Predictive, Prescriptive Analytics for Business Decision Making

LEARN HOW TO BUILD PREDICTIVE AND PRESCRIPTIVE MODELS USING NUMERICAL DATA

Foster and hone financial expertise to manage strategic business units

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Key Highlights

Amazing course for even beginners in the field of Predictive analytics. Highly recommend to do this course for enhancing the analytical skills. Examples and case studies explain the concepts very well.

Suresh M

Informatica

I have learned a lot from the course. Many interesting topics that help me to understand this field of Analytics. This course has generated more interest for me to continue learning more in this specialization

Rahul A

Mindworks

What will you Learn?

Understand the difference between Cross sectional and Longitudinal data.
Differentiate between a prediction and forecasting problem scenario and apply these concepts towards data led decision making.
Understand Parametric and Non Parametric modelling approach towards addressing the key tradeoff between Predictive accuracy and Explain- ability of models.
Use LPP towards building multiple “What if “ scenarios which are widely used in business decision making.
Conceptualize Gradient Descent Algorithm which is a key foundation for most of the widely used Machine learning algorithms to be introduced subsequently.

Top skills you will learn

Develop predictive and prescriptive models using numerical data
Time-series Forecasting
Optimization through Linear Programming
Gradient Descent and it’s applicability in Machine Learning
Framework towards business decisions

Course Curriculum

Regression Fundamentals
The linear regression equation
Linear Regression explained
Linear Regression with independent variable
Interpreting R -Squared
Evaluating Model Performance
Key assumptions of Linear Regression
Residual Analysis
Statistical tests to validate assumptions
Correlation and Casuation
Heat map and Scatter plots
Multiple Linear Regression use case
Interpreting regression outputs
Regression use cases
Chapter Quiz

Time Series Fundamentals
Visualizing time series data using plots
Components of Time series
Stationary time series
Forecasting fundamentals
Forecasting techniques
Forecasting techniques : Exponential Smoothing
Forecasting techniques : Holt’s method
Forecasting techniques : Holt’s Winter method
Forecasting techniques : ACF & PACF
Forecasting techniques : ARIMA
Forecasting techniques : ARIMA models in Python
Applications of Time Series
Chapter Quiz

Introduction to Prescriptive Analytics
Gradient Descent (& code)
Gradient descent fundamentals
Stochastic Gradient descent regression
Chapter Quiz

Linear Programming fundamentals
Components of LPP
Formulating the LPP model
Solving linear models-Graphical method
Solving linear models -Simplex method
Assumptions of LPP
Business applications of LPP
Chapter Quiz

Parametric & Non Parametric Methods – Model building
Tradeoffs -Accuracy vs Explainability
Chapter Quiz

Framework to choose the right model to address business problems
Chapter Quiz

Ideal For

1 – 8 yrs work experience.- Engineering, Math/Statistics/Programming background preferred
Typical roles: Domain experts, Engineers, Software and IT Professionals, Project
Managers, Business Analysts, Consultants, Entrepreneurs.

Engineers with over 5 years of experience

Common Scenarios to Enroll

34% of the firms that were top in class in using analytics got about 6% more profitability and were about 5% more productive

Source: Harvard Business Review

Predictive, Prescriptive Analytics for Business Decision Making

LEARN HOW TO BUILD PREDICTIVE AND PRESCRIPTIVE MODELS USING NUMERICAL DATA

Foster and hone financial expertise to manage strategic business units

Sign In

Key Highlights

Course Length

100% Online

Effort

Learning Access

What will you Learn?

Top skills you will learn

Course Curriculum

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