Python Data Science Classes Chicago, IL

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.

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.

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.

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 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 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. 

Python + Data Science Bootcamp teaches full-stack web development and data science using Python in 10 weeks. If you're accepted to this program, there is three weeks of prework you'll have to complete before starting the program. The program covers HTML/CSS, JavaScript, APIs, frameworks, and more to build front-end applications. After you've learned front-end development, you'll transition to Python and learn its data science packages. In the end, you'll combine your knowledge and create a capstone project to showcase to potential employers.

This Python for Finance Immersive course gives students a deep understanding of the use of Python for data extraction, analysis, and visualization. Throughout the 30-hour program, the students will work with NumPy, Pandas, Matplotlib and other scientific libraries as they learn how to pull and process data. Students will graduate with hands-on experience working with Jupyter Notebook, having completed small projects using Python to analyze data. This program is open to beginners, with no requirement for experience in advanced mathematics or coding.

In just 12 hours, this Algorithmic Trading with Python course gives students the skills they need to build their own automated trading strategies. They will first learn how to use Python scripts to connect with an online trading brokerage before learning how to trade. Students will also learn to use algorithms to inform and drive investments while learning more about backtesting and exponential moving average strategies. Students will test their knowledge through the use of mock trades in Python.

Perfect for programming beginners, this 6-hour Python for Automation course teaches the basics of web scraping with Python. This class will teach students how to use Python code to retrieve the data you want from websites. Using loops, students will be able to use these strategies to compile data from large numbers of pages with ease. Students can attend online or in person.

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.

In this course, students discover the essentials of machine learning. By learning to work in Python, students gain familiarity with object-oriented programming and neural networks.