

Neural Networks

ANNs, backpropagation, activation functions.

Advanced DL

CNNs, RNNs, TensorFlow, PyTorch.

Checkpoint — Deep Learning

Big Data & Deployment

Big Data Tools

Hadoop, Spark, distributed computing basics.

Model Deployment

APIs, model serving, monitoring, versioning.

Checkpoint — Production Systems

Projects & Portfolio

End-to-end projects, Kaggle, case studies.

Continuous Learning

Research papers, new tools, industry trends.

Checkpoint — Industry Ready Data Scientist