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NYC Data Science Academy is the premier training ground providing premium, accelerated training in data science through immersive bootcamps and courses.

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Full-time 16 weeks

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Full-time 12 weeks

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4 - 6 weeks

Python and R courses at different levels
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Our graduates have undergone a rigorous, collaborative training experience as they prepare to join leading companies as data science professionals. Join our hiring partner network and let us help you grow your team with the right talent.

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Join Our Upcoming Courses and Events

Introduction to Data Science for Strategic Management Consulting Workshop

Aug 8th, 2020

1:00PM

What is management consulting, and what does it entail really from a data science perspective? Most people consider management consulting to be a non-quantitative industry with an emphasis on business acumen and getting an MBA. Many management consulting firms are heavily investing in building out digital and technical solutions to help clients reap the benefits of data and automation. In this workshop, former McKinsey data scientist Joseph Lee we will cover what to realistically expect in a data science career at a top management consulting firm. Moreover, this workshop will also focus on the foundational technical, business, financial, and communication skills needed from any client-facing data scientist. Key takeaways - Importance of communication and relationship management- Overview and current landscape of management consulting- Core finance concepts and metrics to know- Management consulting techniques including problem formulation,- MECE framework, GE-McKinsey Matrix, and BCG Growth Matrix Save your spot here: https://www.eventbrite.com/e/foundations-in-data-science-for-management-consulting-workshop-series-tickets-110682149288?discount=nycopendata10 Exclusive to members of this group: Apply the code nycopendata10 to save 10% on your workshop tickets.

Introductory Python

Aug 10th, 2020

7:00-9:30pm

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.

Building a Career in Data Science

Aug 12th, 2020

6:00PM

Join us on Wednesday, August 12, and hear from bootcamp alumnus Raul Vallejo as he shares first-hand experiences on how he built a career in data science and what it’s like to work in the field today. He will share pieces of advice and valuable lessons pulled from personal experience while learning data science and working both independently and within a larger team of data scientists. This webinar is perfect for individuals interested in building a career in data science. Save your spot today - https://info.nycdatascience.com/building-a-career-in-data-science?utm_source=meetup&utm_medium=referral&utm_campaign=august_2020&utm_content=building-a-career-in-data-science

Big Data with Amazon Cloud, Hadoop/Spark and Docker

Aug 25th, 2020

7:00-9:30pm

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.

Data Science with R: Machine Learning

Sep 12th, 2020

10:00am-5:00pm

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.

Data Science with R: Data Analysis and Visualization

Sep 12th, 2020

10:00am-5:00pm

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.

Data Science with Python: Machine Learning

Sep 13th, 2020

1:00-5:00pm

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.

Data Science with Python: Data Analysis and Visualization

Sep 13th, 2020

1:00-5:00pm

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.

Introductory Python

Sep 15th, 2020

7:00-9:00pm

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.

Big Data with Amazon Cloud, Hadoop/Spark and Docker

Oct 27th, 2020

7:00-9:30pm

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.

Data Science with R: Machine Learning

Oct 31st, 2020

10:00am-5:00pm

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.

Data Science with R: Data Analysis and Visualization

Oct 31st, 2020

10:00am-5:00pm

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.

Data Science with Python: Machine Learning

Nov 1st, 2020

1:00-5:00pm

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.

Data Science with Python: Data Analysis and Visualization

Nov 1st, 2020

1:00-5:00pm

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.

Introductory Python

Nov 3rd, 2020

7:00-9:00pm

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.

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Introduction to Data Science for Strategic Management Consulting Workshop

Aug 8th, 2020

1:00PM

What is management consulting, and what does it entail really from a data science perspective? Most people consider management consulting to be a non-quantitative industry with an emphasis on business acumen and getting an MBA. Many management consulting firms are heavily investing in building out digital and technical solutions to help clients reap the benefits of data and automation. In this workshop, former McKinsey data scientist Joseph Lee we will cover what to realistically expect in a data science career at a top management consulting firm. Moreover, this workshop will also focus on the foundational technical, business, financial, and communication skills needed from any client-facing data scientist. Key takeaways - Importance of communication and relationship management- Overview and current landscape of management consulting- Core finance concepts and metrics to know- Management consulting techniques including problem formulation,- MECE framework, GE-McKinsey Matrix, and BCG Growth Matrix Save your spot here: https://www.eventbrite.com/e/foundations-in-data-science-for-management-consulting-workshop-series-tickets-110682149288?discount=nycopendata10 Exclusive to members of this group: Apply the code nycopendata10 to save 10% on your workshop tickets.

Introductory Python

Aug 10th, 2020

7:00-9:30pm

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.

Building a Career in Data Science

Aug 12th, 2020

6:00PM

Join us on Wednesday, August 12, and hear from bootcamp alumnus Raul Vallejo as he shares first-hand experiences on how he built a career in data science and what it’s like to work in the field today. He will share pieces of advice and valuable lessons pulled from personal experience while learning data science and working both independently and within a larger team of data scientists. This webinar is perfect for individuals interested in building a career in data science. Save your spot today - https://info.nycdatascience.com/building-a-career-in-data-science?utm_source=meetup&utm_medium=referral&utm_campaign=august_2020&utm_content=building-a-career-in-data-science

Big Data with Amazon Cloud, Hadoop/Spark and Docker

Aug 25th, 2020

7:00-9:30pm

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.

Data Science with R: Machine Learning

Sep 12th, 2020

10:00am-5:00pm

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.

Data Science with R: Data Analysis and Visualization

Sep 12th, 2020

10:00am-5:00pm

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.

Data Science with Python: Machine Learning

Sep 13th, 2020

1:00-5:00pm

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.

Data Science with Python: Data Analysis and Visualization

Sep 13th, 2020

1:00-5:00pm

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.

Introductory Python

Sep 15th, 2020

7:00-9:00pm

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.

Big Data with Amazon Cloud, Hadoop/Spark and Docker

Oct 27th, 2020

7:00-9:30pm

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.

Data Science with R: Machine Learning

Oct 31st, 2020

10:00am-5:00pm

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.

Data Science with R: Data Analysis and Visualization

Oct 31st, 2020

10:00am-5:00pm

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.

Data Science with Python: Machine Learning

Nov 1st, 2020

1:00-5:00pm

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.

Data Science with Python: Data Analysis and Visualization

Nov 1st, 2020

1:00-5:00pm

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.

Introductory Python

Nov 3rd, 2020

7:00-9:00pm

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.

August

2020

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