Business Analytics

Business Analytics

1. Definition

   • Process of using data, statistical analysis, and predictive models to make business decisions.
     • Objectives
       • Improve decision-making
         • Optimize processes
           • Identify market opportunities
     • Components
       • Descriptive Analytics : Interpretation of historical data to understand what has happened.
         • Predictive Analytics : Use of data and statistical models to foresee future trends.
           • Prescriptive Analytics: Recommendation of actions to influence future outcomes.
     • Data and Statistical Methods
       • Definition A set of techniques for collecting, analyzing, and interpreting quantitative and qualitative data.
         • Types of Data
           • Structured Data organized in tables (e.g., databases).
           • Unstructured Data without a specific organization (e.g., text, images).
         • Statistical Methods
           • Descriptive Statistics Measures such as mean, median, standard deviation.
             • Inferential Statistics Methods for making predictions from a sample of data (e.g., hypothesis testing, confidence intervals)
               • Regression Models that predict a dependent variable from independent variables (e.g., linear regression, logistic regression).
                 • Multivariate Analysis Techniques to analyze more than one variable at a time (e.g., principal component analysis, clustering).
     • Interconnection between Business Analytics and Statistical Methods
       • Definition A set of techniques for collecting, analyzing, and interpreting quantitative and qualitative data.
         • Types of Data
           • Structured Data organized in tables (e.g., databases).
           • Unstructured Data without a specific organization (e.g., text, images).
         • Statistical Methods
           • Descriptive Statistics Measures such as mean, median, standard deviation.
             • Inferential Statistics Methods for making predictions from a sample of data (e.g., hypothesis testing, confidence intervals).
               • Regression Models that predict a dependent variable from independent variables (e.g., linear regression, logistic regression).
                 • Multivariate Analysis Techniques to analyze more than one variable at a time (e.g., principal component analysis, clustering)
     • Interconnection between Business Analytics and Statistical Methods
       • Data usage: Business analytics relies on the use of accurate, organized, and quality data.
         • Predictive models: Statistical methods, especially predictive ones, are the foundation of business models to anticipate outcomes.
           • Decision-making: The results of statistical analyses feed into strategic decision-making in businesses.
     • 4. Technologies and Tools
       • Analytics Software: R, Python, Excel, Power BI, Tableau
         • Big Data and Machine Learning: Advanced applications for large-scale analysis and decision automation.