BIG DATA

BIG DATA

1. Data and Statistical Methods.

   • Types of statistical methods
     • Inferential Statistics
       • Uses a sample to draw conclusions about a larger population.
         • Regression analysis
         • Hypothesis testing
         • ANOVA (Analysis of Variance)
     • Descriptive Statistics:
       • Summarizes the basic features of a dataset.
         • Measures of spread (standard deviation)
         • Data visualization (charts, graphs)
         • Measures of central tendency (mean, median)
     • OTHERS
       • Exploratory Data Analysis: Involves investigating data to identify patterns, anomalies, and relationships.
       • Causal Analysis: Determines the cause-and-effect relationship between variables.
       • Predictive Analysis: Uses historical data to forecast future outcomes

2. Business Analytics.

   • Benefits of Business Analytics
     • Productivity uplift through workforce management
     • Strategic market navigation
     • Cost optimization
     • Operational efficiency enhancement
     • Data-driven decision empowerment
   • WHAT IT'S
     • Business analytics is a systematic approach to analyzing data for valuable insights that guide smart decision-making in a company. It uses diverse techniques and tools to examine data from different sources, find patterns, and make recommendations for improving business processes.
       • Business analytics begins with a dataset or commonly with a database. As databases grow, they need to be stored somewhere. Technologies, such as computer and data warehousing, store data. Database storage areas have become so large that a new term was devised to describe them. Sedkaoui, S. (2018). Data Analytics and Big Data: ( ed.). Wiley.
         • RONALD DURAN, ESPECIALIZACION EN CIENCIA DE DATOS Y ANALITICA UNAD

3. Big data analytics helps organizations harness their data and use it to identify new opportunities. That, in turn, leads to smarter business moves, more efficient operations, higher profits, and happier customers.