AI Applications for Business Success
Leverage AI for business success: enhance supply chain efficiency and solve critical challenges
$99.00
Lifetime access
What you get:
- 2 hours of content
- World-class instructor
- Closed captions
- Q&A support
- Future course updates
- Course exam
- Certificate of achievement
AI Applications for Business Success
$99.00
Lifetime access
What you get:
- 2 hours of content
- World-class instructor
- Closed captions
- Q&A support
- Future course updates
- Course exam
- Certificate of achievement
$99.00
Lifetime access
$99.00
Lifetime access
What you get:
- 2 hours of content
- World-class instructor
- Closed captions
- Q&A support
- Future course updates
- Course exam
- Certificate of achievement
What You Learn
- Master the SMART goals framework to effectively define and implement specific, measurable, achievable, relevant, and time-bound business objectives
- Perform SWOT analysis to deeply understand business challenges before designing suitable data-driven solutions
- Discover how to use advanced AI techniques to infer causation
- Boost your business acumen by gaining a thorough understanding of supply chain processes and key value drivers
- Master hybrid experiments to evaluate whether AI-proposed solutions generate business value
- Advance your technical data science skills to impress hiring managers and recruiters
Top Choice of Leading Companies Worldwide
Industry leaders and professionals globally rely on this top-rated course to enhance their skills.
Course Description
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.
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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
Interactive Exercises
Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.
Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.
Curriculum
- 2. Business Goals6 Lessons 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 case2 minSWOT Analysis5 minSMART Goals7 minLimitations of the BI Approach4 minCorrelation vs. Causation5 minMaking Recommendations with Descriptive Statistics2 min - 3. Approaches to solving the business objective4 Lessons 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 approaches5 minThe BI Approach2 minState Space and Takens' Theorem4 minShadow Manifolds and K-Nearest Neighbors4 min - 4. Artificial Intelligence in Business12 Lessons 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 Attainability6 minGradient Boosted Machines: Part 16 minGradient Boosted Machines: Part 24 minGradient Boosted Machines: Part 39 minSHAP Values9 minFriedman's H-Statistic7 minLIME9 minWaterfall Charts 15 minWaterfall Charts 25 minCausation: Traditional Statistical Methods4 minCausation: Advanced Statistical Methods6 minTime Series Forecasting with Takens' Theorem8 min - 5. Artificial Intelligence Recommends Metrics3 Lessons 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.
Introduction3 minThe Hybrid Experiment4 minQuantile Difference Tests5 min
Topics
Course Requirements
- You need to complete an introduction to Python before taking this course
- Basic skills in statistics, probability, and linear algebra are required
- It is highly recommended to take the Machine Learning in Python course first
- You will need to install the Anaconda package, which includes Jupyter Notebook
Who Should Take This Course?
Level of difficulty: Advanced
- Aspiring data scientists
- Existing data scientists who want to improve their technical and domain-specific skills
Exams and Certification
A 365 Data Science Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.
Meet Your Instructor
Horia Margarit has a demonstrated history of success in the field of data science. Ever since earning dual Bachelor’s degrees in Cognitive and Computer Science from UC Berkeley and a Master’s degree in Statistics from Stanford University, Horia has managed to embark on an exciting career path involving data scientist roles at companies like ask.com, Intuit, Qubole, Autodesk, and Booster Fuels. With over 10 years of professional experience in the San Francisco Bay Area, he has differentiated himself by applying highly novel methods and approaches to tackling complex business problems. His predictions for business applications of AI have been featured in both CIO Magazine and Forbes. Horia’s primary focus is on technical underpinnings that maximize actual business and customer value.
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