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Become a Data Scientist

Launch your career as a data scientist through our cutting edge curriculum, real–world projects, and personalized career support
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Learn R, Python, machine learning and big data in just 12 weeks with our full job support

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Short Courses

Upgrade your data science skills with these in-person targeted courses and earn a certificate

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Learn data science online at your own pace or through our live daily lectures

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Master in-demand skills through industry-proven curriculum and premium learning system

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Customized Training

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.

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Consulting

Expert professional consulting services from data scientists and engineers, building big data solutions and solving data science problems.

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Project Help

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Upcoming Courses and Events

Check your schedule for some in-person data science actions
Jun 26th, 2018
[Partner Event]Peeking into the On-Demand Economy
Event

DetailsWe're excited to partner with ACM NY (www.meetup.com/ACM-NY) for a talk opening up the black box of the on-demand economy by Ming Yin, Post-doctoral Researcher at Microsoft Research. Co-hosted with Dataiku Meetup Group (https://www.meetup.com/Analytics-Data-Science-by-Dataiku-NY) Peeking into the On-Demand Economy:Today, an increasing number of digital and mobile technologies have emerged to match customers, in almost real time, with a potentially global pool of self-employed labor, leading to the rise of the on-demand economy, which has brought about dramatic changes in our society. It creates new business models and new dynamics of labor allocation. It enables new models of computation, that is, human-in-the-loop computing. And it leads to new forms of knowledge creation—people all over the world are contributing to scientific studies in dozens of fields, either by making scientific observations as amateur scientists or by participating in online experiments as subjects. Despite its already significant impacts, the on-demand economy has still been considered as a black-box approach to soliciting labor from a crowd of on-demand workers. Little is known about these workers and their aggregated behavior. In this talk, using the on-demand crowdsourcing platforms as an example, Ming present her attempts and findings on opening up this black box with a combination of experimental and computational approaches, with focuses on understanding who the on-demand workers are, how to model their unique working behavior, and how to improve their work experience. Ming Yin, Postdoctoral Researcher at Microsoft Research:Ming Yin is currently a postdoctoral researcher at Microsoft Research New York City. Starting in Fall 2018, she will join Purdue University as an Assistant Professor in the Department of Computer Science. Ming’s primary research interests lie in the interdisciplinary area of social computing and crowdsourcing. Her research has contributed to better understanding human behavior in social computing and crowdsourcing systems through large-scale online behavior experiments, as well as incorporating the empirical insights from the behavioral data into developing models, algorithms, and interfaces to facilitate the design towards better systems. More broadly, her research connects to the fields of applied artificial intelligence and machine learning, computational social science, human-computer interaction and behavioral economics. Ming’s work is published in top venues like WWW, CHI, AAAI and IJCAI. Ming is named as a Siebel Scholar (Class of 2017), and she has received Best Paper Honorable Mention at the ACM Conference on Human Factors in Computing Systems (CHI’16). Ming obtained her bachelor's degree from Tsinghua University, Beijing, China, in 2011, and completed her PhD at Harvard University in 2017.

6:30PM
RSVP
Jul 2nd, 2018
Data Science Bootcamp: Intensive 12-Week
Course

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.

9:00am-5:00pm
Enroll Now
Jul 21st, 2018
Deep Learning
Course

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.

10:00am-4:00pm
Enroll Now
Jul 28th, 2018
Data Science with R: Data Analysis and Visualization
Course

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.

10:00am-5:00pm
Enroll Now
Aug 6th, 2018
Data Science with Tableau
Course

This course offers an accelerated intensive learning experience with Tableau - the growing standard in business intelligence for data visualization and dashboard creation. Without prior experience, students will learn to work with multiple data sources, create compelling visualizations, and roll out their data science products for continuous, scalable outputs to key stakeholders. By building insight and weaving narrative, students will be empowered to harness data in a striking way that provides value to organizations large and small.

7:00-9:30pm
Enroll Now
Aug 13th, 2018
Introductory Python
Course

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.

7:00-9:30pm
Enroll Now
Aug 14th, 2018
Big Data with Hadoop and Spark
Course

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.

7:00-9:30pm
Enroll Now
Sep 4th, 2018
Machine Learning in Finance
Course

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.

7:00-9:30pm
Enroll Now
Sep 8th, 2018
Data Science with R: Data Analysis and Visualization
Course

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.

10:00am-5:00pm
Enroll Now
Sep 8th, 2018
Data Science with R: Machine Learning
Course

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.

10:00am-5:00pm
Enroll Now
Sep 9th, 2018
Data Science with Python: Data Analysis and Visualization
Course

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.

1:00-5:00pm
Enroll Now
Sep 9th, 2018
Data Science with Python: Machine Learning
Course

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.

1:00-5:00pm
Enroll Now
Sep 24th, 2018
Data Science Bootcamp: Intensive 12-Week
Course

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.

9:00am-5:00pm
Enroll Now
Oct 20th, 2018
Deep Learning
Course

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.

10:00am-4:00pm
Enroll Now
Oct 22nd, 2018
Introductory Python
Course

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.

7:00-9:30pm
Enroll Now
Oct 27th, 2018
Data Science with R: Data Analysis and Visualization
Course

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.

10:00am-5:00pm
Enroll Now
Oct 28th, 2018
Data Science with Python: Machine Learning
Course

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.

1:00-5:00pm
Enroll Now
Oct 28th, 2018
Data Science with Python: Data Analysis and Visualization
Course

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.

1:00-5:00pm
Enroll Now