Become a Data Scientist

The talk will serve as an overview of Jon Krohn's upcoming Deep Learning course (http://nycdatascience.com/courses/deep-learning/) at NYC Data Science Academy, running from October 19th to December 7th. It will conclude with a book-signing featuring Jon's newly-published book, Deep Learning Illustrated - 5 copies will be given away to attendees. 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 content, and game-playing. Almost unknown a few years ago, Deep Learning today powers countless everyday services from Tesla's Autopilot to Amazon Alexa’s voice recognition, and from Google Translate to superhuman ability at elaborate boardgames. Via analogy to biological neurons and the human brain, this talk is a highly visual introduction to Deep Learning. It features interactive demos and example code from all three of the leading open-source Deep Learning libraries: TensorFlow, Keras, and PyTorch. Agenda:6:00 - 6:30 pm - Food, Drinks and Mingling6:30 - 6:40 pm - Hank, 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 and Mingling Speaker:Jon Krohn (https://www.jonkrohn.com/) is Chief Data Scientist at the machine learning company untapt. He is the presenter of a popular series of tutorials on artificial neural networks, including (https://learning.oreilly.com/videos/deep-learning-with/9780134770826), and is the author of Deep Learning Illustrated (https://deeplearningillustrated.com), the acclaimed book released by Pearson in 2019. Jon holds a doctorate in neuroscience from Oxford University and has been publishing on machine learning in leading academic journals since 2010. He guest lectures at Columbia University and, along with researchers from the university's Irving Medical Center, holds a National Institutes of Health grant to automate medical image processing with deep learning.

This course is an introduction to artificial neural networks that brings high-level theory to life with interactive labs featuring TensorFlow, Keras, and PyTorch -- the leading Deep Learning libraries. Essential theory will be covered in a manner that provides students with a complete intuitive understanding of Deep Learning’s underlying foundations. Paired with hands-on code run-throughs in Jupyter notebooks as well as strategic advice 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 all of the contemporary families, including: Convolutional Networks for machine vision Long Short-Term Memory Recurrent Nets for natural language processing and time series analysis Generative Adversarial Networks for producing jaw-dropping synthetic data Reinforcement Learning for complex sequential decision-making

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.

In this technical deep dive, Viktor Gamov will go through challenges and pitfalls of managing Kafka on Kubernetes as well as the goals and lessons learned from the development of the Confluent Operator for Kubernetes. Abstract:When it comes to choosing a distributed streaming platform for real-time data pipelines, everyone knows the answer: Apache Kafka! And when it comes to deploying applications at scale without needing to integrate different pieces of infrastructure yourself, the answer nowadays is increasingly Kubernetes. However, with all great things, the devil is truly in the details. While Kubernetes does provide all the building blocks that are needed, a lot of thought is required to truly create an enterprise-grade Kafka platform that can be used in production. Agenda6:45 - 7:00 pm - Food, Drinks, and Mingling7:00 - 8:00 pm - Talk by Viktor Gamov8:00 pm - 8:15 pm - Q&A and Networking. Speaker Bio:Viktor Gamov is a Developer Advocate at Confluent, the company that makes a streaming platform based on Apache Kafka. Working in the field, Viktor Gamov developed comprehensive expertise in building enterprise application architectures using open source technologies. He enjoys helping different organizations design and develop low latency, scalable and highly available distributed systems. He is a professional conference speaker on distributed systems, streaming data, JVM and DevOps topics, and is regular on events including JavaOne, Devoxx, OSCON, QCon, and others. He co-authored O’Reilly’s 'Enterprise Web Development'.

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.

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 is a 6-week evening program providing a hands-on introduction to the Hadoop and Spark ecosystem of Big Data technologies. The course will cover these key components of Apache Hadoop: HDFS, MapReduce with streaming, Hive, and Spark. Programming will be done in Python. The course will begin with a review of Python concepts needed for our examples. The course format is interactive. Students will need to bring laptops to class. We will do our work on AWS (Amazon Web Services); instructions will be provided ahead of time on how to connect to AWS and obtain an account.

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 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 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.