Array
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[January] => Array
(
[0] => Array
(
[date] => January 15, 2020
[hours] => 6:00 PM – 8:00 PM EDT
[status] => Active
[excerpt] => Join Our Data Scientist & Employer Networking Event
[title] => Data Scientist & Employer Networking Event
[datetime] => 1579046400
[eventtype] => Eventbrite
[url] => https://www.eventbrite.com/e/data-scientist-employer-networking-event-tickets-75229309891?aff=erelexpmlt,
[image] => https://nycdatascience.com/wp-content/uploads/2017/08/network-event.png
)
[1] => Array
(
[utc_offset] => -18000000
[date] => January 21, 2020
[time_adjusted] => 1579669200000
[image] => https://secure.meetupstatic.com/photos/event/1/5/e/c/global_487505612.jpeg
[status] => upcoming
[excerpt] => Join us for an introductory course demo covering the Beginner's Guide to Data Science by NYC Data Science Academy Instructor Drace Zhan. The course demo will discuss the basics of data science tools and resources that you can use when you first introduce yourself to data science. Both, programmers who are looking to expand their data toolkit and non-programmers who do not have any coding experience, are invited to join. To take advantage of the hands-on portions of the lecture, it is recommended that students have installed Anaconda with Python 3.7. This session will also include a brief overview of the NYC Data Science Academy, including its data science bootcamp and other course offerings. What to expect: In the first part of the info session, we will give you an overview of what makes NYC Data Science Academy different. You will learn how to prepare for the Bootcamp, what to learn, and application process. The information session will be as follows: 7:00 - 7:10 pm Introduction to NYC Data Science Academy and What We Do7:10 - 7:45 pm Data Science Tools with Drace Zhan7:45 - 8:00 pm Q&A and Mingling When and Where: Jan 21, 2020, Tuesday | Starts at 7pmNYC Data Science Academy500 8th Avenue, Suite 905, New York, NY 10018 Save your spot: https://info.nycdatascience.com/data-science-tools-21-01-2020
[city] => New York
[title] => Free Course Demo: Beginner's Guide to Data Science
[datetime] => 1579633200
[eventtype] => Event
[url] => https://www.meetup.com/NYC-Open-Data/events/267273360/
)
)
[March] => Array
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[0] => Array
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[id] => 16079
[date] => March 7, 2020
[hours] => 1:00-5:00pm
[status] => Future
[type] => Weekends
[parent] => 6300
[city] =>
[title] => Data Science with Python: Machine Learning
[excerpt] => 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.
[datetime] => 1583539200
[eventtype] => Course
[url] => https://nycdatascience.com/courses/data-science-with-python-machine-learning/
[sku] => a0E3m00000Jlgqy
[image] => https://nycdatascience.com/wp-content/uploads/2017/08/python-machine-learning.png
)
[1] => Array
(
[id] => 16081
[date] => March 7, 2020
[hours] => 1:00-5:00pm
[status] => Future
[type] => Weekends
[parent] => 6292
[city] =>
[title] => Data Science with Python: Data Analysis and Visualization
[excerpt] => 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.
[datetime] => 1583539200
[eventtype] => Course
[url] => https://nycdatascience.com/courses/data-science-with-python-data-analysis/
[sku] => a0E3m00000JlgrN
[image] => https://nycdatascience.com/wp-content/uploads/2017/08/python-data-analysis.png
)
[2] => Array
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[id] => 16077
[date] => March 9, 2020
[hours] => 7:00-9:30pm
[status] => Future
[type] => Workdays
[parent] => 7971
[city] =>
[title] => Introductory Python
[excerpt] => 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.
[datetime] => 1583712000
[eventtype] => Course
[url] => https://nycdatascience.com/courses/introductory-python/
[sku] => a0E3m00000JlgqZ
[image] => https://nycdatascience.com/wp-content/uploads/2017/08/intro-python.png
)
)
[April] => Array
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[0] => Array
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[id] => 16272
[date] => April 6, 2020
[hours] => 9:00am-5:00pm
[status] => Future
[type] => Bootcamp
[parent] => 6287
[city] =>
[title] => Data Science Bootcamp: Intensive 12-Week
[excerpt] => 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.
[datetime] => 1586131200
[eventtype] => Course
[url] => https://nycdatascience.com/courses/data-science-bootcamp-intensive-12-weeks/
[sku] => a0E3m00000JllOi
[image] => https://nycdatascience.com/wp-content/uploads/2017/08/inperson.png
)
[1] => Array
(
[id] => 16091
[date] => April 18, 2020
[hours] => 10:00am-5:00pm
[status] => Future
[type] => Weekends
[parent] => 6296
[city] =>
[title] => Data Science with R: Machine Learning
[excerpt] => 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.
[datetime] => 1587168000
[eventtype] => Course
[url] => https://nycdatascience.com/courses/data-science-with-r-machine-learning/
[sku] => a0E3m00000Jlh9a
[image] => https://nycdatascience.com/wp-content/uploads/2017/08/r-machine-learning.png
)
[2] => Array
(
[id] => 16092
[date] => April 18, 2020
[hours] => 10:00am-5:00pm
[status] => Future
[type] => Weekends
[parent] => 6291
[city] =>
[title] => Data Science with R: Data Analysis and Visualization
[excerpt] => 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.
[datetime] => 1587168000
[eventtype] => Course
[url] => https://nycdatascience.com/courses/data-science-with-r-data-analysis/
[sku] => a0E3m00000Jlh9f
[image] => https://nycdatascience.com/wp-content/uploads/2017/08/rda.png
)
[3] => Array
(
[id] => 16087
[date] => April 19, 2020
[hours] => 1:00-5:00pm
[status] => Future
[type] => Weekends
[parent] => 6300
[city] =>
[title] => Data Science with Python: Machine Learning
[excerpt] => 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.
[datetime] => 1587254400
[eventtype] => Course
[url] => https://nycdatascience.com/courses/data-science-with-python-machine-learning/
[sku] => a0E3m00000Jlh96
[image] => https://nycdatascience.com/wp-content/uploads/2017/08/python-machine-learning.png
)
[4] => Array
(
[id] => 16090
[date] => April 19, 2020
[hours] => 1:00-5:00pm
[status] => Future
[type] => Weekends
[parent] => 6292
[city] =>
[title] => Data Science with Python: Data Analysis and Visualization
[excerpt] => 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.
[datetime] => 1587254400
[eventtype] => Course
[url] => https://nycdatascience.com/courses/data-science-with-python-data-analysis/
[sku] => a0E3m00000Jlh9L
[image] => https://nycdatascience.com/wp-content/uploads/2017/08/python-data-analysis.png
)
[5] => Array
(
[id] => 16085
[date] => April 20, 2020
[hours] => 7:00-9:30pm
[status] => Future
[type] => Workdays
[parent] => 7971
[city] =>
[title] => Introductory Python
[excerpt] => 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.
[datetime] => 1587340800
[eventtype] => Course
[url] => https://nycdatascience.com/courses/introductory-python/
[sku] => a0E3m00000Jlh7S
[image] => https://nycdatascience.com/wp-content/uploads/2017/08/intro-python.png
)
[6] => Array
(
[id] => 16093
[date] => April 21, 2020
[hours] => 7:00-9:30pm
[status] => Future
[type] => Workdays
[parent] => 6685
[city] =>
[title] => Big Data with Amazon Cloud, Hadoop/Spark and Docker
[excerpt] => 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.
[datetime] => 1587427200
[eventtype] => Course
[url] => https://nycdatascience.com/courses/big-data-with-hadoop-and-spark/
[sku] => a0E3m00000Jlh9p
[image] => https://nycdatascience.com/wp-content/uploads/2017/08/big-data.png
)
)
[June] => Array
(
[0] => Array
(
[id] => 16086
[date] => June 2, 2020
[hours] => 7:00-9:30pm
[status] => Future
[type] => Workdays
[parent] => 7971
[city] =>
[title] => Introductory Python
[excerpt] => 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.
[datetime] => 1591056000
[eventtype] => Course
[url] => https://nycdatascience.com/courses/introductory-python/
[sku] => a0E3m00000Jlh7X
[image] => https://nycdatascience.com/wp-content/uploads/2017/08/intro-python.png
)
[1] => Array
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[id] => 16088
[date] => June 13, 2020
[hours] => 1:00-5:00pm
[status] => Future
[type] => Weekends
[parent] => 6300
[city] =>
[title] => Data Science with Python: Machine Learning
[excerpt] => 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.
[datetime] => 1592006400
[eventtype] => Course
[url] => https://nycdatascience.com/courses/data-science-with-python-machine-learning/
[sku] => a0E3m00000Jlh9B
[image] => https://nycdatascience.com/wp-content/uploads/2017/08/python-machine-learning.png
)
[2] => Array
(
[id] => 16089
[date] => June 13, 2020
[hours] => 1:00-5:00pm
[status] => Future
[type] => Weekends
[parent] => 6292
[city] =>
[title] => Data Science with Python: Data Analysis and Visualization
[excerpt] => 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.
[datetime] => 1592006400
[eventtype] => Course
[url] => https://nycdatascience.com/courses/data-science-with-python-data-analysis/
[sku] => a0E3m00000Jlh9V
[image] => https://nycdatascience.com/wp-content/uploads/2017/08/python-data-analysis.png
)
)
)
Data Scientist & Employer Networking Event
Jan 15th, 2020
6:00 PM – 8:00 PM EDT
Join Our Data Scientist & Employer Networking Event
Free Course Demo: Beginner's Guide to Data Science
Jan 21st, 2020
7:00PM
Join us for an introductory course demo covering the Beginner's Guide to Data Science by NYC Data Science Academy Instructor Drace Zhan. The course demo will discuss the basics of data science tools and resources that you can use when you first introduce yourself to data science. Both, programmers who are looking to expand their data toolkit and non-programmers who do not have any coding experience, are invited to join. To take advantage of the hands-on portions of the lecture, it is recommended that students have installed Anaconda with Python 3.7. This session will also include a brief overview of the NYC Data Science Academy, including its data science bootcamp and other course offerings. What to expect: In the first part of the info session, we will give you an overview of what makes NYC Data Science Academy different. You will learn how to prepare for the Bootcamp, what to learn, and application process. The information session will be as follows: 7:00 - 7:10 pm Introduction to NYC Data Science Academy and What We Do7:10 - 7:45 pm Data Science Tools with Drace Zhan7:45 - 8:00 pm Q&A and Mingling When and Where: Jan 21, 2020, Tuesday | Starts at 7pmNYC Data Science Academy500 8th Avenue, Suite 905, New York, NY 10018 Save your spot: https://info.nycdatascience.com/data-science-tools-21-01-2020
Data Science with Python: Machine Learning
Mar 7th, 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
Mar 7th, 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
Mar 9th, 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.
Data Science Bootcamp: Intensive 12-Week
Apr 6th, 2020
Monday through Friday, from 9:00am to 6:00pm
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.
Data Science with R: Machine Learning
Apr 18th, 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
Apr 18th, 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
Apr 19th, 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
Apr 19th, 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
Apr 20th, 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.
Big Data with Amazon Cloud, Hadoop/Spark and Docker
Apr 21st, 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. 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.
Introductory Python
Jun 2nd, 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.
Data Science with Python: Machine Learning
Jun 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
Jun 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.
