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Introduction to Business Analytics
Digital Transformation
Reference Books
Reference Books
Common traps in decision making: There are some common traps that we can fall in while making decisions. The purpose of this post is to introduce those traps and suggest how to avoid them Read more on this free link
Prospect Theory and Loss Aversion: How Users Make Decisions. The prospect theory describes how people choose between different options (or prospects) and how they estimate (many times in a biased or incorrect way) the perceived likelihood of each of these options. Read more on this free link
Predicting the behavioral tendency of loss aversion. Loss aversion manifests itself in rejecting a gamble of gaining or losing the same amount of money with equal chance. Read more on this free link
Differences in supervised vs unsupervised learning. Gives a short clear summary of how both of the machine learning techniques are applied. Further details about the problem faced in each method are showcased Read more on this free link
The Hidden Traps in Decision Making: Bad decisions can often be traced back to the way the decisions were made–the alternatives were not clearly defined, the right information was not collected, the costs and benefits were not accurately weighed. But sometimes the fault lies not in the decision-making process but rather in the mind of the decision-maker. There are some psychological traps that can affect the way we make business decisions. The best way to avoid all the traps is awareness–forewarned is forearmed . Read more on this free link
Risk-Seeking Behavior. As much money and life as you could want! The two things most human beings would choose above all – the trouble is, humans do have a knack of choosing precisely those things that are worst for them. Read more on this free link
How Machine Learning (ML) is Used by Bayer, AES, American Cancer Society, AIMMO, and Road Commission of Western Australia: Case Studies Read more on this free link
Classification Vs. Clustering - A Practical ExplanationClassification and clustering are two methods of pattern identification used in machine learning. Read more on this free link
Should You Focus on an Efficient or Responsive Supply Chain? An efficient vs. responsive supply chain is a common dilemma. Let’s begin with efficiency. There is rarely the case where efficiency is a bad thing. Read more on this free link
Key Applications of Machine Learning in the Fashion Industry. On the heels of the devastating economic effects of the COVID-19 pandemic, the fashion industry is beginning to pick up the pieces and build a path forward. Read more on this free link
The AI that fashion is using to reinvent itself. Retailers have turned to AI to replace photoshoots and predict what people will want to buy and wear in the future Read more on this free link
A Step-by-Step Explanation of Principal Component Analysis (PCA). The purpose of this post is to provide a complete and simplified explanation of Principal Component Analysis (PCA). Read more on this free link
Decoding the impact of AI and machine learning in the fashion industry. Digitization in the fashion industry is continuously transforming the way brands engage with customers Read more on this free link
An Introduction to Product Segmentation Strategy: Definition & Examples. Read more on this free link
Understanding the "Operations Rules": No firm can be both extremely efficient, and thus compete on price, and at the same time highly responsive, and thus provide its customers with a large set of choices in a speedy manner while maintaining an extraordinary service level. These are conflicting objectives, an issue that is discussed in the link Read more on this free link
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