Big Data

Big Data

1. Julian Francisco Murillo Parrado

2. Business Analysis

   • Its objective is to extract useful knowledge for decision making.
     • It consists of: data exploration, data mining, data visualization, predictive modeling and data-driven decision making.
       • Data Exploration: Identifying Patterns and Trends
       • Data Mining: Algorithms for discovering relationships in large volumes of data.
       • Data Visualization: Charts and dashboards for interpretation.
       • Predictive Modeling: Use of statistical and machine learning models.
       • Data-Driven Decision-Making: Using information for business strategies.

3. Data and Methods Statistics

   • Data
     • Data types: Structured data (tables, relational databases), unstructured data (images, videos, text), and semi-structured data (JSON, XML).
     • Data processing: Extraction, transformation, and loading (ETL), cleansing and normalization, and storage in databases and data lakes.
     • Relationship between Data and Statistical Methods: Data is the raw material for analysis, Statistical Methods allow patterns to be extracted and knowledge to be generated, and both are essential for evidence-based decision-making.
   • Statistical methods
     • Descriptive statistics: measures of central tendency (mean, median, mode) and measures of dispersion (variance, standard deviation).
     • Inferential statistics: hypothesis testing, confidence intervals, and regression and correlation.
     • Predictive Models: linear and nonlinear regression, classification algorithms and time series.
     • Sampling Techniques: Random, Stratified, Systematic, and Big Data Methods (Adaptive Sampling)