Big Data contents

Big Data contents

1. Business Analytics.

   • A discipline that makes it easier for companies and organizations to make decisions based on data.
     • The analysis.
     • The visualization of information.
     • The recollection.
   • is applied in
     • areas such as finance, marketing, human resources, logistics and sales, among others.
   • Types of categories
     • Descriptive business analytics
       • It focuses on exposing what happened in the past, which allows us to observe the evolution of historical data to understand the situations that arise from them.
     • Diagnostic business analytics
       • It is derived from the descriptive, and focuses on identifying the causes of the problems or trends observed there.
     • Prescriptive business analytics
       • focuses on suggesting specific actions to improve company performance.
     • Predictive business analytics
       • uses mathematical models to forecast what may happen in the future.
   • used tools
     • Google Analytics
       • collect and analyze data from a website
         • allows you to create conversion goals and funnels, perform segment analysis, and link the tool with other Google platforms
     • Tableau
       • analyze and visualize large data sets in a simple and intuitive way.
         • Connect to multiple data sources, produce custom charts, and use advanced analytics tools such as predictive and time series.
     • IBM Cognos Analytics
       • Business analytics platform for creating reports, dashboards, and advanced data analysis.
         • The platform also has security tools that guarantee data protection and privacy.
     • SAS Analytics
       • collect, analyze and visualize large data sets.
         • analyze large amounts of data in real time.
     • Microsoft Power BI
       • data collection, analysis and visualization
         • security applied to privacy and the protection of sensitive data,
     • QlikView
       • echopilate, analyze and visualize large volumes of information from different sources.
         • speed to manipulate data instantly
     • Apache Hadoop
       • open source data processing and analysis
         • Apache Hadoop's main advantages is its ability to process data in parallel on multiple servers, allowing for greater speed and efficiency in data processing.
     • Lenguajes R y Python
       • efficiency and versatility to work with large data sets.
         • The R language specializes in statistics and data analysis.
           • Sandra Milena Pardo Gomez

2. Data and Statistical Methods.

   • Data
     • They are numbers that represent the modalities of the variables.
   • analysis of data.
     • Descriptive statistics.
       • uses indices such as the mean and median to summarize data.
     • Inferential statistics.
       • They extrapolate results from data using statistical tests such as the Student t test.
   • Factors
     • The main objective and purpose of the study.
     • The type and dispersion of the data used.
     • The type of observations (paired/unpaired).
   • Half
     • Like averaging, it is the initial technique used to perform statistical analysis.
   • Standard deviation
     • It is frequently used when analyzing the spread of data points, whether they are clustered or not.
   • Regression
     • connection between an independent variable and a dependent variable
   • Hypothesis testing
     • This approach focuses on determining whether a given statement or conclusion is valid for the data collection. It allows data to be compared with various assumptions and hypotheses.
   • Determine sample size
     • It consists of examining a smaller sample or data.