Boost your skills in the field of data science in Python, R, machine learning and more, by choosing a combination of part-time, on-campus and online courses. Select the bundles according to your knowledge, interests and benefit from the bundled discounts. Also, choose dates according to your convenience, take advantage of the cutting-edge curriculum, and jumpstart your path to success. All the bundles are supported with a full along a complete financing support by Climb Credit loan.
Prepare for the Immersive Data Science Bootcamp with courses in Python, R, Data Analysis and Visualization. This combination of three courses helps you prepare for the bootcamp by building a foundation in both R and Python as well as prepare you for building your own applications using R Shiny. You will learn all the practical tools used in data analysis and visualization.
Start with basics with an introduction to Python and advance to comprehensive introduction to data science with Python. Customize your learning path to excel in Machine Learning, Data Analysis and Visualization with Python by choosing a combination of language basics including numpy, scipy, pandas, matplotlib, and seaborn. After successfully completing this bundle, you will be able to analyze complex datasets and make predictions in Python.
Prepare for statistical programming in R with Machine Learning, Data Analysis and Visualization. These intermediate courses offer a full understanding in gaining essential skills of processing, manipulating and analyzing data of various types, creating advanced visualizations, generating reports, implement machine learning algorithms and documenting your code.
This bundle covers the most advanced data science topics like deep learning, Big Data with Amazon Cloud, Hadoop, Spark and Docker. You will also learn data mining, regression models, tree models, discriminant analysis and naive bayes, key components of Apache Hadoop and more. Fluency in programming is recommended to get hands-on training of Big Data technologies.
Big Data with Amazon Cloud, Hadoop/Spark and Docker
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.
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: Data Analysis and Visualization
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 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 R: Data Analysis and Visualization
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 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.
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NYC Data Science Academy
NYC Data Science Academy teaches data science, trains companies and their employees to better profit from data, excels at big data project consulting, and connects trained Data Scientists to our industry.
NYC Data Science Academy is licensed by New York State Education Department.
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