Big Data

Big Data

1. Data and statistical methods

   • They are divided into:
     • Descriptive statistics
       • Summarize and organize data using the mean, median and standard deviation.
         • objective
           • Facilitate understanding of data, such as graphical representations, histograms and pie charts.
     • Inferential Statistics
       • It allows inferences to be made about a population based on a sample.
         • objective
           • Model patterns and make predictions, using techniques such as hypothesis testing, regression analysis and analysis variance.
   • The data can be quantitative (numerical) or qualitative (categorical).
     • They are collected through surveys, experiments or records administrative and are used to describe phenomena and take decisions.

2. Business analytics

   • Use of data and analytical techniques to improve decision making within an organization.
     • Key features:
       • Data-driven decision making
         • Allows companies to base their decisions on concrete data, reducing risks and increasing effectiveness of strategies.
       • Customer knowledge
         • Facilitates the understanding and preference of customers, allowing personalization of products and services.
       • Operations Optimization
         • Identify inefficiencies in processes, improving resource management and reducing costs.
       • Innovation
         • Through analysis, it is discovered patterns that lead to new opportunities and development products.
     • Methods used
       • Descriptive analysis
         • Summarize historical data to understand past trends.
       • Predictive analysis
         • Use statistical models to predict future results.
       • Prescriptive analysis
         • Offers recommendations on possible actions based on previous analysis.