Business Analytics Course in Mysuru Overview


Business Analytics is a field that involves using data, statistical analysis, and quantitative methods to gain insights into business performance and drive strategic decision-making. It combines techniques from data analysis, statistics, and data science to help organizations make informed decisions, optimize operations, and achieve business goals.

  • Definition and importance of Business Analytics
  • Overview of analytics tools and techniques
  • Business Analytics lifecycle: Problem statement, data collection, analysis, interpretation, decision- making

  • Introduction to data analysis concepts
  • Descriptive vs. inferential statistics
  • Data types and data sources

  • Introduction to data analysis concepts
  • Data quality assessment and cleaning techniques

  • Data preprocessing: Handling missing values, outliers, normalization, encoding

  • Descriptive statistics: Mean, median, mode, variance, standard deviation
  • Data visualization techniques:
  • Histograms, box plots, scatter plots
  • Heatmaps, pair plots for correlation analysis

  • Hypothesis testing:
  • Regression analysis:
  • Regression analysis:
  • Linear regression, logistic regression
  • Model evaluation metrics: R- squared, AIC, BIC

  • Introduction to predictive modeling
  • Machine learning algorithms for business analytics:
  • Decision trees, random forests, SVMs
  • Naive Bayes, k-nearest neighbors

  • Introduction to time series data
  • Techniques for forecasting:
  • Moving average, exponential smoothing
  • ARIMA models

    • Customer segmentation techniques:

  • RFM analysis, clustering algorithms

    • Cluster analysis:

  • K-means clustering, hierarchical clustering

  • Linear programming basics
  • Optimization models and applications in business

  • Overview of BI tools (e.g., Tableau, Power BI)
  • Creating dashboards and reports for business analytics

  • Ethical considerations in data analysis and decision-making
  • Compliance with data protection regulations (e.g., GDPR, CCPA)