Become a Data Scientist

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 Special Summer Data Science Bootcamp that starts on June 3, 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 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, MongoDB, 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 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 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 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.