Last Updated: 7th November 2022
FEATURED CATEGORIES
Machine Learning
Digital Economy
Data Science
Cryptocurrency
Fintech
Blockchain
Explainable AI
Maths & Stats
XploRe THE QUANTINAR UNIVERSE
FEATURED COURSES
Last Updated: 7th November 2022
Pricing Kernels and Risk Premia
- Components: 2
- Free
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
- Components: 2
- Free
DS2 Data Science & Digital Society
Last Updated: 7th November 2022
Kalman Filter
- Components: 2
- Free
Introduction of Kalman filter including the background, calculation methodology and application.
Last Updated: 7th November 2022
Financial Risk Meter for Emerging Markets
- Components: 2
- Free
Financial risk measure FRM (Financial Risk Meter) is proposed for Emerging markets (FRM@EM).
Last Updated: 7th November 2022
Hedging Cryptocurrency Options
- Components: 2
- Free
Crypto Hedging strategies based on the paper Hedging Cryptocurrency Options.
Last Updated: 7th November 2022
Network Analysis on Financial Market
- Components: 2
- Free
Investigate the financial market from the perspective of networks.
Last Updated: 7th November 2022
Statistics with R
- Components: 4
- Free
Basic calculations Vectors and matrices Data frames and data IO Plotting
Last Updated: 7th November 2022
Multivariate Statistical Analysis
- Components: 2
- Free
Descriptive Techniques Principal Component Analysis
Last Updated: 7th November 2022
Computer Museum
- Components: 2
- Free
Introduction to the Computer Museum
Last Updated: 7th November 2022
QuantLet
- Components: 2
- Free
QuantLet: A open-source platform hosted on GitHub for demonstrating your research project
Last Updated: 7th November 2022
Statistics of Financial Markets
- Components: 0
- Free
Statistics of Finance data; Quantitative finance; Forecasting
Last Updated: 17th December 2022
DEDA_2022NYCU_Fraud Detection
- Components: 2
- Free
NYCU Final project
Last Updated: 17th December 2022
Reddit Sentiment BTC Returns
- Components: 3
- Free
Do RoBERTa and VADER sentiment models on r/Bitcoin help to explain Bitcoin returns?
Last Updated: 17th December 2022
Using Machine Learning to Predict The Price of Taiwan Top 50 Tracker Fund
- Components: 2
- Free
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 - Putin's Speeches about the war
- Components: 2
- Free
Sentiment Analysis of Putin's Speeches about Russo-Ukrainian War
Last Updated: 21st February 2024
Welcome & Introduction
- Components: 3
- Free
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
Foundations of Descriptive Analytics
- Components: 3
- Free
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
Foundations of Predictive Analytics
- Components: 2
- Free
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
Explanatory Data Analysis and Data Preparation - Part 1
- Components: 2
- Free
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
Explanatory Data Analysis and Data Preparation - Part 2
- Components: 1
- Free
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
- Components: 2
- Free
Variational Bayes is designed to approximate posterior densities arising in Bayesian inference and machine learning
Last Updated: 16th January 2023
Advanced Mathematics - Sets, Relations, Preferences
- Components: 6
- Free
This background course on mathematics aims to provide fundamental mathematical knowledge essential for advanced economic analysis. Although open to all master students, it is specifically tailored to those wishing to directly pursue the advanced Y-track of courses. Therefore in content and form, this intensive course is intended to deliver methods beyond refreshing advanced calculus and linear algebra. The course solely deals with deterministic mathematics. For some theorems formally rigorous proofs are presented in order to make participants more comfortable with - and ideally to provide some intuition for – constructing and understanding of mathematical proofs. Throughout the course proper use of notation will be stressed. Topics presented in class constitute the minimal required program given the above aim, and the maximal feasible program given time.
Last Updated: 16th January 2023
Advanced Mathematics - Vector Spaces and Linear Algebra
- Components: 6
- Free
This background course on mathematics aims to provide fundamental mathematical knowledge essential for advanced economic analysis. Although open to all master students, it is specifically tailored to those wishing to directly pursue the advanced Y-track of courses. Therefore in content and form, this intensive course is intended to deliver methods beyond refreshing advanced calculus and linear algebra. The course solely deals with deterministic mathematics. For some theorems formally rigorous proofs are presented in order to make participants more comfortable with - and ideally to provide some intuition for – constructing and understanding of mathematical proofs. Throughout the course proper use of notation will be stressed. Topics presented in class constitute the minimal required program given the above aim, and the maximal feasible program given time.
Last Updated: 16th January 2023
Advanced Mathematics - Topology and Convex Optimization
- Components: 5
- Free
This background course on mathematics aims to provide fundamental mathematical knowledge essential for advanced economic analysis. Although open to all master students, it is specifically tailored to those wishing to directly pursue the advanced Y-track of courses. Therefore in content and form, this intensive course is intended to deliver methods beyond refreshing advanced calculus and linear algebra. The course solely deals with deterministic mathematics. For some theorems formally rigorous proofs are presented in order to make participants more comfortable with - and ideally to provide some intuition for – constructing and understanding of mathematical proofs. Throughout the course proper use of notation will be stressed. Topics presented in class constitute the minimal required program given the above aim, and the maximal feasible program given time.
Last Updated: 16th January 2023
Advanced Mathematics - Differential calculus
- Components: 3
- Free
This background course on mathematics aims to provide fundamental mathematical knowledge essential for advanced economic analysis. Although open to all master students, it is specifically tailored to those wishing to directly pursue the advanced Y-track of courses. Therefore in content and form, this intensive course is intended to deliver methods beyond refreshing advanced calculus and linear algebra. The course solely deals with deterministic mathematics. For some theorems formally rigorous proofs are presented in order to make participants more comfortable with - and ideally to provide some intuition for – constructing and understanding of mathematical proofs. Throughout the course proper use of notation will be stressed. Topics presented in class constitute the minimal required program given the above aim, and the maximal feasible program given time.
Last Updated: 1st November 2023
Bahadur, Edgeworth, Cornish-Fisher expansions
- Components: 2
- Free
In this course, we will study three important asymptotic expansions that improve upon the normal distribution approximation provided by the central limit theorem - the Bahadur, Edgeworth, and Cornish-Fisher expansions. A key focus will be using these expansions for estimating quantiles, which are widely used on risk measures.
Last Updated: 6th September 2023
Sampling Bias and Scorecard Evaluation
- Components: 2
- Free
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
Introduction to Python Programming for Machine Learning & AI
- Components: 0
- Free
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
Variable Importance with missing values
- Components: 2
- Free
Sobol indices and Shapley values with missing values
Last Updated: 7th April 2024
Nonparametric Variance Estimation with MAR Missing Values in the Predictor
- Components: 2
- Free
Video to the Paper Nonparametric Variance Estimation with MAR missing values in the predictor
Last Updated: 9th April 2024
Expectation Maximization (EM) Algorithm
- Components: 2
- Free
The Expectation Maximization (EM) Algorithm is a statistical technique for finding maximum likelihood estimates of parameters in probabilistic models, especially when the data is incomplete or has missing values.
WHAT WE HAVE TO SAY
Georg Keilbar, Vienna U
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.
Sheila Xiaohe XU, Fudan U
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.
Rupesh Poudel, HU Berlin
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.
Matthias Fengler, St Gallen U
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!
Teodor Nicolau
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.
Vasile Strat, ASE
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.
Patrick Woitschig
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.
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