Python Data Science Classes NYC

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.

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 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.

Upskill and take your finance skills to the next level with this Python for Finance class. You'll learn to analyze large amounts of financial data using Python, create visualizations, and start using statistics for predictive modeling. 

This comprehensive Python course teaches beginners how to code, analyze data, and create machine learning models with Python. Students will start with the basics of programming in Python and build up their data skills on their way to learning machine learning and automation. Topics include programming fundamentals, data analysis, data visualization, machine learning, automation, and web scraping. This course offers flexible scheduling options and provides a free retake so students can refresh the material.

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 18-hour Machine Learning & Automation for Finance course teaches advanced skills in machine learning in Python. Students in this course will develop advanced skills in the Python language, learn how to use object-oriented programming and regression models, and apply these concepts to a real world problem. They will also learn how to use Python to build predictive financial models and to create budgets that may be exported.

This course on data wrangling with Python offers a 3-day comprehensive introduction. The purpose of this course is to give an immersive foundation that equips students with the most critical data analysis tools. Students will learn how to work with NumPy, Pandas, and MatPlotLib to calculate descriptive statistics, work with alternative data sources, and scrape web pages. The course culminates with a real-life data wrangling project.