Prof. Manoel GadiYou are visiting the webpage of prof. Manoel Gadi. This web is dedicated to teaching of Big Data and Analytics applied to Risk Managment, Finance and Banking.
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About Prof. Manoel Gadi
Executive with MBA from IE Business School. More than 13 years in Banking and Financial Institutions, most of them in Risk Management, and large experience in Analytics, both in its statistical and machine learning forms.
Passionate about using and teaching the power of Data Crushing and Analytics. Hands on approach with preferred language being Python (NumPy/SciPy/Pandas/Flask).
In this article we will discuss how companies blend Analytics as part of their competitive advantage to become much stronger players.
We introduce a new angle for looking into companies that have mastered Analytics by separating them into three groups: companies with Analytics as core competitive advantage; companies who were able to blend Analytics with their previous competitive advantage and look inside Startups that are committed to Analytics.
The need for this new angle becomes evident when we look at the implementation of Analytics. First, the motivation for implementing Analytics varies largely and second, different challenges lead to specific road maps. We close this session and the article with a discussion on why, for the majority of Startups, Analytics cannot be the sole competitive advantage but the main driver for pivoting.
Wecome to the Financial Analytics course's Big Data y Business Analytics model challenge website. Are you ready for the challenge of delivering the best model (when assessed on the out-of-time sample)?
This web-service gives you two ways of competing:
(1) Do the execise programming, please use this python 3 code and the API call you can find in the code to upload predictions.
(2) Download the development sample and the out-of-time sample datasets and use the software tool of your choice. Next, upload your predictions using the buttons below.
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Method 2: Upload CSV with your predictions
Remember your CSV file must contain 2968 rows and id and pred fields only.By clicking to upload back the out-of-time sample (in csv format) containing fields id [ORIGINAL ID FROM o0.csv], pred [INTEGER OR FLOAT with your prediction/score representing your ranking], it will be upload and it will be assessed against the actual performance of the out-of-time sample (out-of-time is the year following the development period). All submissions will be ranked according to the KS2 of the prediction in the out-of-time sample.
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Below is the ranking of the best submissions so far:
FRAUD WARRIORS GAME is all about applying Analytics for Fraud Detection.
Do you think you have what it takes to fight fraud? Are you interested in participating in the revolution that Big Data is doing in fraud detection and in the financial sector as a whole?
So, this starting game is is probably for you! Among all problems that will see during the full Business Analytics and Big Data Msc, in this game we will tackle a very hard one. We will apply hands on Analytics to identify fraudster.
By the way, as it happens in my classes during the MSc - It is a competition! So I welcome you to the battlefield!
Wecome to the Financial Analytics course's Big Data y Business Analytics model challenge website. Are you ready for the challenge of delivering the best model (when assessed on the out-of-time sample)?
(2) Download the development sample and the out-of-time sample datasets and use the software tool of your choice. Next, upload your predictions using the buttons below.
- * - * - * -
Method 2: Upload CSV with your predictions
Remember your CSV file must contain 2968 rows and id and pred fields only.By clicking to upload back the out-of-time sample (in csv format) containing fields id [ORIGINAL ID FROM o0.csv], pred [INTEGER OR FLOAT with your prediction/score representing your ranking], it will be upload and it will be assessed against the actual performance of the out-of-time sample (out-of-time is the year following the development period). All submissions will be ranked according to the KS2 of the prediction in the out-of-time sample.
- * - * - * -
Below is the ranking of the best submissions so far:
Please evaluate all items, give any grade to your own group item please. You get 1 participation point for each assessed item, including your own, this is to to make sure you have reviewed all items. Do not worry, your grade for your own item will not count for final grade.
