Online Course
AI Applications for Business Success

Name: AI Applications for Business Success Course
Price: 36 USD

with Horia Mărgărit

Leverage AI for business success: enhance supply chain efficiency and solve critical challenges

4.8

863 reviews on

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4,896 students already enrolled

Skill level:

Advanced

Duration:

2 hours

Lessons (2 hours)

Practice exams (12 minutes)

CPE credits:

3.5

CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of professionals must earn to maintain their licenses and stay current with regulations and best practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's official website: www.nasbaregistry.org

Accredited

certificate

What you learn

Master the SMART goals framework to define measurable business objectives.
Perform SWOT analysis to uncover business challenges and design solutions.
Discover advanced AI techniques to infer causation in business analytics.
Understand supply chain processes and key value drivers.
Master hybrid experiments to evaluate whether AI solutions drive value.

Topics & tools

TheoryPythonCareer DevelopmentBusiness AnalyticsMachine LearningData AnalysisAIBusiness Skills

Your instructor

Horia Mărgărit

Technical Director & AI Architect | Expert in RL, NLP, Deep Learning, and LLMs

Track record

Bringing real-world expertise from leading global companies

Academic background

Master's degree, Statistics, Data Science

Media and recognition

Featured in CIO Magazine and Forbes for AI business predictions
Patent holder in modular neural network architectures

Ask Horia a question

Course overview

Accredited certificates

Course OVERVIEW

Description

CPE Credits: 3.5 Field of Study: Specialized Knowledge
Delivery Method: QAS Self Study

AI isn’t just a fancy concept that powers self-driving vehicles, robots, and high-tech companies. Most organizations – regardless of their size and industry – can benefit from the application of artificial intelligence. In this course, you’ll get an overview of business analytics and find out how to define SMART goals and conduct SWOT analysis. You’ll go through the challenges and opportunities of supply chain analytics to then determine the business problem we’ll tackle throughout the course.

Prerequisites

Python (version 3.8 or later), pandas library, and a code editor or IDE (e.g., Jupyter Notebook, Spyder, or VS Code)
Intermediate Python skills are required.
Familiarity with basic statistics and linear algebra is helpful but not mandatory.

Advanced preparation

Curriculum

27 lessons 2 exams

1. Introduction

6 min

This is the intro to the course. We give you a taste of what you’ll learn throughout the course, as well as a step by step of what’s to follow in each of the following sections.

6 min

This is the intro to the course. We give you a taste of what you’ll learn throughout the course, as well as a step by step of what’s to follow in each of the following sections.

What Does the Course Cover Free

Course Resources Free
2. Business Goals

25 min

Here we introduce the case study we’ll follow throughout the course and define our business goal, or SMART goal. We also do a SWOT analysis and discuss main problems in Business Analytics.

25 min

Here we introduce the case study we’ll follow throughout the course and define our business goal, or SMART goal. We also do a SWOT analysis and discuss main problems in Business Analytics.

Introduction to the business case Free

SWOT Analysis Free

SMART Goals Free

Limitations of the BI Approach Free

Correlation vs. Causation Free

Making Recommendations with Descriptive Statistics Free
3. Approaches to solving the business objective

15 min

Here we take a look at how a Business Intelligence analyst might tackle the problem, and see the limitation of the BI approach. We’ll also start discussing shadow manifolds and Taken’s theorem.

15 min

Here we take a look at how a Business Intelligence analyst might tackle the problem, and see the limitation of the BI approach. We’ll also start discussing shadow manifolds and Taken’s theorem.

Introduction to the approaches

The BI Approach

State Space and Takens' Theorem

Shadow Manifolds and K-Nearest Neighbors
4. Artificial Intelligence in Business

78 min

This is the main body of the course. Here, we delve into topics starting from Attainability, then, we go through techniques Convergent Cross Mapping – which is where the power of AI to infer causality lies. We also dedicate time to SHAP values and LIME to evaluate the performance of a model.

78 min

This is the main body of the course. Here, we delve into topics starting from Attainability, then, we go through techniques Convergent Cross Mapping – which is where the power of AI to infer causality lies. We also dedicate time to SHAP values and LIME to evaluate the performance of a model.

Quantifying Attainability

Gradient Boosted Machines: Part 1

Gradient Boosted Machines: Part 2

Gradient Boosted Machines: Part 3

SHAP Values

Friedman's H-Statistic

LIME

Waterfall Charts 1

Waterfall Charts 2

Causation: Traditional Statistical Methods

Causation: Advanced Statistical Methods

Time Series Forecasting with Takens' Theorem
5. Artificial Intelligence Recommends Metrics

12 min

In this section, we devote time to parametric tests and designing a hybrid experiment. In other words, we discuss how we can show if the recommended metrics by our AI model, have actually generated the change we see in metrics.

12 min

In this section, we devote time to parametric tests and designing a hybrid experiment. In other words, we discuss how we can show if the recommended metrics by our AI model, have actually generated the change we see in metrics.

Introduction

The Hybrid Experiment

Quantile Difference Tests

Practice exam
6. Course exam

45 min

45 min

Course exam

Free lessons

1.1 What Does the Course Cover

5 min

1.2 Course Resources

1 min

2.1 Introduction to the business case

2 min

2.2 SWOT Analysis

5 min

2.3 SMART Goals

7 min

2.4 Limitations of the BI Approach

4 min

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Early in my career, I took 365 Data Science courses that built a strong foundation in data science and financial analysis with Excel. The detailed, visual content—from basics to advanced—helped me grasp key concepts, develop practical skills, and apply them to real projects in multiple industries. The instructors, quick responses to questions, and collaborative community made learning even more engaging. 365 truly supports continuous growth, and I recommend it to anyone starting or advancing in data and AI.

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94%

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successfully change

or advance their careers.

Very well designed, well narrated course. Starts from scratch and ends up at the advanced levels. The icing on the cake is the integration of My SQL with Tableau at the end of the course which is a very important aspect of today's Data Analytics field. I have personally saved the last 10 practice exercises to dive in some days later, thus it will enable me to revise the course in the process.