Class 9 Computer Science Unit 4, Data and Analysis Federal Board FBISE, Big Data Class 9

Data and Analysis

Data Analytics: The process of examining raw data to draw conclusions. It transforms data into actionable knowledge to solve problems and inform decision-making.
Techniques: Includes mathematical calculations, statistical techniques, and charts to find patterns or trends.
Data Types:
- Quantitative: Numerical data.
- Qualitative: Descriptive data.

Introduction to Data Science

Definition: An interdisciplinary field using mathematics, statistics, data analysis, and machine learning to extract insights.
The Process: It functions like a pipeline, moving from raw data to actionable insights.

Key Concepts of Data Science

Data: Observations or facts collected in various forms:
- Structured: Processed data (e.g., tables).
- Unstructured: Unprocessed data (e.g., audio, video, tweets, PDFs).
Dataset: A structured collection of related data (e.g., a collection of brain CT scans).
Statistics & Probability: Statistics analyzes the frequency of past events; probability predicts the likelihood of future events.
Mathematics: Used to solve problems, optimize models, and interpret complex data.
Machine Learning (ML): A branch of AI using algorithms to help computers imitate human learning.
Deep Learning: A subset of ML that uses artificial neural networks to simulate human brain behavior.
Data Mining: A subset of data science focused specifically on discovering patterns in existing datasets.
Data Visualization: Graphical representations (charts, infographics) used to communicate complex insights clearly.
Big Data: Large volumes of data that provide better opportunities for machine learning and accurate results.
Predictive Analysis: Using historical data to predict future trends.
Natural Language Processing (NLP): The ability of computers to understand and generate human language (e.g., chatbots, translation).

Scope and Application

Data science solves business problems by closing the gap between the current state and a desired objective. Key applications include:

Logistics: Deciding shipping routes and delivery times to reduce costs.
Sales/Marketing: Choosing products to buy, creating promotional offers, and forecasting revenue.
Predictive: Foreseeing delays in transport or predicting election outcomes.
Health: Analyzing benefits of physical training programs.
Sentiment Analysis: Identifying if customer reviews are positive, negative, or neutral.

Industry-Specific Usage

Industry: Analyzes historical data for quality control and trend prediction.
Consumer Goods: Optimizes inventory based on demand forecasting for specific demographics.
Logistics: Real-time tracking, load balancing, and route optimization.
Stock Markets: Used for algorithmic trading and market surveillance.
E-commerce: Recommendation systems, fraud detection, and shopping cart analysis.

Data Types in Data Science

Data in data science is classified into two primary categories: Qualitative (Categorical) and Quantitative (Numeric).

Qualitative or Categorical Data

Describes objects using labels or categories; it cannot be expressed in numerical form.

Nominal Data: Mutually exclusive categories with no inherent order (e.g., gender, city, color, transportation types like car or bus).
Ordinal Data: Follows a specific order or ranking system (e.g., military rank, economic status, test grades).

Quantitative or Numerical Data

Deals with numeric values that can be computed mathematically.

Discrete Data: Consists of counted values that cannot be divided into smaller units (e.g., number of students, computers, or tickets sold).
Continuous Data: Represents measurements that can take any value within a range (e.g., weight, temperature, wind speed). It is further divided into:
- Interval Scaled: Equal differences between measurements but no true zero point (e.g., Celsius or Fahrenheit).
- Ratio Scaled: Meaningful differences with a true zero point, where zero represents the absence of the property (e.g., weight in kg).

Data Collection and Sources

Sources of Data

Primary Data: Collected directly from original sources via surveys, interviews, experiments, sensors (e.g., seismic data), or social media (e.g., tweets).
Secondary Data: Collected from existing records like published research, books, websites, or government records.

Key Terms in Collection

Investigator: The person conducting the statistical enquiry.
Enumerator: Individuals who assist the investigator in collecting information from people.
Respondent: The person providing the required information.

Datasets and Databases

Definitions

Dataset: A structured or organized collection of data related to a specific body of work.
Database: An organized collection of data stored in multiple datasets/tables, accessed electronically.
DBMS (Database Management System): The interface between the database and the end-user for creating, modifying, and retrieving data.

Database Types

Relational: Stores data in tables (rows and columns). Examples: MySQL, Oracle, MS-Access.
Non-Relational (NoSQL): Stores data as key-value pairs, column families, graphs, or documents. Examples: MongoDB, Cassandra.

Role in Data Science

Databases are essential due to rapid data generation and the dependence of science on data. They allow for:

Inventory Management: Placing products with short shelf lives in accessible areas.
Predictive Analysis: Identifying high-demand seasons (e.g., festivals) to optimize stock and identify customer traffic trends.

Data Storage and Analysis

Data Storage Methods

Relational/NoSQL databases
Data warehouse
Distributed file systems
Cloud-based data storage
Blockchain

Data Visualization

The graphical representation of data to find insights, trends, and patterns using charts, graphs, maps, and dashboards.

Summary Statistics

Provides a quick overview of a sample’s characteristics, including total count, minimum/maximum values, mean, and standard deviation.

Importance: Helps in understanding trends, distribution, and identifying outliers (unusual data points).
Utility: Essential for data cleaning, preprocessing, and feature selection.

What is Big Data?

Big data refers to larger, more complex datasets from new sources that are so massive traditional software cannot manage them. It is characterized by the "Three Vs":

Volume: The sheer amount of data, ranging from terabytes to hundreds of petabytes.
Velocity: The high speed at which data is received and processed, often in real-time.
Variety: The different formats available, including structured data (databases) and unstructured data (text, images, videos).

History and Evolution

Early 2000s: The term "Big Data" emerged to describe exponential data growth.
2005: Growth spiked due to services like Facebook and YouTube. Hadoop was developed to store and analyze these massive datasets.
Current Drivers: The Internet of Things (IoT) and Machine Learning continue to generate vast amounts of data for business insights.

Advantages and Benefits

Product Development: Anticipating customer demand and building predictive models for new products.
Predictive Maintenance: Analyzing data to predict equipment failure before it happens.
Customer Experience: Using social media and web logs to improve satisfaction.
Fraud and Compliance: Identifying suspicious patterns to enhance cybersecurity.

Big Data Challenges

Data Quality: Poor data leads to errors and misleading insights.
Security and Privacy: Difficulty in protecting massive datasets from unauthorized access.
Rapid Growth: Systems struggle to keep up with the constant increase in data volume.
Tool Selection: Finding compatible tools that interact seamlessly.
Data Integration: Difficulty in harmonizing diverse data formats and structures.

Business Applications

Healthcare: Using wearable devices to monitor patients and predicting epidemic outbreaks.
Media and Entertainment: Analyzing viewer patterns to target ads and create content.
Internet of Things (IoT): Enhancing device capabilities through personalized data analytics.
Manufacturing: Improving product quality, tracking faults, and planning supply chains.
Government: Reducing costs, combating fraud, and improving citizen services.

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