Predictive, Prescriptive Analytics for Decision Making

Learn how to build Predictive and Prescriptive Models using Numerical Data

3.90 (37 reviews)
Udemy
platform
English
language
Data & Analytics
category
Predictive, Prescriptive Analytics for Decision Making
287
students
5 hours
content
May 2021
last update
$49.99
regular price

What you will 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

Content

Chapter 1 - Overview of Predictive Analytics

Overview, Models & Modelling
Recap -Key libraries
Understanding cross sectional and longitudinal data
Chapter Quiz

Chapter 2 - Simple Linear Regression and Multiple Linear Regression

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 2 Quiz

Chapter 3 - Time Series Forecasting

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

Chapter 4 - Prescriptive Analytics -Gradient Descent

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

Chapter 5-Prescriptive Analytics-Linear Programming Problems

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

Chapter 6 -Business Decisions I

Parametric & Non Parametric Methods -Model building
Tradeoffs -Accuracy vs Explainability
Chapter - 6 Quiz

Chapter 7 -Business Decisions ..II

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

Reviews

Carlos
January 31, 2023
Good overview of predictive analytics and business cases, but sometimes is too general. It does not have available the slides to download.
Allen
February 9, 2022
No hands on - lots of talk about different python packages but not hands on how to install, use or build
Gillian
January 19, 2022
Straightforward content and the instructor does not read straight from the slide and is very knowledgeable. Easy to digest content.

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udemy ID
5/3/2021
course created date
5/22/2021
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