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Predictive, Prescriptive Analytics for Decision Making

Business

Predictive, Prescriptive Analytics for Decision Making, available at $49.99, has an average rating of 3.85, with 44 lectures, 7 quizzes, based on 41 reviews, and has 305 subscribers.

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You will learn about 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. This course is ideal for individuals who are Individuals seeking a career in Product Management or Professionals aiming to transition into Product Management or Product Managers looking to enhance their skillset or Entrepreneurs or Software and IT Professionals or Project Managers or Business Analysts or Math/Statistics/Programming background preferred Typical roles It is particularly useful for Individuals seeking a career in Product Management or Professionals aiming to transition into Product Management or Product Managers looking to enhance their skillset or Entrepreneurs or Software and IT Professionals or Project Managers or Business Analysts or Math/Statistics/Programming background preferred Typical roles.

Enroll now: Predictive, Prescriptive Analytics for Decision Making

Summary

Title: Predictive, Prescriptive Analytics for Decision Making

Price: $49.99

Average Rating: 3.85

Number of Lectures: 44

Number of Quizzes: 7

Number of Published Lectures: 44

Number of Published Quizzes: 7

Number of Curriculum Items: 51

Number of Published Curriculum Objects: 51

Original Price: $29.99

Quality Status: approved

Status: Live

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.

Who Should Attend

  • Individuals seeking a career in Product Management
  • Professionals aiming to transition into Product Management
  • Product Managers looking to enhance their skillset
  • Entrepreneurs
  • Software and IT Professionals
  • Project Managers
  • Business Analysts
  • Math/Statistics/Programming background preferred Typical roles

Target Audiences

  • Individuals seeking a career in Product Management
  • Professionals aiming to transition into Product Management
  • Product Managers looking to enhance their skillset
  • Entrepreneurs
  • Software and IT Professionals
  • Project Managers
  • Business Analysts
  • Math/Statistics/Programming background preferred Typical roles

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

Course Curriculum

Chapter 1: Chapter 1 – Overview of Predictive Analytics

Lecture 1: Overview, Models & Modelling

Lecture 2: Recap -Key libraries

Lecture 3: Understanding cross sectional and longitudinal data

Chapter 2: Chapter 2 – Simple Linear Regression and Multiple Linear Regression

Lecture 1: Regression Fundamentals

Lecture 2: The linear regression equation

Lecture 3: Linear Regression explained

Lecture 4: Linear Regression with independent variable

Lecture 5: Interpreting R -Squared

Lecture 6: Evaluating Model Performance

Lecture 7: Key assumptions of Linear Regression

Lecture 8: Residual Analysis

Lecture 9: Statistical tests to validate assumptions

Lecture 10: Correlation and Casuation

Lecture 11: Heat map and Scatter plots

Lecture 12: Multiple Linear Regression use case

Lecture 13: Interpreting regression outputs

Lecture 14: Regression use cases

Chapter 3: Chapter 3 – Time Series Forecasting

Lecture 1: Time Series Fundamentals

Lecture 2: Visualizing time series data using plots

Lecture 3: Components of Time series

Lecture 4: Stationary time series

Lecture 5: Forecasting fundamentals

Lecture 6: Forecasting techniques

Lecture 7: Forecasting techniques : Exponential Smoothing

Lecture 8: Forecasting techniques : Holt’s method

Lecture 9: Forecasting techniques : Holt’s Winter method

Lecture 10: Forecasting techniques : ACF & PACF

Lecture 11: Forecasting techniques : ARIMA

Lecture 12: Forecasting techniques : ARIMA models in Python

Lecture 13: Applications of Time Series

Chapter 4: Chapter 4 – Prescriptive Analytics -Gradient Descent

Lecture 1: Introduction to Prescriptive Analytics

Lecture 2: Gradient Descent (& code)

Lecture 3: Gradient descent fundamentals

Lecture 4: Stochastic Gradient descent regression

Chapter 5: Chapter 5-Prescriptive Analytics-Linear Programming Problems

Lecture 1: Linear Programming fundamentals

Lecture 2: Components of LPP

Lecture 3: Formulating the LPP model

Lecture 4: Solving linear models-Graphical method

Lecture 5: Solving linear models -Simplex method

Lecture 6: Assumptions of LPP

Lecture 7: Business applications of LPP

Chapter 6: Chapter 6 -Business Decisions I

Lecture 1: Parametric & Non Parametric Methods -Model building

Lecture 2: Tradeoffs -Accuracy vs Explainability

Chapter 7: Chapter 7 -Business Decisions ..II

Lecture 1: Framework to choose the right model to address business problems

Instructors

Rating Distribution

  • 1 stars: 4 votes
  • 2 stars: 1 votes
  • 3 stars: 7 votes
  • 4 stars: 11 votes
  • 5 stars: 18 votes

Frequently Asked Questions


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You can view and review the lecture materials indefinitely, like an on-demand channel.


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