BIG DATA

BIG DATA

1. Data that cannot be processed using standard databases because it is large, moves quickly, or is too large for traditional data processing tools.

   • Data and Statistical Methods
     • Set of algorithmic techniques used to analyze large and complex data sets (Big Data) in order to discover patterns, trends and obtain valuable information to make informed decisions
       • Descriptive statistics Big Data Summary
         • • Measures of central tendency and dispersion: mean, median, mode, variance, standard deviation. • Summary tables and graphs: histograms, boxplots, heatmaps for millions of records. • Automated exploratory analysis: data profiling to discover basic patterns.
       • Statistical inference: Large-scale hypothesis estimation and testing
         • • Efficient stratified random sampling for large datasets (instead of using everything). • Robust estimators for population parameters, e.g., distributed bootstrap. • Massive hypothesis testing with Type I error correction (FDR, Bonferroni). • Prediction confidence intervals calculated using online (streaming) methods.
       • Specific methods for massive or streaming data
         • • Online or incremental algorithms: update estimators without storing everything. • Sketching and approximate counting (HyperLogLog, Count-Min Sketch) for cardinality metrics. • Graph data analysis (PageRank, centrality, modularity). • Spatial and spatiotemporal statistics for IoT/sensors.
       • Multivariate and dimension reduction methods
         • • Principal component analysis (PCA) and scalable variates (randomized PCA). • Cluster analysis: distributed k-means, DBSCAN, Gaussian Mixture Models. • Correspondence/factor analysis for massive categorical data. • Self-organizing maps and t-SNE/UMAP for visual exploration.
       • Relationship and prediction models
         • • Linear and logistic regression (with LASSO/Ridge regularization for high dimensionality). • Time series models (ARIMA, Prophet, Holt-Winters) adapted to streams. • Survival analysis and proportional hazards models for large cohorts. • Simplified structural equation models for big data.
   • Business analysis
     • It allows you to extract value from large volumes of data, improving strategic decision-making, process optimization, new product development, and personalizing the customer experience.
       • Trend Prediction
         • Companies can anticipate product demand, market trends, and even predict equipment failures, giving them a proactive advantage.
       • Process optimization
         • Data analysis helps identify bottlenecks and areas for improvement in processes, increasing efficiency and reducing costs.
       • Personalization of products and services
         • Collecting data on customer preferences and behaviors allows for tailored product and service offerings, as seen in the personalized recommendations and offers from Starbucks and other businesses.
       • decision making
         • By analyzing historical data and patterns, companies gain deeper insight into their operations, enabling them to make more informed and accurate decisions.