Class 9 Data Literacy – Artificial IntelligenceNotes, Questions from CBSE Curriculum, MCQs & Q&A

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Data literacy means knowing how to understand, work with, analyze, and communicate with data in ways that make sense.

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1. Data, 2. Information, 3. Knowledge, 4. Wisdom.

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Continuous data can take any decimal value along a scale (e.g., body temperature: 37.2°C). Discrete data consists strictly of indivisible whole integers (e.g., 42 students in a class).

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Data augmentation is the process of increasing dataset size by creating altered copies of existing data (e.g., modifying brightness, rotation, or color).

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Data Privacy deals with ethical rights, individual consent, and rules governing what sensitive data is collected and how it is shared. Data Security refers to protective technical safeguards preventing digital data from unauthorized access, cyber theft, or corruption.

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1. Structure: Data stored systematically in organized rows/columns rather than unstructured prose. 2. Cleanliness: Datasets free from duplicates, missing attributes, and anomalous outliers. 3. Accuracy: Data values that closely reflect ground-truth real-world measurements.

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Independent features are input variables provided to the model to make predictions (e.g., age, income, education). Dependent features are the resulting output labels that the AI attempts to forecast (e.g., purchase decision 1 or 0).

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1. Use strong, unique passwords with mixed characters. 2. Activate Two-Factor Authentication (2FA). 3. Prioritize secure 'https://' websites for logins. 4. Keep operating systems, browsers, and antivirus software updated regularly.

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1. Plan: Define program goals, understand participants, establish execution strategy and timeline. 2. Communicate: Design a transparent communication plan explaining purpose and securing stakeholder commitment. 3. Assess: Deploy data literacy assessment tools to determine baseline comfort levels. 4. Develop Culture: Imbibe data literacy skills permanently into the community's culture over time. 5. Prescriptive Learning: Provide customized educational resources aligned with individual learning styles. 6. Evaluate: Establish evaluation metrics to assess learning progress at regular intervals.

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• Textual DI: Presents data in descriptive narrative paragraphs. Suitable for very small datasets, but becomes unreadable and overwhelming for large figures. • Tabular DI: Systematically organizes data in rows and columns with explicit titles and column headings. Allows precise reading of exact numbers, but difficult to discern trends quickly. • Graphical DI: Represents data visually through Bar Graphs, Pie Charts, and Line Graphs. Enables instant comprehension of trends, distributions, and outliers at a glance.

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Scenario: Niki has 7 candies and Ruchi has 4 candies. • Data Processing: Answering 'How many candies do they have in total?' by computing 7 + 4 = 11 candies. It performs a basic mathematical operation on raw numbers. • Data Interpretation: Answering 'Who should get more candies so both have an equal count, and how many?' by reasoning that Ruchi needs 3 more candies (or sharing to reach 5.5 each). It analyzes processed figures contextually to reach a fair decision.

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1. Prepare a spreadsheet with columns: Song Name, Album, Artist, Genre, Year, Song Length. 2. In Tableau Public, click 'Microsoft Excel' to connect and drag the sheet into the canvas. 3. In Sheet 1, drag 'Genre' to Columns and 'Sample (Count)' to Rows to construct a Bar Chart. 4. Drag 'Genre' to the 'Color' mark to color-code each music genre. 5. Duplicate Sheet 1, open the top-right 'Show Me' menu, and select 'Packed Bubbles'. 6. Click the 'Label' card to adjust mark labels to 12pt Chalkboard font for intuitive visual presentation.