Data Science Classes New York

In this data science bootcamp, students will build programming skills and data analysis skills using Python. This course is open to beginners and is meant to get individuals up and running with Python programming and data science to generate insights from data. Topics covered include programming fundamentals, working with data frames, data analysis, data visualization, and statistical analysis. This course offers flexible scheduling options and a free retake for students to refresh the materials.

In this course, students expand their Python programming skills into machine learning and algorithms that can independently learn patterns and make decisions. The course begins with linear and logistic regressions, the most time-tested and reliable tools for approaching a machine learning problem. Students then progress to algorithms with a different theoretical basis, such as k-nearest neighbors, decision trees, and random forest. This will bring important statistical concepts to the forefront, such as bias, variance, and overfitting. Participants also learn how to measure the accuracy of your models, as well as tips for choosing effective features and algorithms.

Learn to problem solve, and effectively communicate, like an analyst. This course teaches you to use industry-standard tools to make ethical, data-driven decisions. Experience hands-on training to master SQL, Excel, Tableau, PowerBI, and Python – tools listed in virtually every data analytics job posting across industries.

In the Python for Data Science Masterclass, students learn both foundational and advanced Python programming skills, including data types, control flow, and object-oriented programming. The curriculum covers data science libraries like NumPy and Pandas for data manipulation and Matplotlib and Seaborn for data visualization. The course also covers advanced techniques such as web scraping, database querying, and using APIs.

The Advanced Python for Data Science Bootcamp teaches students how to refine their Python programming skills by learning advanced functions, web scraping techniques, and working with JSON data and APIs. The lessons cover regression analysis, creating complex visualizations, and integrating SQL in Jupyter Notebooks. The course also dives into the use of libraries like Seaborn and Beautiful Soup for data visualization and web scraping, respectively.

Master the fundamentals of AI and data science in this certificate program, designed to provide hands-on experience with key tools like Python, SQL, and machine learning algorithms. Whether you're starting fresh or enhancing your skill set, this course prepares you for a successful career in data-driven decision-making.

In this Python automation course, students will learn to automate tasks using Python for various applications. This course is meant for those with prior Python experience looking to learn automation techniques like scheduling programs, updating spreadsheets, and web scraping. Topics include HTML and CSS basics, web scraping techniques, working with spreadsheets using Python, and scheduling scripts. This course offers flexible scheduling options plus a free retake for students to refresh the material.

This 1-week data analytics course provides a deep-dive into using Python for data analysis. Students will get comfortable with the basics of Python programming and start working with critical data analysis libraries like Numpy, Pandas, and Matplotlib to perform data analysis and create data visualizations. This 35 hour intensive is meant to quickly get beginners in Python up to speed on performing data analysis and visualization in Python.

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.