Become a Data Scientist

We have introduced a Summer Data Science Bootcamp that starts on July 1, 2019. In this 12-week program, students will learn beginner and intermediate levels of Data Science with R, Python, Spark, and Hadoop as well as widely used industry tools such as Selenium, Caret, Tensorflow, MongoDB, AWS, and more. Students are expected to complete four projects that demonstrate industry experience as well as pass coding challenge exams and a final consisting of machine learning theory. Along the way, students will have assistance in preparing for the job search through resume review, interview preparation, and opportunities to interview with our hiring partners. Our bootcamp is proud to be the only bootcamp to offer both R and Python. Our curriculum is focused on practical real data science experience with a heavy emphasis on machine learning algorithms, coding expertise, and database query. Topics are taught by specialists in the field rather than a general instructor for an entire semester. The bootcamp will encompass over four hundred hours of curriculum content including three hours of daily lectures in the morning as well as afternoon jump start sessions of widely used industry tools such as Selenium, Caret, Tensorflow, AWS, etc. Enjoy 1-on-1 career support and access to all amazing job assistance resources. Apply Now: https://nycdatascience.com/data-science-bootcamp/

Join our live online info session about how to start a career in data science! This event is to help you with important information regarding NYCDSA and how it has assisted many in achieving their data science goals at companies such as Facebook, Spotify, Google, JP Morgan, and more! We know what it takes to succeed in this field. Throughout the session, you'll learn the tools, techniques, and fundamental concepts you need to know to make immediate impacts with data.We will do a course overview and expected outcomes, and cover the admission process as well as career services.What you will learn from this session: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 second part of the information session will be a 'Kaggle Workshop' presentation by our instructor Zeyu Zhang. He will be talking about the different ways to hack Kaggle Competitions with Google. Students are not required to have a background in coding to apply for the program. A laptop with a working installation of R version > 3.30 is recommended for the bootcamp. The information session will be as follows:6:45 - 7:00 pm - Check-in and introduction7:00 - 7:10 pm - Introduction to NYC Data Science Academy and What We Do7:10 - 7:45 pm - Kaggle Workshop7:45 - 8:00 pm - Q&A RSVP to receive call details: https://info.nycdatascience.com/online-info-rsvp-how-to-become-a-data-scientist

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 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.

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.

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 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.