Introduction to Data Science for Beginners, A Visual, Beginner-Friendly Guide to Data Science, Databases, Data Cleaning, Visualization, and Machine Learning.
What you’ll learn
-
Explain in plain language what data science is, and describe the stages of the data science lifecycle
-
Distinguish structured, semi-structured, and unstructured data, and identify common data types and measurement scales
-
Describe how relational databases use tables, keys, and normalization to organize data
-
Explain the difference between a star schema and a snowflake schema, and when each is used
-
Apply row-wise and column-wise cleaning steps, including duplicates, wrong data types, missing values, and outliers
-
Summarize a dataset with descriptive statistics and choose an appropriate chart to show it
-
Explain correlation, causation, and the basic logic of testing an idea with data
-
Describe, at a conceptual level, how simple machine learning models are trained and evaluated
Course Description
This course contains the use of artificial intelligence.
This course is a conceptual, visual introduction to data science for beginners and learners at an early intermediate level. It uses short lessons, each focused on one idea, with slides, diagrams that build step by step, and narration. It contains no coding and no mathematics beyond basic arithmetic. One running example, a fictional coffee company called Brewline Coffee, connects every lesson.
The course is organized in five parts. The first part covers the foundations: what data science is, the data science lifecycle, the types of data, data types and measurement scales, and where data comes from. The second part explains how data is stored and modeled, including databases, tables and keys, normalization, data warehouses and data lakes, the star schema, the snowflake schema, and the basics of SQL. The third part focuses on cleaning and preparing data, with separate lessons on row-wise cleaning, column-wise cleaning, missing values, outliers, and feature preparation. The fourth part shows how to make sense of data through exploratory analysis, descriptive statistics, visualization, correlation and causation, hypothesis testing ideas, and the basics of machine learning. The fifth part covers evaluating results, communicating them honestly, and bringing the whole pipeline together.
Each lesson runs about five minutes, so you can move through the material at your own pace, pause on any slide, and return to a topic whenever you need it. Key terms are defined the first time they appear, and each lesson closes with a short summary that restates the main idea before the next one begins. The lessons build on each other, so watching them in order is recommended.
The course contains 24 short lectures totaling about two hours of video. It is an introduction to concepts and terminology. It does not teach a specific programming language or software tool, and it makes no claims about career or income outcomes.
The instructor directed the course structure and content. Artificial intelligence tools were used to support the professional production of the course, including text-to-speech narration and the creation and preparation of visual and multimedia elements. The instructor reviewed and directed the use of these tools throughout the course.
Who this course is for:
- Beginners with no technical background who want a clear, visual, conceptual introduction to data science
- Professionals in any field who work with reports, spreadsheets, or dashboards and want to understand how data is stored, cleaned, and analyzed
- Learners who plan to study data analysis, databases, or machine learning later and want a solid conceptual foundation first
- This course is not intended for learners who want to practice writing code. It is conceptual and contains no programming exercises.
