Collect and analyze data effectively with 9th Class Computer Science Chapter 9 Data Science notes. Learn about Data Types, Data Collection Methods, and Visualization.
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Data Science is a field that involves gathering, analyzing, and interpreting data to solve problems and make informed decisions. It combines computer science, mathematics, and statistics. Workflow includes: Problem ID -> Data Collection -> Cleaning -> Analysis -> Interpretation -> Visualization.
Data consists of raw facts. Sources include Weather Data (sensors), Sales Data (transactions), Survey Responses (feedback), Website Data (analytics), and Social Media Data (posts/likes).
Qualitative Data describes qualities or characteristics (categorical, non-numeric). Types:
1. Nominal: No specific order (e.g., gender, colors, names).
2. Ordinal: Has a meaningful order but non-uniform differences (e.g., satisfaction ratings, education levels).
Quantitative Data measures quantity or amount (numerical). Types:
1. Discrete: Countable, distinct values (e.g., number of students).
2. Continuous: Measurable, can take any value in a range (e.g., height, temperature).
Organizing data reduces errors, saves time, and improves clarity. Methods include Tables (rows/columns) and Charts/Graphs (visual representation like Bar charts, Pie charts, Line graphs).
Methods to gather data include:
1. Surveys: Collecting written responses (e.g., Google Forms).
2. Questionnaires: Similar to surveys, often with multiple-choice questions.
3. Interviews: One-on-one conversations for detailed info.
4. Observations: Watching and recording behaviors.
5. Online Sources: Databases, articles, digital tools.
Using the internet to find information. Steps:
1. Online Databases: Google Scholar, IEEE Xplore.
2. Data Extraction: Finding relevant data using keywords.
3. Integration: Combining data from multiple sources.
4. Organization: Storing key stats/quotes systematically.
Structured Data: Organized, searchable (e.g., Spreadsheets, SQL databases).
Unstructured Data: Free-form, hard to search (e.g., emails, social media posts, videos, images).
Preparing data for analysis involves:
1. Evaluating Quality: Checking accuracy and completeness.
2. Identifying Errors: Fixing mistakes, outliers, and biases.
3. Data Cleaning: Removing duplicate or incorrect records.
Using math to understand data.
Measures of Center: Mean (Average), Median (Middle value), Mode (Most frequent).
Measures of Spread: Range (Max - Min), Variance (Spread from mean), Standard Deviation (Square root of variance).
Where data is saved:
1. Spreadsheets: Simple tables (Excel).
2. Databases: Structured storage (SQL).
3. Data Warehouses: For analyzing large datasets (Amazon Redshift).
4. NoSQL: For unstructured data (MongoDB).
Cloud Storage: Saving data online (Google Drive, OneDrive). Benefits: Remote access, backups.
Collaborative Tools: Multiple users editing docs simultaneously (Google Docs). Features: Version control, real-time editing.
Extremely large datasets. Characterized by:
1. Volume: Huge amount of data.
2. Velocity: Speed of data generation.
3. Variety: Different forms (text, video, etc.). Applications: Healthcare (predict outbreaks), Finance (fraud detection).
Turning data into visuals to identify trends. Tools: Excel, Tableau, Power BI.
Nominal Data: Bar/Pie charts.
Continuous Data: Line graphs, Scatter plots.
Future of data analysis includes:
1. Automated Cleaning: Using AI to fix errors.
2. AI Integration: Faster analysis.
3. Improved Privacy: Better encryption.
4. Advanced Visualization: Interactive dashboards.