The curriculum emphasizes the significance of data in today’s digital age. Over 2.5 quintillion bytes of new data are generated daily. Major corporations like Google, Facebook, Microsoft, and Amazon collectively manage vast amounts of information, each storing at least 1,200 petabytes of data. This highlights the critical nature of data in informing business strategies and consumer insights.
The Evolution of the Web
Web 1.0: The Web of Contents
Introduced in 1989 by Tim Berners-Lee, Web 1.0 served as a platform for information exchange but was primarily a "read-only" web, where user participation was minimal and the content was static.
Web 2.0: The Read-Write Web
Emerged with platforms like LiveJournal and Blogger around 1999, fostering user interactions and the rise of social media, allowing users to create content and engage with one another actively.
Web 3.0: The Semantic Web
Focuses on data decentralization, AI integration, and enhanced user connectivity through IoT. It emphasizes privacy due to blockchain technology, enabling users to control their data without the need for third-party interference.
Web 4.0: Represents wireless connectivity improvements, enhancing user interactions established by earlier web generations.
Web 5.0: Known as the "symbiotic web," facilitates emotional interactions between humans and computers, refining the user experience further.
Understanding Big Data
Big data is defined by three Vs:
Volume: Refers to large amounts of data generated continuously.
Velocity: The speed at which data is generated and processed.
Variety: The many formats and types of data, including text, audio, video, and images.
Additionally, two more Vs are important:
Value: The intrinsic worth of the data which can lead to insights, efficiencies, and innovation.
Veracity: The reliability and trustworthiness of the data.
Data vs. Information
Data consists of raw facts and figures with no context. Once analyzed and organized, data transforms into information, which is essential for informed decision-making. In essence:
Data: Unstructured, individual and meaningless on its own.
Information: Organized and contextualized, forming the basis for decision-making.
Data Analytics and Mining
Big data analytics involves five stages:
Collecting Data: Compiling from various sources for analysis.
Processing: Organizing the data for accurate query results.
Cleaning: Eliminating duplicates and irrelevant data to maintain quality.
Analyzing: Finding patterns and making forecasts based on insights derived from the cleaned data.
Data Mining: Identifies patterns and relationships within vast datasets and cluster data points to unveil anomalies.
Real-World Applications of Data
Various companies leverage data to enhance their services:
Uber: Dynamic Pricing
Uber utilizes customer data to analyze usage behaviors, allowing for surge pricing based on supply and demand.
Netflix: Predicting Demand
Utilizes data analytics for content creation and recommending shows based on viewing habits, stopping points, and ratings, positioning itself as a content creator.
Booking.com: Hyperpersonalization
Employs big data for personalizing recommendations, tailored advertising based on returning customer data, and refining user interface elements to improve customer experience.
Case Studies
Thomas Cook
Founded in 1841, it innovated with package tours. However, its inability to adapt to the digital landscape amidst increasing competition led to bankruptcy in 2019 after accumulating massive debts.
Yunnan Lucky Air
Established in 2004, it adopted e-commerce strategies and capitalized on the success of Web 2.0 technologies to become a prominent low-cost airline in China, ultimately increasing its value to over $400 million.
The curriculum emphasizes the significance of data in today’s digital age. Over 2.5 quintillion bytes of new data are generated daily. Major corporations like Google, Facebook, Microsoft, and Amazon collectively manage vast amounts of information, each storing at least 1,200 petabytes of data. This highlights the critical nature of data in informing business strategies and consumer insights.
The Evolution of the Web
Web 1.0: The Web of Contents
Introduced in 1989 by Tim Berners-Lee, Web 1.0 served as a platform for information exchange but was primarily a "read-only" web, where user participation was minimal and the content was static.
Web 2.0: The Read-Write Web
Emerged with platforms like LiveJournal and Blogger around 1999, fostering user interactions and the rise of social media, allowing users to create content and engage with one another actively.
Web 3.0: The Semantic Web
Focuses on data decentralization, AI integration, and enhanced user connectivity through IoT. It emphasizes privacy due to blockchain technology, enabling users to control their data without the need for third-party interference.
Web 4.0: Represents wireless connectivity improvements, enhancing user interactions established by earlier web generations.
Web 5.0: Known as the "symbiotic web," facilitates emotional interactions between humans and computers, refining the user experience further.
Understanding Big Data
Big data is defined by three Vs:
Volume: Refers to large amounts of data generated continuously.
Velocity: The speed at which data is generated and processed.
Variety: The many formats and types of data, including text, audio, video, and images.
Additionally, two more Vs are important:
Value: The intrinsic worth of the data which can lead to insights, efficiencies, and innovation.
Veracity: The reliability and trustworthiness of the data.
Data vs. Information
Data consists of raw facts and figures with no context. Once analyzed and organized, data transforms into information, which is essential for informed decision-making. In essence:
Data: Unstructured, individual and meaningless on its own.
Information: Organized and contextualized, forming the basis for decision-making.
Data Analytics and Mining
Big data analytics involves five stages:
Collecting Data: Compiling from various sources for analysis.
Processing: Organizing the data for accurate query results.
Cleaning: Eliminating duplicates and irrelevant data to maintain quality.
Analyzing: Finding patterns and making forecasts based on insights derived from the cleaned data.
Data Mining: Identifies patterns and relationships within vast datasets and cluster data points to unveil anomalies.
Real-World Applications of Data
Various companies leverage data to enhance their services:
Uber: Dynamic Pricing
Uber utilizes customer data to analyze usage behaviors, allowing for surge pricing based on supply and demand.
Netflix: Predicting Demand
Utilizes data analytics for content creation and recommending shows based on viewing habits, stopping points, and ratings, positioning itself as a content creator.
Booking.com: Hyperpersonalization
Employs big data for personalizing recommendations, tailored advertising based on returning customer data, and refining user interface elements to improve customer experience.
Case Studies
Thomas Cook
Founded in 1841, it innovated with package tours. However, its inability to adapt to the digital landscape amidst increasing competition led to bankruptcy in 2019 after accumulating massive debts.
Yunnan Lucky Air
Established in 2004, it adopted e-commerce strategies and capitalized on the success of Web 2.0 technologies to become a prominent low-cost airline in China, ultimately increasing its value to over $400 million.
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