Understanding Data and Problems
Before learning advanced tools, models, or programming, a data scientist must first understand what data represents, how problems are defined, what evidence can support, and where responsible analysis begins.
What you should understand by the end
Level 1 develops the reasoning foundation required for every later stage of the Data Science Academy.
Recognize what data represents
Understand that data is a recorded representation of real people, events, measurements, systems, or processes.
Define analytical problems
Convert broad concerns into clear questions that data may be able to investigate.
Evaluate evidence carefully
Distinguish observations, assumptions, interpretations, and conclusions.
Think responsibly
Consider privacy, fairness, context, limitations, and possible harm before analysis begins.
Level 1 modules
These six modules build the conceptual foundation for statistics, SQL, Python, visualization, and machine learning.
What Is Data?
Explore how observations become recorded information and why data is never the same thing as the full reality it represents.
- Observations
- Measurements
- Records
- Variables
- Representation
Types and Structures of Data
Learn how data differs by meaning, format, scale, source, and structure—and why those differences affect analysis.
- Qualitative data
- Quantitative data
- Structured data
- Unstructured data
- Measurement scales
The Data Science Process
Understand the complete workflow from identifying a problem to collecting data, analyzing evidence, communicating results, and evaluating impact.
- Problem definition
- Data collection
- Preparation
- Analysis
- Communication
Asking Good Analytical Questions
Learn how to turn vague concerns into specific, measurable, answerable, and useful questions.
- Broad problems
- Research questions
- Measurable outcomes
- Scope
- Decision context
Data Quality, Bias, and Ethics
Examine how missing information, poor measurement, biased samples, privacy concerns, and unfair assumptions can affect conclusions.
- Data quality
- Missing data
- Bias
- Privacy
- Fairness
Thinking Like a Data Scientist
Develop the habit of questioning assumptions, checking evidence, considering alternatives, recognizing uncertainty, and explaining limitations.
- Curiosity
- Skepticism
- Evidence
- Uncertainty
- Communication
How every module will work
Each module will follow the same five-part structure so that understanding leads to practical capability.
Learn
Understand the concept, purpose, assumptions, and common mistakes.
Practice
Strengthen understanding through focused questions and examples.
Build
Apply the module through a meaningful analytical task.
Reflect
Examine assumptions, limitations, uncertainty, and alternatives.
Apply
Connect the concept to healthcare, business, research, or society.
Practice, project, and reflection
Learners will not complete Level 1 by reading alone.
Question classification exercise
Review real-world questions and determine whether they are descriptive, comparative, predictive, causal, or not yet suitable for data analysis.
Turn a real problem into a data plan
Select a practical problem, define the decision that matters, identify the data needed, and document possible limitations, biases, and ethical concerns.
What can the data not tell us?
Explain the difference between what is observed, what is inferred, and what remains unknown.
Level 1 completion standard
A learner is ready to continue when they can clearly define a problem, explain what the available data represents, identify important limitations, and describe how an analysis could support a real decision without overstating what the evidence proves.
The curriculum is ready. The first lesson comes next.
Module 1 will begin by exploring a foundational question: What is data, and how does recorded information relate to reality?
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