big data

 Bilal hussain Malik                                                         Future Applications of Big Data                                                 Climate Science & Sustainability Big data enables environmental conservation. Satellites and IoT sensors track deforestation, wildlife, and pollution in real-time. Precision farming makes use of soil/weather data to save water/fertilizers, reducing wastage. Carbon footprint apps provide eco-friendly suggestions depending on the behavior of the user. Challenges include unavailability of data in developing nations and the risk of greenwashing. Future technologies can enable worldwide carbon trading networks and AI-driven reforestation efforts. Cross-border data sharing i...

 Bilal Hussain Malik                                                          Future Applications of Big Data                                        Hyper Personalized Consumer Experiences Retailers utilize big data for real-time personalization. AI suggests products based on purchase history, location, and even facial expressions (via emotion AI). Voice assistants auto-order supplies by learning user habits. Dynamic pricing varies prices based on demand, weather, or social trends. However, overpersonalization risks privacy intrusion—open data use is essential. Future shopping could be like a virtual concierge, but requires ethical data practices to maintain trust. Consumer profiling must balance convenience with consent and avoid manipulati...

 Bilal Hussain Malik                                                      Future Applications of Big Data                                      Smart Cities & Autonomous Infrastructure Big data fuels self-sustaining urban systems. AI adjusts traffic flow through real-time sensor inputs, reducing congestion by 20-30%. Predictive analytics makes energy grids balance supply/demand, lowering emissions. Public safety is boosted through crime-prediction software that dispatches police in advance. During disaster, AI processes satellite/social data to guide emergency services. Issues are cybersecurity threats (traffic/utility infrastructure hacking) and surveillance. Smart cities of the future can have autonomous public transport and pollution-free areas, but ...

 Bilal hussain Malik                                                         Data Mining Methods                                                       Dimensionality Reduction Dimensionality reduction techniques reduce datasets by performing dimensionality decrease without sacrificing meaningful information. t-SNE and PCA are common algorithms that project high-dimensional data onto low-dimensional spaces. This is essential for visualizing data with high complexity, improving computational efficiency, and avoiding the "curse of dimensionality." Some applications include image compression, gene expression data analysis, and feature selection for machine learning. The process is used to reveal latent structure a...

 Bilal Hussain Malik                                                          Data Mining Methods                                                   Time Series Analysis Time series analysis examines serial points of data that are collected over time in order to find patterns, trends, and seasonality. Projections of future values based on historical data require it. Exponential smoothing is used for trend-based projections, and ARIMA (AutoRegressive Integrated Moving Average) is for stationary data. Its applications involve stock market forecasting, weather forecasting, and inventory management. Missing data handling, noise removal, and anomaly detection are some of the main challenges. The method is particularly valu...

 Bilal Hussain Malik                                                            Data Mining Methods                                                     Neural Networks Neural networks are deep learning models that handle complex data by possessing artificial neurons stacked in layers that are interconnected. They automatically learn features from raw data and perform optimally for image/speech recognition and natural language comprehension. Architectures go from feedforward networks to complex CNNs and RNNs. Although extremely powerful for unstructured data, they need enormous data sets and heavy computation power. Applications include medical imaging inspection, autonomous vehicles, and predictive maintena...

 Bilal Hussain Malik                                                             Data Mining Methods                                                 Association Rule Learning This method reveals significant variable interactions in big data, primarily for market basket analysis. Apriori identifies rules like "customers who buy X also buy Y" through support, confidence, and lift metrics. These patterns are then used by retailers for product position, promotions, and stock control. While beneficial in transactional data mining, the procedure can generate numerous unimportant rules that need to be well-filtered. It is excellent at discovering latent purchasing patterns but needs big data to generate useful results....

Bilal Hussain Malik                                                          Data Mining Methods                                                          Regression Analysis Regression predicts constant outcomes by estimating dependencies between independent and dependent variables. Linear regression supports constant forecasting (e.g., house prices), while logistic regression estimates probabilities for binary outcomes. Applications include sales forecasting, risk assessment, and scientific modeling. The technique assumes linear relationships and normally distributed errors, with verification done with residual analysis. Regularization methods prevent overfitting in complex models. Regression provides...

Bilal Hussain Malik                                                         Data Mining Methods                                                                Clustering Clustering distinguishes similar data points without pre-assigned labels using algorithms like k-means. Clustering is utilized by retailers for market segmentation based on similarities in purchasing behavior. In contrast to classification, clustering discovers natural groupings in data and is therefore handy for exploratory analysis. Considerations encompass the discovery of optimal numbers of clusters and the choice of suitable distance measures. Applications vary from anomaly detection to image segmentation and social netw...

Bilal Hussain Malik                                       Application of Big Data Techniques to a Problem                                                   Application of Big Data Techniques Problem Identified: The highest priority problem was the 28% dropout rate of first-year students from online STEM courses, highlighting the need for intervention at an early stage as well as enhanced mechanisms for support to the students. Data Collected: To address this issue, some learning trends through digital learning were tracked, including: • Frequency of logins to the Learning Management System (LMS) • Long-term trends in quiz scores • Discussion forum usage • Viewing duration of video lessons Assignment hand-ins timeliness and consistency Data Mining Techniqu...

 Bilal hussain Malik                                        Application of Big Data Techniques to a Problem                                        Implications for individual and society  Drawbacks of Predictive Analytics: While big data yields interesting insights, it cannot record personal, offline variables like part-time work or family commitments that influence the performance of students. Excessive dependence on algorithms risks unfairly labeling some students as destined to fail, reinforcing stereotypes rather than support. Implications for Individuals: Students are growing more concerned about academic surveillance, and they feel uncomfortable being monitored and assessed all the time by computerized systems. A majority of them demanded greater transparency specifically, a...

 Bilal Hussain Malik                                        Application of Big Data Techniques to a Problem                                      Application in Business, Science and Society Business Application: Institutional performance is enhanced by leveraging big data to raise student retention rates—a prime indicator of success for education providers. Greater retention reduces the rate of tuition refunds and enhances institution performance-based funding eligibility, with direct consequences for financial sustainability. Scientific Application: The high-quality information collected is of great use for research, particularly learning analytics and the effectiveness of online pedagogical strategies. Trend analysis on the behavior of students can be done by analysts to make pedagogical ...

Bilal Hussain Malik                                           Application of Big Data Techniques to a Problem                                                    Explaining Big Data Concepts Big data means examination of vast quantities of structured and unstructured educational data—such as how often students log in, submit assignments, watch videos, take quizzes, and engage in discussions. While e-learning became more widespread, EduTrack experienced a tremendous spike in data generation, from 1 terabyte annually to more than 10 terabytes. By harnessing real-time behavioural analytics, the platform was able to identify the earliest warning signs of disengagement by students and could intervene early and precisely. Comparison: Big Data vs. Traditional...

 Bilal Hussain Malik                                       Types of Problem Suited to Big Data Analysis                                                        Unstructured Data Extraction In the internet era, businesses are increasingly depending on big data solutions to extract value hidden in unstructured data - documents, images, audio, and video files that make up roughly 80% of all enterprise data. Social media sites and customer review forums have become goldmines for brand perception analysis, with natural language processing (NLP) algorithms trawling millions of tweets, posts, and comments to extract emerging trends, customer sentiment, and nascent PR crises. So, for instance, when a new smartphone model is launched, manufacturers can monitor ...

Bilal hussain malik                                              Types of Problem Suited to Big Data Analysis                                                                                                               Real Time Decision Making    The instantaneity of the digital economy today enables big data to enable organizations to make real-time, data-based decisions that confer competitive advantage. Retail giants like Amazon employ real-time analytics to dynamically change product prices in real time in response to changes in demand, competitor price action, and inventory lev...

 Bilal Hussain Malik                                                     Implications of Big Data for Society                                                        Public Health and Safety The video segment discusses the key challenges and approaches in COVID-19 vaccination, with specific reference to the Iowa experience. Health workers emphasize the importance of education, fighting vaccine hesitancy through honest discussion rather than lecturing, and listening to people's personal concerns. They highlight successful practices like community partnerships (e.g., churches), incentives (free drinks for immunization), and cross-language assistance for migrant communities. The discussion also touches on such healthcare...

 Bilal Hussain Malik                                                      Implications of Big Data for Society                                           Education and Workforce Transformation Big data is powerfully impacting education and the workforce, with both challenges and opportunities for society. In the education industry, data analytics facilitates customized learning, improves teaching approaches, and optimizes institutional performance. Learning management systems monitor student performance, participation, and behavior and enable instructors to tailor instruction to individual needs. For every example, adaptive learning systems adjust content difficulty in real time based on a student's performance to close gaps in achievement and acc...

 Bilal hussain malik                                                       Implications of Big Data for Society                                                       Economic growth and innovation Big data is one of the most important drivers of economic development and innovation in today's digital economy. With its capacity for analyzing big and intricate data sets, big data allows businesses to make more informed decisions, operate more effectively, and create new value. Industries across the board ranging from finance and retail to agriculture and manufacturing—use big data to understand market behavior, predict customer behavior, and optimize supply chains. This leads to more effective businesses, lower costs, a...

 Bilal Hussain Malik                                               Implications of Big Data for Individuals                                                          Healthcare Developments Big data has revolutionized healthcare by giving doctors more precise, effective, and anticipatory treatment of patients. The collection and analysis of medical histories, genetic information, wearable sensor information, and social and environmental factors allow doctors to better understand each patient's unique needs. This facilitates personalized medicine, with the treatment regimen tailored around the genetic, lifestyle, and medical history of an individual, with improved results and reduced side effects. For example, big data is a...

 Bilal Hussain Malik                                   Implications of Big Data for Individuals(3)                                                                      Better Decision Making Big data allows individuals to take better and more efficient decisions in various areas of life. By providing individuals with vast amounts of real-time, relevant, and tailored data, big data technology allows individuals to measure alternatives, predict outcomes, and choose routes that are most suited to their goals. From planning finances and health care to studies and career choices, big data plays a major role in enhanced decision-making abilities. In private personal finance, for example, personal finance tools like Mint or YNAB...

 Bilal Hussain Malik                                                    Implications of Big Data for Individuals                                                   Improved Public Services Big data is transforming the design and delivery of public services, resulting in more efficient, responsive, and targeted interventions in the lives of individuals. Governments and the public sector gather and analyze vast quantities of information from numerous sources—such as traffic sensors, social media, public health data, and weather systems—to gain insights that enable them to serve people's needs more effectively. For example, transportation authorities use real-time data to optimize traffic light control systems, reduce congestion, and r...

Bilal Hussain Malik                                                Limitations of Predictive Analytics                                               Limited Understanding of Causality One of the greatest flaws of predictive analytics is that it cannot establish causality. While predictive models can easily identify patterns and correlations between variables, they do not necessarily indicate whether a variable is the cause of another. Correlation only means that two behaviors or events occur more frequently together than coincidence would ever lead us to believe, but it is not definitive proof that one is the result of the other. This disability is especially risky when organizations act on forecasts without full understanding of the underlying causes. For...

Bilal Hussain Malik                                                 Limitations of Predictive Analytics                                             Bias in Algorithms and Data  One of the weakest aspects of predictive analytics is the presence of data bias and algorithmic bias. Predictive models use a large proportion of historical data for model construction. If this data reflects societal or institutional biases—such as against the race, gender, age, or economic status of individuals the model is likely to absorb and replicate such biases in prediction. Rather than providing unbiased information, such biased models can reinforce prevailing inequalities. For example, in lending or employment, if previous records show biased preference for particular group...

 Bilal Hussain Malik                                               Limitations of Predictive Analytics                                         Changing Conditions (Dynamic Environments) One of the fundamental assumptions of predictive analytics is that past trends in data will continue to hold true in the future. As accurate as this may be in equilibrium states, the reality is that systems typically operate under dynamic and constantly changing conditions. This is a critical restriction for predictive models because, in the majority of situations, they cannot recover when the circumstances suddenly or unexpectedly shift. Markets, consumers' preferences, technological advancements, and economic climates are all prone to continuous change. For example, a model trained on p...

 Bilal Hussain Malik                                                       Limitations of Predictive Analytics                                                       Overfitting or Underfitting In predictive analytics, proper model complexity is the secret to acquiring accurate and trustworthy results. Two of the most common problems in building models are overfitting and underfitting, both of which compromise a model's ability to perform well on new data. Overfitting occurs when a model is too intricate and it not only learns the actual patterns of the training set but also the noise and irregularities. The result is that the model performs highly on the training set but fails to make accurate predictions on new...

 Bilal Hussain Malik 19/4/25                                                               Types of Visualization                                                                3D Visualizations 3D visualizations display information as three-dimensional graphics, which exhibit depth and a sense of reality not possible in 2D visuals. They are best utilized in scientific, medical, engineering, and architectural applications, where spatial relations and form matter. For example, 3D models occur in medical imaging to simulate organs or tumors, in molecular biology to illustrate protein structure, or in geoscience to simulate terrain and seismicity.  These visualizations a...

 Bilal Hussain Malik 19/4/25                                                              Types of Visualization                                                                  Dashboards Dashboards are graphical display interfaces that collect numerous data visualizations—e.g., tables, charts, and gauges—into a single interface. They are widely used in business intelligence, operations, and monitoring applications for tracking key performance indicators (KPIs), trends, and real-time data. Dashboards provide a bird's-eye view by displaying relevant metrics in an easy-to-read format, often with filtering and drill-down capabilities for deeper analysis. For example, a...

 Bilal Hussain Malik 19/4/25                                                           Types of Visualization                                                                    Infographics Infographics combine graphic elements—icons, images, charts—with brief text to present information in a powerful and engaging way. They are designed to tell a message, simplify hard data, or describe a process, so they are ideal for education, advertising, journalism, and public awareness campaigns. Infographics often include a combination of data visualizations (e.g., pie charts or bar charts), timelines, flow diagrams, and callout text to guide the reader. It is their capacity for dis...

 Bilal Hussain Malik 19/4/25                                                                Types of Visualization                                                               Maps (Geospatial Visualizations) Maps signify information pertaining to locations, and thus they are required for spatial analysis. They enable us to discover patterns, distributions, and relations varying by place. Common types are choropleth maps, heatmaps, dot maps, and symbol maps. Choropleth maps utilize different colors or shades to signify the density or value of data over pre-defined areas like countries or states. Heatmaps indicate intensity or concentration through gradient color, ideal to...

 Bilal Hussain Malik 19/04/25                                                             Types of Visualization                                                       Graphs (Network Diagrams) Network graphs or diagrams are employed to show relationships between objects using nodes (points) and edges (lines). Nodes represent every individual object, i.e., a person, web page, or computer, and edges represent relations or interactions between them. Such representations play an important role in understanding complex networks such as social media interactions, communication networks, citation networks, or transit networks. For example, a social network graph can show users as nodes and friendships as edg...

 Bilal Hussain Malik 18/4/25                                                     Future applications of big data                                                 Astronomy and Astrophysics Astronomy has reached the age of big data discovery. Modern telescopes such as the James Webb Space Telescope, the Vera C. Rubin Observatory, and the Square Kilometre Array (SKA) are generating terabytes to petabytes of data each day. The data consist of images, spectra, and radio signals of billions of objects in the universe. Analysis of such enormous data sets is impossible without advanced big data machinery and machine learning programs. Employing this machinery and algorithms, astronomers discover exoplanets, track supernovae, and investigate the...

 Bilal Hussain Malik 18/4/25                                       Contemporary Applications of Big Data in Society                                                        Company: Siemens Mobility Application of Big Data: Intelligent traffic light systems and smart city traffic management Siemens Mobility applies big data in developing intelligent traffic light systems that ensure optimal traffic flow, reduce congestion, and ensure road safety in modern cities. The systems collect real-time information from various sources—such as traffic cameras, sensors embedded in roads, vehicle GPS tracking data, and weather. Functioning on sophisticated analytics and artificial intelligence, the traffic light system adapts in real-time to changing conditions. For...

Bilal Hussain Malik 18/04/25                                       Contemporary applications of big data in science                                       Precision Medicine and Genomics Big data is an underpinning of precision medicine, which shapes healthcare according to individual genetic, environmental, and lifestyle differences. Through examining enormous quantities of genomic data, healthcare professionals are able to identify genetic mutations and biomarkers for specific diseases. Genomic sequencing of millions of patients generates gigantic datasets that are analyzed by machine learning in order to predict disease risk and recommend personalized treatments. This is especially groundbreaking in oncology, with therapies being tailored to the genetic signature of a patient's tumor. Further, real...

 Bilal Hussain Malik 15/04/25                               Contemporary applications of big data in business(8)                    Challenge/Objective: A niche segment customer competing against                                                market behemoths looking                                       to become a “Niche Segment Leader” A customer who was operating in a niche segment, and wished to compete against large, established companies, had a grand vision of becoming a recognized leader in their niche domain. In order to support this vision, they needed a data-driven approach that would enable them to better know their business, improve cu...

Bilal Hussain Malik 15/04/25                             Contemporary applications of big data in business(7)                Logistics startup with an objective to become the “Uber of the Trucking Sector”                                                     with the help of data analytics A trucking startup that wants to become the "Uber of the Trucking Industry" set out to tap the power of analytics to redefine the way freight is moved. The startup focused on tracking vehicle and driver performance by gathering information from in-cab sensors (vehicle telemetry) as well as trends in order volumes and routes. They sought to simplify operations, reduce inefficiencies, and create a more informed, data-driven logistics system. To make this vision a r...