Basics of Data Analysis (basic-level)

This is a basic-level self-standing learning module on Basics on Data Analysis.

Data analysis is the process of transforming raw information into meaningful insights through systematic exploration, organization, and interpretation. This beginner-friendly module (5–10 hours) provides an introduction to the main steps of data analysis: data collection, cleaning, descriptive statistics, visualization, simple models and simple interpretation. Learners will be introduced to essential concepts such as variables, distributions, correlations, and patterns, and will discover how data can be summarized both numerically and graphically.

The module emphasizes a hands-on and intuitive approach, designed for participants with no prior background in programming or statistics. Through real-world examples from domains such as healthcare, business, and social sciences, students will see how data analysis supports evidence-based decision-making and problem solving.

By the end of the course, students will have acquired the fundamental knowledge to:

• Understand the role of data analysis in different contexts.
• Recognize the key steps in a typical data analysis pipeline.
• Apply basic descriptive and visualization methods to small datasets.
• Appreciate the value of data-driven reasoning for everyday and professional challenges.

This course serves as a first step into the world of data analysis and prepares learners for more advanced modules, where more complex statistical and machine learning techniques will be introduced.

Target audience

Non-professionals, general audience with no prior knowledge assumed in Data Analysis, Data Science, Artificial Intelligence, or Programming.

Intended Learning Outcomes

• Understand the fundamental role of data analysis across disciplines.
• Identify the main steps in a data analysis workflow (collection, cleaning, description, visualization).
• Apply descriptive statistics to summarize datasets.
• Recognize basic data visualization techniques (charts, histograms, scatter plots) and their use in interpreting results.
• Understand the importance of data quality and common issues (missing values, outliers).

Lessons

• Introduction to Data Analysis
• Types of Data
• Data Collection and Cleaning
• Descriptive Statistics
• Data Visualization Basics
• Applications of Data Analysis