FinTech Classes New Haven, CT

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. 

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 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.

Learn Python for financial analysis, machine learning, and algorithmic trading from experienced finance & engineering professionals in this 60-hour immersive.

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.

This 72-week Machine Learning program was designed to teach students the skills they need to launch careers in the fields of computer science, software engineering, and machine learning. This program is split into six trimesters and covers a wide range of computer science and machine learning tools and concepts. The first three trimesters focus on computer science and software engineering, with a focus on Linux, data structures, algorithms, various programming languages, databases, and more. The final three trimesters are very math-heavy, and focus on computer vision, natural language processing, autonomous driving, working with Pandas, Numpy, Tensorflow, and more. Throughout the program, students will also learn important soft skills to enhance their job readiness, with an emphasis around technical writing, collaboration, whiteboarding, as well as the opportunity to participate in mock interviews.

This 2-day course on Python with data science is intended for software developers, IT architects, and data scientists. This course will build on their knowledge of statistics and programming, teaching them the theoretical and technical aspects of the use for Python. Students will learn about the history and purpose of data science, life-cycle phases of data analytics, methods for repairing and normalizing data, and how to aggregate and group data. Students will also complete 10 lab exercises.

This course teaches the fundamentals of SQL querying. This 1-day program teaches students how to execute a simple query, perform a conditional search, work with functions, and organize data. Further, students will learn how to retrieve data from multiple sources and export query results via a text or XML file.

This 1-day course builds on the fundamentals of SQL querying. This intermediate program teaches advanced querying techniques. The coursework will cover the use of subqueries to perform advanced querying, manipulating table data and table structure. Students will learn how to work with views, index data and manage transactions.