Last Updated: 7th November 2022
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We are pleased to welcome you into our community. Join us by scanning the QR Code above, to learn more about subjects like data science, economics, DeFi and coding.
Last Updated: 7th November 2022
Bitcoin Pricing Kernels are inferred using a novel data set from Deribit, one of the largest Bitcoin derivatives exchanges. This enables arbitrage-free pricing of various instruments. State Price Densities are estimated with Rookley’s method.
Last Updated: 2nd November 2023
DS2 Data Science & Digital Society
Last Updated: 7th November 2022
Introduction of Kalman filter including the background, calculation methodology and application.
Last Updated: 7th November 2022
Financial risk measure FRM (Financial Risk Meter) is proposed for Emerging markets (FRM@EM).
Last Updated: 7th November 2022
Crypto Hedging strategies based on the paper Hedging Cryptocurrency Options.
Last Updated: 7th November 2022
Investigate the financial market from the perspective of networks.
Last Updated: 7th November 2022
Basic calculations Vectors and matrices Data frames and data IO Plotting
Last Updated: 7th November 2022
Descriptive Techniques Principal Component Analysis
Last Updated: 7th November 2022
Introduction to the Computer Museum
Last Updated: 7th November 2022
QuantLet: A open-source platform hosted on GitHub for demonstrating your research project
Last Updated: 7th November 2022
Statistics of Finance data; Quantitative finance; Forecasting
Last Updated: 17th December 2022
NYCU Final project
Last Updated: 17th December 2022
Do RoBERTa and VADER sentiment models on r/Bitcoin help to explain Bitcoin returns?
Last Updated: 17th December 2022
This study is to predict the closing price of the Taiwan Top 50 Tracker Fund using different machine learning algorithms and compare their performances.
Last Updated: 17th December 2022
Sentiment Analysis of Putin's Speeches about Russo-Ukrainian War
Last Updated: 21st February 2024
Introduction to the foundations of Data Science with focus on business applications. We emphasize supervised machine learning algorithms to build predictive decision support models for credit risk, marketing analytics, and several other use cases.
Last Updated: 28th March 2024
Introduction to the foundations of Data Science with focus on business applications. We emphasize supervised machine learning algorithms to build predictive decision support models for credit risk, marketing analytics, and several other use cases.
Last Updated: 10th March 2024
Introduction to the foundations of Data Science with focus on business applications. We emphasize supervised machine learning algorithms to build predictive decision support models for credit risk, marketing analytics, and several other use cases.
Last Updated: 28th March 2024
Introduction to the foundations of Data Science with focus on business applications. We emphasize supervised machine learning algorithms to build predictive decision support models for credit risk, marketing analytics, and several other use cases.
Last Updated: 19th March 2024
Introduction to the foundations of Data Science with focus on business applications. We emphasize supervised machine learning algorithms to build predictive decision support models for credit risk, marketing analytics, and several other use cases.
Last Updated: 16th January 2023
Variational Bayes is designed to approximate posterior densities arising in Bayesian inference and machine learning
Last Updated: 6th September 2023
In this talk, we introduce a Bayesian evaluation framework that leverages unlabeled data to facilitate more accurate predictions of the performance of a credit scorecard when put into production.
Last Updated: 18th October 2023
Bachelor-level lecture aimed to strike a balance between teaching students how to code in Python and conveying the foundations of modern data analysis.
Last Updated: 15th March 2024
Sobol indices and Shapley values with missing values
Last Updated: 7th April 2024
Video to the Paper Nonparametric Variance Estimation with MAR missing values in the predictor
The DEDA course on Digital Economy & Decision Analytics presents modern machine learning tools for tomorrow's data. Based on a solid founding of Python applications, DEDA evolves into a powerful decision tool for navigating through heterogenous and massively unstructured data. The quantlet.com technology enables transparency and reproducibility in all scales.
Quantinar provides a perfect intro into Machine Learning & Fintech. It equips students with the basic ability to analyze data using Python. The lectures and videos are super cool, understandable and really attractive. For the first time, it made me realize that data science could be very interesting. In the first class, I drew a cute little PY elephant.
I also use edX, udemy and Coursera. I am extremely happy with the quality of the content and its practical applications. This is a very good initiative. I hope that more courses would follow this style.
The absence of transparency and reproducibility of scientific research are the root of a credibility crisis. Quantinar.com is an initiative that that offers brilliant insights into data science, Machine Learning, and up to date FinTech tools without losing the solid grip on mathematical and statistical foundations. Every student or teacher should like to become a Quantinar!
I used Quantinar in order to acces the DEDA courses. I can tell about me that i am a beginner regarding python but the information was well structured and came in handy to support the work done at the Research Methods course at my university.
Quantinar is an excellent platform for those looking for an intro to Machine Learning and concepts connected with Fintech. The lectures and videos have a good structure that are understandable and are created so that they are appealing also to those non-technical students.
Quantinar is an excellent platform for those looking for an intro to Machine Learning and concepts connected with Fintech. The lectures and videos have a good structure that are understandable and are created so that they are appealing also to those non-technical students.