The ultimate Data Science Bundles helps you develop your data science and data analytics skills.
Pay $0 and start the courses with financing plans.
In response to COVID-19 State reopening, all our courses could be taken either in-person or remote/live online. Please indicate your preference by emailing [email protected] after registering for our class.
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.
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 is an in-person class that will be conducted on Weekdays
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.
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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