Python Classes Hartford, CT

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 the essential skills and tools to become a Python Developer. This beginner-friendly course teaches students Python for software development with Django and Django REST in addition to other developer tools such as Git and SQL. After completing this certificate, students will be able to apply for the following roles: Python Developer, Back End Developer, Software Engineer, and many more.

This 3-day introductory Python programming course is designed for students with no prior programming experience and are looking to get comfortable with basic skills needed for Python programming. The course will cover foundational Python concepts, including data types, control statements, using functions and modules, handling exceptions and debugging programs, working with lists, and how to work with a variety of data types. By the end of the course, students will be familiar with writing programs for simple applications.

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.

Participants in this course will learn the basics of the Python programming language and its functions. They’ll also learn some aspects of object-oriented programming, multi-threading, and source codes.

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 5-day introductory Python programming course is designed for students with no prior programming experience and are looking to get comfortable with basic skills needed for Python programming. This skills-centric course will cover foundational Python concepts, including data types, control statements, using functions and modules, handling exceptions and debugging programs, working with lists, and how to work with a variety of data types. By the end of the course, students will be familiar with writing programs to create real-world Python applications.

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.