Business Analytics y Data and Statistical Methods

Business Analytics y Data and Statistical Methods

1. Business Analytics

   • Refers to the process of transforming data into useful insights for decision-making in an organization. It uses different types of analyses to answer key business questions. Closely related to Business Intelligence (BI), but with a focus on data analysis and actionable insights rather than just reporting.
     • Includes three main types
       • Descriptive Analytics:
         • •Focuses on understanding what has happened in the past. •Techniques: Trend analysis, averages, distributions. •Tools:Dashboards, KPI (Key Performance Indicators) reports. •Example: Determining the average monthly sales over the past year.
       • Predictive Analytics
         • • Used to forecast or predict future outcomes based on historical data. • Techniques: Regression models, Time series analysis, Classification models. • Tools: Python (scikit-learn), R, SAS. • Example: Predicting customer buying behavior for the next few months.
       • Prescriptive Analytics
         • • Suggests actions or decisions that could optimize future outcomes based on predictive analysis. • Techniques: Optimization algorithms, Simulations, Linear programming. • Tools: IBM CPLEX, Gurobi, Excel Solver. • Example: Suggesting the best pricing strategy to maximize profits in a particular market.

2. Data and Statistical Methods

   • These are the tools and techniques used to collect, process, and analyze data with the goal of drawing valid and reliable conclusions.
     • Data Collection
       • • The process of gathering relevant data for analysis. •Sources: Internal databases, APIs, surveys, transactional data. •Importance: Incorrect data collection can impact the entire analysis process.
     • Data Cleaning
       • •The process of preparing data by removing errors, duplicates, and handling missing values. •Techniques: Data imputation, Outlier detection, Data normalization. •Example: Filling in missing values in a customer database. •Tools: Python (pandas), R, SQL.
     • Data Visualization
       • The process of visually representing data to facilitate understanding and analysis. Data visualization helps identify patterns, trends, and outliers that may not be apparent in raw data. Tools: Tableau, Power BI, Matplotlib, Seaborn (Python). Example: Creating a bar chart to show employee performance over time, allowing business managers to easily compare productivity across different teams or periods.
     • Statistical Methods
       • Mathematical techniques that allow for objective data analysis and decision-making by drawing conclusions from data samples. Statistical methods help quantify relationships between variables and test hypotheses. Regression analysis: To model relationships between variables. Hypothesis testing: To determine whether observed results are statistically significant. Analysis of variance (ANOVA): To compare multiple populations or groups. Time series analysis: To analyze data dependent on time.
     • Machine Learning
       • A field of artificial intelligence that allows machines to learn from data and make predictions or decisions without being explicitly programmed for every task. Machine learning is used to build predictive models based on historical data patterns.
         • Algorithms: Classification (K-Nearest Neighbors, Support Vector Machines), Clustering (K-means), Linear and Logistic Regression, Neural Networks. Tools: scikit-learn, TensorFlow, Keras. Example: Using a classification model to predict whether a customer will churn (leave) based on their past interactions and behaviors, allowing businesses to target customer retention strategies effectively.