Best Python Data Science Classes Online

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.

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 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 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 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.

As people get busier and busier, we want to automate as much as we can day to day including our investments and trading strategies. Using Python, students can learn how to build robust and automated trading strategies without needing to spend hours a day overseeing their portfolio. In the first half of the course, students will learn how to connect their Python scripts with an online trading brokerage. After connecting to a brokerage firm, students will learn how to place and query stock orders. After students feel comfortable placing basic orders, we will introduce trading strategies such as exponential moving average (EMA), Moving Average Convergence Divergence (MACD), and backtesting strategies. After learning these strategies, students will be introduced to Machine learning as it applies to properly value an Option.

The Data Science with Python class teaches fundamental Python coding skills for data science. Additionally, students learn how to use Pandas, NumPy, SciPy, Matplotlib, and Scikit-learn (among other topics) for data science.

This summer course for high school and college students dives into Python programming and its applications in data science. After they master some Python fundamentals, students move onto more advanced topics. Using platforms like Matplotlib, Pandas, and scikit-learn, students learn how to input, graph, and analyze data. This course gives attendees a head start on the most popular and versatile programming language and the fundamentals for a career as a data scientist or analyst.

This advanced Python course helps high school or college students expand their Python programming skills to machine learning and automation. This course breathes life into industry buzzwords like automation and machine learning, so students can meaningfully contribute to large and small organizations. Once participants master tools like logistic and linear regression, students learn more advanced topics, including algorithms with k-nearest neighbor, decision tree, and random forest algorithms. As a result, students quickly master sought-after skills such as variance, bias, fitting, and other statistical concepts.

This live online advanced Python course helps high school or college students expand their Python programming skills to machine learning and automation. This course breathes life into industry buzzwords like automation and machine learning, so students can meaningfully contribute to large and small organizations. Once participants master tools like logistic and linear regression, students learn more advanced topics, including algorithms with k-nearest neighbor, decision tree, and random forest algorithms. As a result, students quickly master sought-after skills such as variance, bias, fitting, and other statistical concepts.