No prior coding or data experience is required.Provides a conceptual overview of data science workflows and lifecycle.Introduces tools like ...
Includes exercises to reflect on data-driven problems.Students can sketch simple data questions using sample datasets.Hands-on exposure is ...
No deep ML or AI algorithms are covered.Introduces basic modeling concepts conceptually.Focus on data problem formulation, cleaning, and ...
Estimated total time: 7–8 hours.Self-paced with lifetime access.Modules range from 1.5 to 2.5 hours each.Flexible scheduling allows ...
Introduces key roles: data scientist, data analyst, ML engineer.Covers tools and workflows used in real-world projects.Highlights ethical ...
Prior experience helps but is not strictly required.Designed as a culmination of beginner-level data analytics learning.Focuses on applying ...
You can select a dataset from the course or your own data.Provides guidance on preparing and processing your data.Supports portfolio-ready ...
Prepares for roles like Entry-level Data Analyst or Junior Data Analyst.Includes portfolio-building and AI-assisted analysis tasks.Hands-on ...
Labs are interactive but optional, focusing on practical skills.No advanced SQL, R, or Python required.AI tools assist in data analysis ...
Entirely self-paced with lifetime access.Modules range from 1–3 hours each for flexibility.Optional exercises allow focus on ...
