FinTech Classes Atlanta, GA

In this FinTech Bootcamp, participants learn how to use Python for financial analysis, machine learning and automation, and SQL. The program is taught by seasoned finance and data science professionals. Students will develop the ability to analyze financial statements, build predictive models, and work with databases. By the end of this course, newly-hired financial analysts will be prepared to succeed on the job, and aspiring financial analysts will have bolstered their resumes and technical interview skills. This course offers a free retake so students can refresh the material. It also includes one-on-one mentoring sessions.

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.

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 2-day 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 Numpy Arrays, pandas, and MATPLOTLIB to perform data analysis and create data visualizations. This intensive course is meant to quickly get beginners in Python up to speed on performing data analysis and visualization in Python.

This financial modeling course teaches the commonly-used valuation and modeling techniques. Students will learn fundamental Excel and finance skills needed for financial modeling, build a DCF model, create an M & A model, and make an LBO model.

In this advanced modeling course, students will focus on M&A and LBO analyses. Students will learn key accounting and finance concepts for each type of analysis as they build towards a full project analysis. This course features a mix of instructor teaching as well as project work building an M&A model as well as an LBO model.

This Python and machine learning course is a 2-day bootcamp designed for individuals with some familiarity with Python programming looking to advance their skill set. In this hands-on bootcamp, students will learn how to use some Python data science libraries and various machine learning techniques. Day one of the bootcamp focuses on introducing students to two data science libraries (NumPy and Pandas), best practices for preparing data, an intro to machine learning, and regression analysis. Day two starts off with cross validation and regularization of data, concludes with classification, and brings everything learned in days one and two together.