Python & Data Science Path

Description

Turn Raw Data Into Actionable Intelligence

The Python & Data Science Path takes you from Python beginner to confident data practitioner. Over 16 weeks and 30 modules, you’ll learn to collect, clean, analyze, visualize, and model data — the skills that every industry now demands. This isn’t abstract statistics theory. Every concept is taught through real datasets and practical projects.

What You’ll Build

• Exploratory Data Analysis Report — A comprehensive analysis of a real-world dataset with statistical summaries, correlation analysis, and publication-quality visualizations
• Interactive Data Dashboard — A web-based dashboard with dynamic filters, drill-down charts, and real-time data updates built with Plotly and Streamlit
• Predictive Model Pipeline — An end-to-end machine learning pipeline with data preprocessing, feature engineering, model training, evaluation, and deployment as a REST API
• Natural Language Analysis Tool — A text analysis application that performs sentiment analysis, topic modeling, and keyword extraction on large document collections

Curriculum Overview — 30 Modules

Python Mastery (Weeks 1–4): Python fundamentals (variables, control flow, functions, OOP), data structures (lists, dicts, sets, tuples), file I/O and exception handling, list comprehensions and generators, virtual environments and package management, Jupyter Notebook workflows

Data Wrangling (Weeks 5–8): NumPy for numerical computing, Pandas for data manipulation, data cleaning and missing value strategies, merging, joining, and reshaping datasets, regular expressions for text processing, working with APIs and web scraping

Visualization (Weeks 9–11): Matplotlib fundamentals and customization, Seaborn for statistical visualization, Plotly for interactive charts, dashboard creation with Streamlit, storytelling with data — choosing the right chart, design principles for data communication

Machine Learning (Weeks 12–15): Supervised learning (linear/logistic regression, decision trees, random forests, SVMs), unsupervised learning (K-means, hierarchical clustering, PCA), model evaluation (cross-validation, confusion matrices, ROC curves), feature engineering and selection, scikit-learn pipelines, introduction to neural networks with TensorFlow/Keras

Production (Week 16): Model deployment with Flask/FastAPI, data pipeline automation, version control for data projects (DVC), ethical AI considerations and bias detection

What’s Included

• 30 structured modules with real-world datasets
• 140+ interactive coding challenges in Jupyter environments
• 4 portfolio-ready data science projects
• AI-powered code and analysis reviews
• Private data science community
• Certificate of completion
• Lifetime access to all materials and future updates

Who This Is For

Aspiring data analysts and data scientists, business professionals wanting to make data-driven decisions, developers expanding into ML/AI, researchers looking for computational skills, or students preparing for data science careers. No prior programming experience required.