Understanding Data Chapter Notes | Informatics Practices for Class 11 - Humanities/Arts PDF Download

Introduction to Data

• Data is a collection of characters, numbers, and symbols representing values of situations or variables, crucial for decision-making across various fields.
• Decisions, such as choosing a college, rely on analyzing data like placement records, faculty qualifications, facilities, and fees.
• Governments collect population data through censuses to plan and formulate policies, highlighting the role of systematic data collection.
• In sports, coaching staff analyze opponent teams' past performances to devise strategies, showing data’s role in competitive planning.
• Banks maintain customer data, including account details and transactions, to manage financial operations effectively.
• Raw data alone is insufficient for decisions; it requires processing and analysis, as seen in college placement data summarized in brochures for clarity.
• Data is plural, with the singular form being "datum."
• Computers store data electronically, enabling faster and easier processing compared to manual methods.
• The ICT revolution, driven by computers, mobiles, and the internet, has led to the generation of large volumes of data at a rapid pace.
• Examples of common data include personal details (name, age, gender), transaction records, images, videos, documents, web pages, social media posts, sensor signals, and satellite data.

Importance of Data

• Data is essential for human decision-making, providing insights into possibilities or hidden patterns not visible without processing.
• Bank transactions, such as ATM withdrawals, rely on data to update account balances accurately.
• Meteorological offices monitor satellite data to predict weather events like cyclones or heavy rainfall.
• In business, companies analyze market behavior and customer feedback to adjust products or services in a competitive environment.
• Dynamic pricing in airlines, railways, and cab booking apps uses data on demand and supply to adjust prices in real-time.
• Restaurants use sales data to determine "happy hour" discounts, optimizing pricing based on time-specific sales patterns.
• Electronic voting machines record voting data, enabling quick result compilation compared to manual ballot counting.
• Scientists collect experimental data to compare results, while pharmaceutical companies track data to assess medicine effectiveness.
• Libraries maintain data on books and memberships to manage operations efficiently.
• Search engines analyze vast web data to deliver relevant search results.
• Weather alerts are generated by analyzing satellite data, aiding in disaster preparedness.

Types of Data

• Data varies by source and format, such as images (pixels), videos (frames), fee slips (numeric and non-numeric entries), or messages (text, emoticons, images).
• Data is classified into two broad categories based on format: structured and unstructured.

Structured Data

• Structured data is organized and recorded in a well-defined format, typically stored in tabular form (rows and columns) in computers.
• Each column represents an attribute, characteristic, or variable, while each row represents an observation’s data for those attributes.
• Example: A shop’s kitchen inventory table includes columns like ModelNo, ProductName, Unit Price, Discount, and Items_in_Inventory.
• Using software like spreadsheets, shop owners can calculate total items by summing the Items_in_Inventory column or compute total inventory value by multiplying Unit Price by Items_in_Inventory and summing the results.
• Other examples include bookshop data (BookTitle, Author, Price, YearofPublication), school fee deposits (StudentName, Class, RollNo, FeesAmount, DepositDate), and ATM withdrawals (AccHolderName, AccountNo, TypeofAcc, DateofWithdrawal, AmountWithdrawn, ATMid, TimeOfWithdrawal).

Unstructured Data

• Unstructured data lacks a traditional row-and-column structure and does not follow a fixed pattern or format.
• Example: Newspapers have no fixed layout, with varying numbers of images, news items, and advertisements daily.
• Emails lack a fixed structure regarding lines, paragraphs, or attachments.
• Other examples include web pages (text and multimedia), text documents, business reports, books, audio/video files, and social media messages.
• Unstructured data is often described using metadata (data about data), such as email components (subject, recipient, body, attachment) or image file details (size, type, resolution).
• This book focuses on handling structured data, though methods exist to process unstructured data.

Data Collection

• Data collection involves identifying or gathering data from appropriate sources for processing and storage in files or databases.
• Example scenarios for grocery store sales data include:
  • Data recorded in a diary/register, requiring entry into a digital format like a spreadsheet.
  • Data already in digital format, such as a CSV file.
  • No recorded data, but the shopkeeper wants software (e.g., using Python) to store and retrieve data in CSV or a DBMS like MySQL.
• Digital interactions generate vast data volumes, such as hospital patient data for service improvement or shopping mall purchase data for sales strategies.
• Analyzing purchase patterns (e.g., bedsheets and groceries bought together) can lead to strategic product placement to boost sales.
• Political analysts use social media data to gauge public opinion before elections.
• Organizations like the World Bank and IMF collect economic data from countries for forecasting.

Data Storage

• Data storage involves saving data on devices for future retrieval, a challenging task due to the high volume and rate of data generation.
• Decreasing costs of digital storage devices, such as HDD, SSD, CD/DVD, tape drives, pen drives, and memory cards, have simplified storage.
• Data like images, documents, and audio/videos are stored as files on computers, while school/hospital data are stored in data files.
• Computers allow adding, modifying, or deleting data in files and processing them to obtain results.
• File processing has limitations, which can be addressed using Database Management Systems (DBMS).

Data Processing

• Data processing transforms raw data into meaningful information for decision-making, as large datasets alone cannot yield conclusions.
• Automated data processing is used in online bill payments, complaint registrations, and ticket bookings.
• The data processing cycle includes input, storage, processing, and output generation.
• Examples of data processing tasks:
  • Online examination form: Inputs include student details, qualifications, marks, and payment details; processing verifies data correctness, eligibility, and fees, generating a roll number and admit card.
  • ATM withdrawal: Inputs include PIN, account details, and withdrawal amount; processing checks PIN validity and balance, deducting the amount and issuing currency and a receipt.
  • Train ticket issuance: Inputs include journey details, passenger info, and payment; processing checks availability and confirms booking, issuing a ticket.

Statistical Techniques for Data Processing

• Statistical techniques summarize tabular data for easier comprehension, revealing data characteristics.
• Common techniques include measures of central tendency (mean, median, mode) and measures of variability (range, standard deviation).

Measures of Central Tendency

• These provide a single value summarizing the data, with the choice of measure depending on data characteristics.

Mean

• Mean is the average of numeric values for an attribute, calculated as the sum of values divided by the number of values.
• Formula: For n values .
• Example: Heights (in cm) [90, 102, 110, 115, 85, 90, 100, 110, 110] yield a mean of \( \frac{912}{9} = 101.33 \) cm.
• Mean is sensitive to outliers (exceptionally large or small values), which may need removal to avoid skewing results.
• Outliers are often considered errors as they affect statistical calculations like the mean.

Median

• Median is the middle value when data is sorted in ascending or descending order, dividing the dataset into two equal parts.
• For an odd number of values, the median is the middle value; for an even number, it’s the average of the two middle values.
• Example: Sorted heights [85, 90, 90, 100, 102, 110, 110, 110, 115] (9 values) give a median of 102 cm (5th value).
• Median is less affected by outliers, making it suitable for skewed datasets.

Mode

• Mode is the value with the highest frequency in the dataset, applicable to both numeric and non-numeric data.
• A dataset may have no mode (if all values occur once) or multiple modes (if multiple values share the highest frequency).
• Example: In the height dataset, the mode is 110 cm, appearing three times.

Measures of Variability

• These measures, also called measures of dispersion, indicate the spread or variation of data around the mean.
• Two datasets may have identical mean, median, or mode but differ in dispersion, or vice versa.
• Common measures are range and standard deviation.

Range

• Range is the difference between the maximum and minimum values in a dataset, applicable only to numerical data.
• Formula: Range = Maximum - Minimum.
• Example: For heights (min = 85 cm, max = 115 cm), range = (115 - 85 = 30) cm.
• Range is sensitive to outliers, as it relies on extreme values, potentially misrepresenting data spread.

Standard Deviation

• Standard deviation measures data spread by calculating the positive square root of the average of squared differences from the mean.
• Unlike range, it considers all data points, providing a more comprehensive measure of variability.
• Formula: For n values (x₁, x₂, ...x_n) with mean , standard deviation .
• Example: For heights with mean 101.33 cm, squared differences sum to 938, so standard deviation =.
• Smaller standard deviation indicates less spread, while a larger value indicates greater spread.

Summary

• Data are unorganized facts that, when processed, generate meaningful information.
• Data can be structured (organized in rows and columns) or unstructured (lacking a fixed format).
• Common storage devices include Hard Disk, SSD, CD/DVD, Pen Drive, and Memory Card.
• The data processing cycle involves input, storage, processing, and output generation.
• Statistical techniques like summarization reveal data characteristics for easier understanding.
• Mean is the average of values, median is the middle value in sorted data, and mode is the most frequent value.
• Range is the difference between maximum and minimum values, while standard deviation measures spread using all data points.