Business Analytics

Business Analytics

1. Digital Transformations

   • A company's operation or day
     • Combination of analytical and social skills in organizational culture
       • Application and technology infrastructure with micro and macro architecture
         • Data driven mindset
           • Culture of metrics, kpi and dashboard

2. Data use in the digital enterprise

   • Identifying and framing the situation
     • Interaction with quantitative profiles
       • Different types of analyses
         • Descriptive, predictive, prescriptive, and assisted.
       • Train analysts in business knowledge, language, and hypothesis formulation.
     • Instead of considering how to solve a problem, it allows you to identify the problem to be solved.

3. Integrate new technologies

   • Algorithm construction
     • Predominant processing environments
       • Hapdoop Mapreduce, Apache Spark, Gpu computing
         • Distributed computing.
           • Solve massive computing problems by using clusters to handle computation and distribution of the data obtained.
             • Distributed Processing Scenarios
               • Batch processing, Stream processing,graph processing, GPU processing,
                 • Significant challenges exist
                   • Algorithms that are efficient and capable of working with fractional data will generate associative and combinable data.
                     • Distribute data efficiently among servers without saturating the network.

4. Data and Statistical Methods

   • Descriptive Analysis:
     • Measures of Central Tendency: Mean, median, and mode. Measures of variability: Standard Deviation/Variance, Range, and Interquartile Range (IQR). Frequency Distributions Data visualization: Histograms, bar charts, line charts, pie charts, scatter plots, and scatter plots.
     • Diagnostic analysis:
       • Correlation analysis Hypothesis testing: T-tests, ANOVA, and chi-square tests Data mining and exploration techniques
         • Predictive analysis
           • Regression analysis: Linear, logistic, and multiple regression Time series analysis Time Series Analysis: ARIMA and exponential smoothing. Machine learning algorithms: Decision Trees, Random Forests, Gradient Boosting Machines, and Neural Networks (Deep Learning). Survival analysis
           • Prescriptive analysis
             • Optimization models Simulation (e.g. Monte Carlo ,simulation) Decision analysis:
               • Fundamental concepts applied in analysis:
                 • Sampling techniques Data quality and data preprocessing Exploratory data analysis (EDA) Causality vs. correlation A/B testing (experimentation)