Learn R, Python, machine learning and big data in just 12 weeks with our full job support
Upgrade your data science skills with these in-person targeted courses and earn a certificate
Master in-demand skills through industry-proven curriculum and premium learning system
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.
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.
In this talk, David Kjerrumgaard will present a solution based on Apache Pulsar Functions that significantly reduces decision latency by using probabilistic algorithms to perform analytic calculations on the edge. Agenda6:30 - 7:00 pm - Food, Drinks, and Mingling7:00 - 8:00 pm - Talk by David Kjerrumgaard8:00 pm - 8:30 pm - Q&A and Networking. The business value of data decreases rapidly after it is created, particularly in use cases such as fraud prevention, cybersecurity, and real-time system monitoring. The high-volume, high-velocity datasets used to feed these use cases often contain valuable, but perishable, insights that must be acted upon immediately. In order to maximize the value of their data enterprises must fundamentally change their approach to processing real-time data to focusing on reducing their decision latency on the perishable insights that exist within their real-time data streams. Thereby enabling the organization to act upon them while the window of opportunity is open. Generating timely insights in a high-volume, high-velocity data environment is challenging for a multitude of reasons. As the volume of data increases, so does the amount of time required to transmit it back to the datacenter and process it. Secondly, as the velocity of the data increases, the faster the data and the insights derived from it lose value. Speaker Biography: David Kjerrumgaard is a Director of Solution Architecture at Streamlio, and also a contributor to the Apache Pulsar, and Apache NiFi projects. He was formerly the Global Practice Director at Hortonworks, where he was responsible for the development of best practices and solutions for the professional services team, with a focus on Streaming technologies including Kafka, NiFi, and Storm. He is the author of the “Pulsar In Action”, and holds a B.S and Master’s Degree in Computer Science from Kent State University.
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.
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/
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.
