Business Analytics For Managers

Learn how to process and analyze data, use key analysis tools, apply Python programming, and create visualizations that can inform key business decisions based on Data.

Business Analytics is the collection of Mathematical techniques to collect, transform, and organize data in order to draw conclusions, make predictions, and drive informed decision making.

The objective is to find a mathematical relationship between a target, response, or dependent variable and various predictor or independent variables, with an assigned level of confidence.

The accuracy and usability of business analytics is wholly dependent on how granular the analysis has been run and the type of assumptions that are being made. Forward-thinking organizations will utilize predictive models for a variety of business functions.

This course begins with basic descriptive statistics and progresses to regression analysis.
Throughout the course, you will receive clear guidance on how to implement analytical techniques using Python. No matter your job function or career aspirations, this course will demystify data analysis and equip you with concrete skills to apply in your work or further studies.

The training is designed specially for Managers. The trainer is an expert. The execution of the training assures that it does not exceeds a particular level of technality required by Managers to benefit from the training.

Modules:

• Module 1
  • Python
  • Database skills
  • Statistics and Probability
  • Linear Algebra
• Module 2
  • Simple Regression
  • Method of least square
  • R2
  • Interpretation of regression coefficients
  • Basic Machine Learning Techniques
• Module 3
  • Multiple regression model
  • Interpretation of ANOVA results of regression,
  • Method of regression-hierarchical (block wise entry), forced entry
  • Stepwise regression, forward pass, backward pass
  • Interpretation of beta values
• Module 4
  • Advanced Machine Learning Techniques :- Multiple regression-linearity
  • Normality, autocorrelation
  • Multi-co linearity-VIF and tolerance
• Module 5
  • Curvilinear regression
  • Dummy variable in regression
  • Interaction effect of predictive variables and interpretation of results
  • Data -> Insights -> Decision
  • Visualizing, Testing, and Deploying the Model