Data Analysis and Visualization Foundations Specialization Course Syllabus

Overview: This specialization provides a comprehensive introduction to data analysis and visualization, designed for beginners. Over approximately 30 weeks, learners will build foundational skills using Excel, SQL, Python, and Tableau. The course combines theory with hands-on practice through real-world projects, preparing learners for entry-level data analyst roles. Estimated total time: 120-150 hours.

Module 1: Introduction to Data Analysis

Estimated time: 20 hours

• Understand fundamental data concepts and their role in business
• Learn the importance of data-driven decision-making
• Explore different types of data and their applications

Module 2: Data Cleaning and Preparation

Estimated time: 40 hours

• Work with Excel, SQL, and Python for data organization and transformation
• Identify missing values, inconsistencies, and data errors
• Apply best practices for structuring and cleaning datasets

Module 3: Data Analysis with SQL and Python

Estimated time: 60 hours

• Write SQL queries to extract and manipulate data
• Use Python libraries like Pandas and NumPy for analysis
• Perform statistical analysis and generate insights

Module 4: Data Visualization and Storytelling

Estimated time: 80 hours

• Learn best practices for creating impactful visualizations
• Work with Tableau, Matplotlib, and Seaborn for data storytelling
• Translate complex datasets into meaningful and compelling visual reports

Module 5: Final Capstone Project

Estimated time: 100 hours

• Apply all learned skills to a real-world data analysis project
• Clean, analyze, and visualize data to solve a business problem
• Present findings using professional dashboards and reports

Prerequisites

• Basic computer literacy
• Familiarity with spreadsheets (e.g., Excel)
• No prior programming experience required

What You'll Be Able to Do After

• Explain core data analysis concepts and their business applications
• Clean and prepare datasets using Excel, SQL, and Python
• Perform data analysis with SQL and Python to generate actionable insights
• Create effective visualizations using Tableau and Python libraries
• Present data findings through compelling dashboards and reports