Corporate training offerings in R, Python and Big Data, customized for your needs: from high level executive offerings to technical hands-on training in programming and implementation.
Expert professional consulting services from data scientists and engineers, building big data solutions and solving data science problems.
Free service offered by our advanced Bootcamp trainees to solve immediate project needs, from visualization, to drawing insights from data, to predictive modeling.
Join the webinar via http://info.nycdatascience.com/online-info-session A wire connection is highly recommended. Join us on August 23, 7:00 pm for a live online Q&A panel discussion with our alumni and current students of the NYC Data Science Academy bootcamp. Panelists: Lydia Kan, Data Scientist at Publicis, Bootcamp grad (Fall 2016) Rachel Kogan, Recent bootcamp grad (Spring 2017) Sam O'Mullane, Ph.D., Data Scientist at National Grid, Recent bootcamp grad (Spring 2017) Drace Zhan, Student Success Officer at NYCDSA This is a great opportunity for you to have an in-depth look at a day to day experience of the bootcamp as well as what to expect post-graduation from our bootcamp here. Audience members will also be welcome to field questions for our members in bootcamp as well as have questions answered about the admissions process from our Student Success Officer, Drace Zhan. ------------------------------------------ You can also apply to our fall or winter cohorts here. ------------------------------------------ The meeting agenda will be as follows: 6:45 - 7:00 pm - Early check-in, meet, and greet 7:00 - 7:10 pm - Introduction about NYC Data Science Academy and What We Do 7:10 - 7:45 pm - Alumni and Student Panel Forum 7:45 - 8:00 pm - Questions from the Audience See you all there and we wish you the utmost success on your journey to becoming a Data Scientist! About the 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. The program delivers a combination of lectures and real-world data challenges to its students and is designed specifically around the skills employers are seeking, including R, Python, Hadoop, Spark and much more. By the end of the program, students complete at least five real-world data science projects to showcase their knowledge to prospective employers. Students also participate in presentations and job interview training to ensure they are prepared for top data science positions in prestigious organizations. For more information visit http://nycdatascience.com
This class will be an introduction to the statistical programming language R for business analysts. We’ll explore data science use cases in the business realm and use R for data wrangling, data mining, visualization and prediction. Throughout the class we will be approaching business problems analytically and we’ll use R to explore data, make better business decisions and identify areas for improving performance. The combination of data analytics, R and the data science process will provide the foundation for using R for data science business problems. Students should come prepared with an understanding of computer programming and a curiosity for data science.
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.
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 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.
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 that feature the most popular open-source Deep Learning library, TensorFlow, and its high-level API, Keras. 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 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 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.