Data Science Fundamentals with Python and SQL Specialization Course Syllabus

A comprehensive introduction to Python, SQL, and data science fundamentals, perfect for beginners looking to enter the data-driven job market. This specialization spans approximately 6-10 weeks with a total time commitment of 150-200 hours, combining theory, hands-on labs, and a real-world capstone project to build a professional data science portfolio.

Module 1: Introduction to Data Science & Python Basics

Estimated time: 40 hours

• Introduction to data science and its applications
• Python programming fundamentals for data science
• Data types, variables, and control structures in Python
• Using NumPy and Pandas for data manipulation
• Data wrangling and preprocessing techniques

Module 2: SQL for Data Science

Estimated time: 60 hours

• Writing basic and advanced SQL queries
• Understanding joins, filters, and aggregations
• Managing and querying structured datasets
• Working on real-world SQL-based data projects

Module 3: Data Visualization & Exploratory Analysis

Estimated time: 80 hours

• Creating visualizations with Matplotlib and Seaborn
• Performing exploratory data analysis (EDA)
• Identifying trends, patterns, and outliers in data
• Developing interactive dashboards and reports

Module 4: Machine Learning Foundations

Estimated time: 100 hours

• Introduction to basic machine learning concepts
• Applying regression, classification, and clustering algorithms
• Implementing ML models using Python libraries
• Working on practical machine learning case studies

Module 5: Capstone Project: Real-World Data Science Challenge

Estimated time: 120 hours

• Designing and executing a full-scale data science project
• Integrating Python and SQL for data analysis
• Presentation of findings using data storytelling techniques

Module 6: Final Project

Estimated time: 20 hours

• Final project submission
• Peer review and feedback
• Certificate of completion

Prerequisites

• No prior coding experience required
• Basic computer literacy
• Access to a modern web browser and internet connection

What You'll Be Able to Do After

• Write Python code for data analysis using Pandas and NumPy
• Extract and process data using SQL queries
• Visualize data and communicate insights using Matplotlib and Seaborn
• Apply machine learning techniques to real-world datasets
• Complete a portfolio-ready capstone project demonstrating end-to-end data science skills