Become a Data Scientist

This course is a dense presentation of machine learning (ML) tools used in financial risk management, portfolio management, and trading. Ten classes are offered: two on risk management, two on loan portfolio management, three on portfolio optimization, and three on high-frequency trading. The risk classes cover the risk measurement of financial assets using distribution fitting, copulas, PCA, and splines. The loan portfolio management classes cover risk estimation and backtesting using logistic regression, regularization, clustering methods, and the applied statistics concepts such as parameter and process risk. Kaggle competitions for loan portfolios which used tree-based algorithms for predictions are also reviewed. The classes on portfolio optimization introduce classic theories for asset return estimation and their extensions (multi-factor models) while using unsupervised & supervised ML methods to verify & derive new factors; modern portfolio theory using constrained optimization & robust methods; and Black-Litterman model portfolios where asset-specific, ML-derived models are integrated. The classes on trading introduce the limit order book and market microstructure and then move on to tour the winning strategies of to Kaggle competitions on trading. The feature engineering and code of the winning solutions are reviewed in depth.

Hosted by NYC Data Science Academy (nycdatascience.com). The talk will serve as an overview of Jon Krohn's upcoming Deep Learning course (http://nycdatascience.com/courses/deep-learning/) at the NYC Data Science Academy, running from October 20th to December 1st. We’re now offering 30% tuition deduction for all women who participate in this Deep Learning course. Use code “DLFORWOMEN” at checkout. Deep Learning algorithms have become unprecedentedly transformative across industry verticals and are now the state-of-the-art across applications as diverse as image classification, natural language processing, generation of visual art, and game-playing. Almost unknown a few years ago, Deep Learning today powers countless everyday services from Tesla's Autopilot to Siri's voice recognition, and from Google Inbox's suggested replies to superhuman ability at the elaborate boardgames. Via analogy to biological neurons and the human brain, this talk is an introduction to Deep Learning. It features interactive demos and example code from the leading open-source Deep Learning library, TensorFlow, and its high-level API, Keras. Agenda:6:00 - 6:30 pm - Food, Drinks & Mingling6:30 - 6:40 pm - Edward Weiss, a student on the previous Deep Learning class cohort, will present on his capstone project and his experience taking the course6:40 - 7:30 pm - Course Demo by Jon Krohn7:30-8:00 pm - Q&A & Mingling Speaker: Jon Krohn (https://www.jonkrohn.com/) is Chief Data Scientist at the machine-learning startup untapt. He presents an acclaimed series of tutorials on artificial neural networks, including Deep Learning with TensorFlow LiveLessons (https://www.safaribooksonline.com/library/view/deep-learning-with/9780134770826/). Jon holds a doctorate in neuroscience from Oxford University and has been publishing on machine learning in leading academic journals since 2010. His forthcoming book, Deep Learning Illustrated, is being published on Pearson's Addison-Wesley imprint and will be distributed in 2018. About NYC Data Science Academy: Founded in 2014, the NYC Data Science Academy offers the highest quality in data science and data engineering training. Their top-rated and comprehensive curriculum has been developed by industry pioneers using experience from consulting, corporate and individual training and is approved and licensed by the NYS Department of Education. NYC Data Science Academy offers a variety of services including full-time bootcamps, part-time courses, corporate training, consulting, and career services. For more information visit http://nycdatascience.com (http://nycdatascience.com/)

In this introductory BlockChain course, we offer a bird's-eye view on the BlockChain technology, starting from the high level walk-through of BitCoin/Ethereum and end at some more technical aspects of manipulating Ethereum data. We will cover the computer science basics on hashing, symmetric/public-private-key encryption, Merkle-Patricia trees, IP address/port number, IPC vs RPC, p2p protocols, working with Ethereum node executables, solidity and smart contract compilation, python data analysis with Ethereum data, and finally discussing Swarm/Whisper/Oraclize/Ethereum Bridge, which allows Ethereum to interact with the outside world.

Via analogy to biological neurons and human perception, this course is an introduction to artificial neural networks that brings high-level theory to life with interactive labs featuring TensorFlow, the most popular open-source Deep Learning library. Essential theory will be covered in a manner that provides students with an intuitive understanding of Deep Learning's underlying foundations. Paired with hands-on code run-throughs in Jupyter notebooks as well as strategies for overcoming common pitfalls, this foundational knowledge will empower individuals with no previous understanding of neural networks to build production-ready Deep Learning applications across the major contemporary families: Convolutional Nets for machine vision; Long Short-Term Memory Recurrent Nets for natural language processing and time series analysis; Generative Adversarial Networks for producing realistic images; and Reinforcement Learning for playing video games.

This is a class for computer-literate people with no programming background who wish to learn basic Python programming. The course is aimed at those who want to learn “data wrangling” – manipulating downloaded files to make them amenable to analysis. We concentrate on language basics such as list and string manipulation, control structures, simple data analysis packages, and introduce modules for downloading data from the web.

This 35-hour Machine Learning with R course introduces both the theoretical foundation of machine learning algorithms as well as their practical applications in R. It will introduce you to data mining, performance measures and dimension reduction, regression models, both linear and generalized, KNN and Naïve Bayes models, tree models, and SVMs as well as the Association Rule for analysis. After successfully completing of this course, you will be able to break down the mathematics behind major machine learning algorithms, explain the principles of machine learning algorithms, and implement these methods to solve real-world problems.

This course is a 35-hour program designed to provide a comprehensive introduction to R. You’ll learn how to load, save, and transform data as well as how to write functions, generate graphs, and fit basic statistical models with data. In addition to a theoretical framework in which you will learn the process of data analysis, this course focuses on the practical tools needed in data analysis and visualization. By the end of the course, you will have mastered the essential skills of processing, manipulating and analyzing data of various types, creating advanced visualizations, generating reports, and documenting your codes.

This 20-hour Machine Learning with Python course covers all the basic machine learning methods and Python modules (especially Scikit-Learn) for implementing them. The five sessions cover: simple and multiple Linear regressions; classification methods including logistic regression, discriminant analysis and naive bayes, support vector machines (SVMs) and tree based methods; cross-validation and feature selection; regularization; principal component analysis (PCA) and clustering algorithms. After successfully completing of this course, you will be able to explain the principles of machine learning algorithms and implement these methods to analyze complex datasets and make predictions in Python.

This class is a comprehensive introduction to data science with Python programming language. This class targets people who have some basic knowledge of programming and want to take it to the next level. It introduces how to work with different data structures in Python and covers the most popular data analytics and visualization modules, including numpy, scipy, pandas, matplotlib, and seaborn. We use Ipython notebook to demonstrate the results of codes and change codes interactively throughout the class.

NYC Data Science Academy. a full-time 12-week immersive program, offers the highest quality in data science training. It’s designed specifically around the skills employers are seeking, including R, Python, Machine Learning, Hadoop, Spark, github, SQL, and much more.