The purpose of the class is to expose undergraduate and graduate students to the mathematical concepts and techniques used in the financial industry. Mathematics lectures are mixed with lectures illustrating the corresponding application in the financial industry. MIT mathematicians teach the mathematics part while industry professionals give the lectures on applications in finance.
Azure Cloud Advocates at Microsoft are pleased to offer a 10-week, 20-lesson curriculum all about Data Science. Each lesson includes pre-lesson and post-lesson quizzes, written instructions to complete the lesson, a solution, and an assignment. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'.
Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 26-lesson curriculum all about Machine Learning. In this curriculum, you will learn about what is sometimes called classic machine learning, using primarily Scikit-learn as a library and avoiding deep learning, which is covered in our forthcoming 'AI for Beginners' curriculum.
A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in 2021 without ANY background in the field and stay up-to-date with the latest news and state-of-the-art techniques
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
A curated list of the latest breakthroughs in AI by release date with a clear video explanation, link to a more in-depth article, and code. [work in progress]
A community for current and aspiring data analytics leaders. Started in NYC in early 2018 as an outgrowth of a slack channel / extremely informal meetup group, we hope to share our thoughts / opinions / experiences / trials / tribulations with others in the community.
Collection of the top articles, videos, events, books and jobs on Machine Learning, Deep Learning, NLP, Computer Vision and other aspects of Data Science.
Main skills required by the data scientists vacancies
The research made by Faculty of Applied Sciences at UCU. Link on main article.
Big Data Software Engineer / Data Engineer
Linear algebra. Calculus. Statistics and Probability Theory.
Descriptive statistics (What distribution does my data follow, what are the modes of the distribution, the expectation, the variance)
Probability theory (Given my data follows a Binomial distribution, what is the probability of observing 5 paying customers in 10 click-through events)
Hypothesis testing (forming the basis of any question on A/B testing, T-tests, anova, chi-squared tests, etc).
Regression (Is the relationship between my variables linear, what are potential sources of bias, what are the assumptions behind the ordinary least squares solution)
Bayesian Inference (What are some advantages/disadvantages vs frequentist methods)
Machine Learning: A Bayesian and Optimization Perspective by Sergios Theodoridis. This is more a machine learning text than a specific primer on applied statistics, but the linear algebra approaches outlined here really help drive home the key statistical concepts on regression.