Predictive, Prescriptive Analytics for Decision Making

Rating 4.3 out of 5 (14 ratings in Udemy)

What you'll 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 …

Rating 4.3 out of 5 (14 ratings in Udemy)

What you'll 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.

Description

PREDICTIVE, PRESCRIPTIVE ANALYTICS FOR BUSINESS DECISION MAKING

LEARN HOW TO BUILD PREDICTIVE AND PRESCRIPTIVE MODELS USING NUMERICAL DATA

Prescriptive analytics can cut through the clutter of immediate uncertainty and changing conditions. It can help prevent fraud, limit risk, increase efficiency, meet business goals, and create more loyal customers.

Prescriptive analytics is a type of data analytics—the use of technology to help businesses make better decisions through the analysis of raw data. Specifically, prescriptive analytics factors information about possible situations or scenarios, available resources, past performance, and current performance, and suggests a course of action or strategy. It can be used to make decisions on any time horizon, from immediate to long term.

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

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

Duration 4 Hours 58 Minutes

Paid

Self paced

Beginner Level

English (US)

129

Rating 4.3 out of 5 (14 ratings in Udemy)

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