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Analytical Skills for AI and Data Science: Building Skills for an AI-Driven Enterprise
While several market-leading companies have successfully transformed their business models by following data- and AI-driven paths, the vast majority have yet to reap the benefits. How can your business and analytics units gain a competitive advantage by capturing the full potential of this predictive revolution? This practical guide presents a battle-tested end-to-end method to help you translate business decisions into tractable prescriptive solutions using data and AI as fundamental inputs. Author Daniel Vaughan shows data scientists, analytics practitioners, and others interested in using AI to transform their businesses not only how to ask the right questions but also how to generate value using modern AI technologies and decision-making principles. You’ll explore several use cases common to many enterprises, complete with examples you can apply when working to solve your own issues.
244 pages, Paperback
First published May 1, 2020
32 people are currently reading
99 people want to read
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Displaying 1 - 5 of 5 reviews
December 19, 2021
An interesting book that challenges to think about Data Science beyond a "model building" step. I enjoyed that there were use cases solved in increasing sophistication as the author developed the book. However, the book covers a lot of ground and quite a few parts felt really rushed and shallow. There are quite a few references to relevant sources (books and articles) at the end of the chapters to dive deeper, thouhg.
November 10, 2023
This textbook lives up to its author's expectations
The book is very well laid out and it has lots of use cases.
It touches very well known but important concepts in the industry, like the three Vs (volume, velocity, variety) that has projected the world into the big data era, the role of uncertainty or the difference between correlation and causation, which are important for someone who first approaches analytics, but are a bit redundant for someone already working in this sector.
For me the main takeaway was the clear distinction between the different phases of a business request in the Big Data Era: descriptive, predictive, prescriptive.
It is something that we (analytics) should always keep in mind because it is easy to be carried away from the main mission.
We need to start with the business question in mind, decompose it and move backward until we have some actions that relate to the business objective you want to achieve.
That is why we always need relevant and measurable KPIs.
We need also to identify what is actionable: the problem of choosing levels is one of causality, we want to make decisions that impact our business objectives, so there must be a causal relation from levers to consequences; we need to construct hypothesis and test them.
I often find myself wondering what's the end goal of a request, which forces me to go back to stakeholder and lay down strict and precise requirements as well as the desired outcome.
The author encourages to follow the KISS (Keep it simple, stupid) principle, a revisitation of the Occam's razor: avoid unnecessary complexity and complications. Choose the simplest solution that achieves the desired result.
Every difficult problem can be dissect into simpler and smaller problems.
By starting from those ones we can make educated guesses and rough approximations, estimating probabilities and expected values.
Another important point is how to work with uncertainty.
He introduces the Fermi problems, where the analyst can appreciate the power of intuition based on very few coordinates that help navigates through uncertainty and compute expected utilities.
The prescriptive stage is all about optimisation, which in general is hard. That is why we always want to start by solving the problem with no uncertainty, solving the simpler problem will provide valuable insights intuition as to what the relevant underlying uncertainty is.
He gives some very basic notions about probability and how to cope with uncertainty:
The main takeaway is that value is created by making decisions, not by data or prediction. But these are necessary inputs to make AI-and-data-driven decisions decisions. To create value and make better decisions in a systematic and scalable way we need to improve our analytical skills.
The book is very well laid out and it has lots of use cases.
It touches very well known but important concepts in the industry, like the three Vs (volume, velocity, variety) that has projected the world into the big data era, the role of uncertainty or the difference between correlation and causation, which are important for someone who first approaches analytics, but are a bit redundant for someone already working in this sector.
For me the main takeaway was the clear distinction between the different phases of a business request in the Big Data Era: descriptive, predictive, prescriptive.
It is something that we (analytics) should always keep in mind because it is easy to be carried away from the main mission.
Descriptive Analytics
What it is: involves analyzing historical data to understand what has happened in the past. It focuses on summarizing and presenting data in a meaningful way and relate to the current state of the business objective.
Example: Generating reports, dashboards, or visualizations that show key performance indicators (KPIs) over a specific time period.
Predictive Analytics
What it is: involves using statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. It helps organizations anticipate what might happen in the future.
Example: Building a predictive model to forecast sales for the next quarter based on past sales data and other relevant factors.
Prescriptive Analytics
What it is: goes a step further by recommending actions to optimize or improve future outcomes. It considers the predicted outcomes and suggests the best course of action, to help choose the right levers.
Example: Recommending marketing strategies to maximize the predicted sales, taking into account various influencing factors.
We need to start with the business question in mind, decompose it and move backward until we have some actions that relate to the business objective you want to achieve.
That is why we always need relevant and measurable KPIs.
We need also to identify what is actionable: the problem of choosing levels is one of causality, we want to make decisions that impact our business objectives, so there must be a causal relation from levers to consequences; we need to construct hypothesis and test them.
I often find myself wondering what's the end goal of a request, which forces me to go back to stakeholder and lay down strict and precise requirements as well as the desired outcome.
Business objectives are usually already defined, but we must learn to ask the right business questions to achieve these objectives.
Always start with the business objective and move backward: for any decision you're planning or have already made, think about the business objective you want to achieve. You can then move backward to figure out the set of possible levers and how these create consequences that affect the business.
The sequence of why questions can help define the right business objective you want to achieve: this bottom-up approach generally helps with identifying business objectives and enlarging the set of actions we can take. But other times you can also use a top-down approach similar to the decomposition of conversion rates.
The author encourages to follow the KISS (Keep it simple, stupid) principle, a revisitation of the Occam's razor: avoid unnecessary complexity and complications. Choose the simplest solution that achieves the desired result.
Every difficult problem can be dissect into simpler and smaller problems.
By starting from those ones we can make educated guesses and rough approximations, estimating probabilities and expected values.
Another important point is how to work with uncertainty.
He introduces the Fermi problems, where the analyst can appreciate the power of intuition based on very few coordinates that help navigates through uncertainty and compute expected utilities.
The prescriptive stage is all about optimisation, which in general is hard. That is why we always want to start by solving the problem with no uncertainty, solving the simpler problem will provide valuable insights intuition as to what the relevant underlying uncertainty is.
He gives some very basic notions about probability and how to cope with uncertainty:
Probabilities represent the likelihood of different outcomes occurring. In predictive analytics, estimating probabilities involves using statistical models and data analysis to quantify the chance of various future events.
Expected values, also known as mean or average values, are calculated by multiplying each possible outcome by its probability and summing up these values. It provides a measure of the central tendency of a probability distribution.
Expected utility is a concept in decision theory that combines the probabilities of different outcomes with the associated utility or value of each outcome. It helps in making decisions under uncertainty by considering both likelihood and desirability.
January 27, 2023
I had some difficulties while reading the book and probably because I am not yet at the right level to understand the nuances contained in the content.
"Analytical Skills for AI and Data Science: Building Skills for an Ai-Driven Enterprise" by Daniel Vaughan is an excellent resource for individuals and organizations looking to develop and improve their analytical skills for the age of Artificial Intelligence and Data Science. The book provides a comprehensive overview of the various analytical methods and techniques used in the field, including statistical analysis, data visualization, and machine learning. The author presents the information in a clear and easy-to-understand manner, making it accessible to both beginners and experienced practitioners.
The book is well-organized and easy to navigate, with a table of contents that allows readers to quickly find the information they need. Additionally, the author includes plenty of examples and case studies to illustrate the concepts he covers, making the book even more engaging and helpful. The author also provides a clear explanation of how to build and implement analytical skills within an enterprise, which makes it a valuable resource for organizations looking to develop and improve their analytical capabilities.
Overall, "Analytical Skills for AI and Data Science: Building Skills for an Ai-Driven Enterprise" is a valuable resource for anyone looking to improve their analytical skills in the field of Artificial Intelligence and Data Science. The author's expertise in the field is evident throughout the book, and the wealth of information he provides is sure to be useful for individuals and organizations looking to develop their analytical capabilities. I highly recommend this book to anyone interested in developing analytical skills for the age of Artificial Intelligence and Data Science.
"Analytical Skills for AI and Data Science: Building Skills for an Ai-Driven Enterprise" by Daniel Vaughan is an excellent resource for individuals and organizations looking to develop and improve their analytical skills for the age of Artificial Intelligence and Data Science. The book provides a comprehensive overview of the various analytical methods and techniques used in the field, including statistical analysis, data visualization, and machine learning. The author presents the information in a clear and easy-to-understand manner, making it accessible to both beginners and experienced practitioners.
The book is well-organized and easy to navigate, with a table of contents that allows readers to quickly find the information they need. Additionally, the author includes plenty of examples and case studies to illustrate the concepts he covers, making the book even more engaging and helpful. The author also provides a clear explanation of how to build and implement analytical skills within an enterprise, which makes it a valuable resource for organizations looking to develop and improve their analytical capabilities.
Overall, "Analytical Skills for AI and Data Science: Building Skills for an Ai-Driven Enterprise" is a valuable resource for anyone looking to improve their analytical skills in the field of Artificial Intelligence and Data Science. The author's expertise in the field is evident throughout the book, and the wealth of information he provides is sure to be useful for individuals and organizations looking to develop their analytical capabilities. I highly recommend this book to anyone interested in developing analytical skills for the age of Artificial Intelligence and Data Science.
June 18, 2020
Aimed at beginners. Quite a bit of repetitive content.
Concepts that this book attempts to teach:
-pyramid principle by barbara minto (one example is directly lifted from this book)
-5 whys (in this book it is called - "the sequence of why")
-MECE principle (you would be familiar with this if you are a consultant) i.e breaking down large complex problems into smaller mutually exclusive parts
-framing business problem statements
etc.
Concepts that this book attempts to teach:
-pyramid principle by barbara minto (one example is directly lifted from this book)
-5 whys (in this book it is called - "the sequence of why")
-MECE principle (you would be familiar with this if you are a consultant) i.e breaking down large complex problems into smaller mutually exclusive parts
-framing business problem statements
etc.
December 9, 2020
Extraordinary revision of ground knowledge and skills that managers should have to leverage from new technology and data mining possibilities in order to take better decisions. Very well structured. If you are into Data Science you may as well benefit of a return to basics.
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