NYC Data Science Academy

This course in machine learning with R introduces novice R programmers a foundation in machine learning theory. The course covers the foundations of statistics and simple linear regressions, multiple linear regression and generalized linear models, kNN and Naïve Bayes, the curse of dimensionality, tree models, and SVMs. Students who complete this course will understand the mathematics behind machine learning algorithms and be able to use them in real-world situations.

This Python machine learning course teaches machine learning methods and modules with the Python code designed to implement them. The units of this course explain simple and complex linear regressions, classification methods for logistic regression, discriminant analysis, and naïve Bayes, support vector machines and tree-based methods, regularization strategies, and how to use clustering algorithms. Completion of this 20-week course will prepare students to use machine learning algorithms to analyze complex datasets and make logical predictions.

This six-week course teaches students how to use key components of Apache Hadoop, Spark, MapReduce, and more. Using platforms such as Databricks, Amazon Web Services, and Docker, students will learn how to use Python and cloud computing to run exercises. Students will complete five units on topics such as Hadoop, MapReduce, Apache Hive, Apache Pig, Apache Spark, and Amazon Web Services.

This 35-hour program offers a comprehensive introduction to R. This course teaches the skills needed to process, manipulate, and analyze data, create visualizations, and generate reports. The program begins with an overview of programming with R. It then moves on to the treatment of basic data elements, the use of “dplyr” to manipulate data, and how to create basic and advanced visualizations such as violin and mosaic plots, and time-series diagrams.

This data science with Python course is for people with a basic knowledge of programming with Python. This comprehensive course will explain how to work with some of the most widely-used data analysis and visualization modules, such as Pandas, matplotlib, Numpy, Scipy, and more. The course will begin with a review of the basic syntax and data structures of Python before moving on to object-oriented programming, scientific computation, and data visualization. The final unit will teach you how to manipulate data with Pandas before you complete a final project.

This course provides an introduction to the use of Python programming language for data analysis. Python, through the use of list and string manipulation, control structures, and data analysis packages, can be used to manage and analyze large sets of data. In this course, you will learn how to use Scipy, Numpy, Pandas, Seaborn and matplotlib to analyze data and create visualizations. This course has 4 units, covering list manipulation, strings and simple I/O, control structures and data analysis packages. IPython notebook is also used to show how codes and change codes are used during class.

This data science bootcamp offers the ability to learn online with a 16-week full-time schedule or a 24-week part-time schedule. Students will learn how to use tools such as Python, R, Hadoop, and AWS to facilitate data visualization and analysis, machine and deep learning, and building statistical models. This program culminates in a professional capstone experience.

This data science bootcamp is an immersive program intended to give you the real-world skills needed to become a data scientist. This program teaches skills in Python and R, combining those tools with principles of machine learning, data visualization, and analytics. You will learn how to build statistical models and linear regressions using tools such as Hadoop, Spark, AWS, and much more. Upon completion of this 12-week program, students will be ready to begin a career in data science.