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Data Science for Beginners

A self-paced Robotnix edition of Microsoft's complete 20-lesson Data Science for Beginners curriculum. Each Unit preserves two source-faithful Microsoft lessons and adds a Robotnix-original evidence check that asks students to defend a question, model, chart, analysis, or deployment decision. Units 4-8 include a bounded browser data workbench with fictional data, interactive charting, and editable Pandas analysis. Five standalone Robotnix Data Labs add telemetry, sampling and bias, regression, data drift, and a Data-to-AI bridge. The core path does not require a paid cloud account. Browser runtime and Chromebook-width behavior remain release gates until owner validation.

Structure
10 units
Audience
Grades 9-12
Entries
40
Pacing
Self-paced
Interactives
5 labs
Provenance
Sources and credits

Course licensing

Microsoft source and license

This course preserves the complete source-faithful Microsoft lesson sequence inside CourseOS. The source material is from Microsoft Data Science for Beginners by Microsoft and the upstream contributors, pinned at commit 4d2ac427ad6f022e73a75c4f46a28bbb7978ec3f, and provided under the MIT License.

View complete sources, credits, modifications, and reuse limits.

Unit 01

  1. overviewUnit 1: Defining Data Science and Data EthicsData science starts before Python, a chart, or a model. It starts with a question, the people affected by the answer, and the limits of the evidence.
  2. Lesson 1Defining Data ScienceHowever, data became much more critical with the creation of computers. The primary role of computers is to perform computations, but they need data to operate on. Thus, we need to understand how computers store and…
  3. Lesson 2Introduction to Data EthicsWe are all data citizens living in a datafied world.
  4. evidence checkEvidence Check: Frame the Question Before Touching the DataChoose a real-world question that could be investigated with data. Do not collect personal data for this activity.

Unit 02

  1. overviewUnit 2: Data, Statistics, and ProbabilityA number can be precise and still be misleading. Before trusting a result, you need to know what was measured, who or what was sampled, and what kind of claim the evidence can support.
  2. Lesson 1Defining DataData is facts, information, observations and measurements that are used to make discoveries and to support informed decisions. A data point is a single unit of data with in a dataset, which is collection of data…
  3. Lesson 2A Brief Introduction to Statistics and ProbabilityStatistics and Probability Theory are two highly related areas of Mathematics that are highly relevant to Data Science. It is possible to operate with data without deep knowledge of mathematics, but it is still better…
  4. evidence checkEvidence Check: Audit a Statistical ClaimFind or invent a simple claim such as “students who sleep more score higher.” Use a fictional or public example; do not use private student records.

Unit 03

  1. overviewUnit 3: Relational and Non-Relational DataData storage is an architecture decision. The useful question is not “Which database is better?” It is “What relationships, queries, and changes does this system need to survive?”
  2. Lesson 1Working with Data: Relational DatabasesChances are you have used a spreadsheet in the past to store information. You had a set of rows and columns, where the rows contained the information (or data), and the columns described the information (sometimes…
  3. Lesson 2Working with Data: Non-Relational DataData is not limited to relational databases. This lesson focuses on non-relational data and will cover the basics of spreadsheets and NoSQL.
  4. evidence checkEvidence Check: Choose the Data ModelA school robotics team needs to store members, events, robot test runs, sensor notes, and photos.

Unit 04

  1. overviewUnit 4: Python, Pandas, and Data PreparationReal datasets arrive messy. Missing values, duplicate records, inconsistent labels, wrong types, and suspicious outliers are not side issues—they can change the conclusion.
  2. Lesson 1Working with Data: Python and the Pandas LibraryWhile databases offer very efficient ways to store data and query them using query languages, the most flexible way of data processing is writing your own program to manipulate data. In many cases, doing a database…
  3. Lesson 2Working with Data: Data PreparationDepending on its source, raw data may contain some inconsistencies that will cause challenges in analysis and modeling. In other words, this data can be categorized as “dirty” and will need to be cleaned up. This…
  4. evidence checkEvidence Check: Build a Data-Cleaning PlanImagine a dataset with columns for student_id, team, event_date, score, and notes. Some rows have missing scores, inconsistent dates, duplicate IDs, and team names written several different ways.

Unit 05

  1. overviewUnit 5: Visualizing Quantities and DistributionsA chart is not decoration after the analysis. It decides which differences, outliers, clusters, and absences become visible to the reader.
  2. Lesson 1Visualizing QuantitiesIn this lesson you will explore how to use one of the many available Python libraries to learn how to create interesting visualizations all around the concept of quantity. Using a cleaned dataset about the birds of…
  3. Lesson 2Visualizing DistributionsIn the previous lesson, you learned some interesting facts about a dataset about the birds of Minnesota. You found some erroneous data by visualizing outliers and looked at the differences between bird categories by…
  4. evidence checkEvidence Check: Defend the Chart ChoiceYou have a dataset containing the completion times of 200 robot test runs plus the robot model used for each run.

Unit 06

  1. overviewUnit 6: Visualizing Proportions and RelationshipsPatterns are useful. Explanations are harder. This Unit is about seeing proportions and relationships without turning a visible correlation into a story the evidence cannot support.
  2. Lesson 1Visualizing ProportionsIn this lesson, you will use a different nature-focused dataset to visualize proportions, such as how many different types of fungi populate a given dataset about mushrooms. Let's explore these fascinating fungi using…
  3. Lesson 2Visualizing Relationships: All About Honey 🍯Continuing with the nature focus of our research, let's discover interesting visualizations to show the relationships between various types of honey, according to a dataset derived from the United States Department of…
  4. evidence checkEvidence Check: Separate Pattern From ExplanationUse the honey example from the Microsoft lesson or another non-sensitive public dataset.

Unit 07

  1. overviewUnit 7: Meaningful Visualization and the Data Science LifecycleA data-science workflow is not a conveyor belt. Weak evidence should send you backward—to the question, the data, the preparation, or the analysis.
  2. Lesson 1Making Meaningful VisualizationsOne of the basic skills of a data scientist is the ability to create a meaningful data visualization that helps answer questions you might have. Prior to visualizing your data, you need to ensure that it has been…
  3. Lesson 2Introduction to the Data Science LifecycleAt this point you've probably come to the realization that data science is a process. This process can be broken down into 5 stages:
  4. evidence checkEvidence Check: Trace the LifecycleChoose a small data-science scenario such as school energy use, esports match statistics, public transportation delays, or robot test reliability.

Unit 08

  1. overviewUnit 8: Analysis and CommunicationAnalysis produces a result. Communication determines whether the audience can see what that result actually means—including the uncertainty and limitations.
  2. Lesson 1The Data Science Lifecycle: AnalyzingAnalyzing in the data lifecycle confirms that the data can answer the questions that are proposed or solving a particular problem. This step can also focus on confirming a model is correctly addressing these questions…
  3. Lesson 2The Data Science Lifecycle: CommunicationTest your knowledge of what's to come with the Pre-Lecture Quiz above!
  4. evidence checkEvidence Check: Write the Evidence MemoAssume an analysis found that one group in a fictional dataset has a higher average outcome than another.

Unit 09

  1. overviewUnit 9: Cloud Data Science and Low-Code ToolsCloud changes where computation, data, and tooling live. It also changes cost, privacy, control, reproducibility, and who can access the workflow.
  2. Lesson 1Introduction to Data Science in the CloudIn this lesson, you will learn the fundamental principles of the Cloud, then you will see why it can be interesting for you to use Cloud services to run your data science projects and we'll look at some examples of…
  3. Lesson 2Data Science in the Cloud: The "Low code/No code" wayTable of contents:
  4. evidence checkEvidence Check: Make the Deployment DecisionA class needs to analyze a medium-sized public dataset. Students have Chromebooks, the project contains no confidential records, and the school wants the workflow to remain reproducible next semester.

Unit 10

  1. overviewUnit 10: SDK Workflows and Data Science in the Real WorldThe final Unit connects tooling to consequences. A real data-science system is a chain of decisions about the question, source data, preparation, analysis, communication, deployment, and what happens when the evidence…
  2. Lesson 1Data Science in the Cloud: The "Azure ML SDK" wayTable of contents:
  3. Lesson 2Data Science in the Real WorldWe're almost at the end of this learning journey!
  4. evidence checkEvidence Check: Data Science Evidence DossierFinish the course by defending one small data-science investigation. You may use a public dataset from an earlier lesson or a teacher-supplied synthetic dataset.