This course leads the student from the basics of writing and running Python scripts to more advanced features such as file operations, regular expressions, working with binary data, and using the extensive functionality of Python modules. Extra emphasis is placed on features unique to Python, such as tuples, array slices, and output formatting.
This is a hands-on programming class. All concepts are reinforced by informal practice during the lecture followed by graduated lab exercises.
Python Intro is a practical introduction to a working programming language, not an academic overview of syntax and grammar. Students will immediately be able to use Python to complete tasks in the real world.
Note: This course can be taught for either Python 2.x or Python 3.x. Version-specific course materials and exercises will be provided.
Students should already have a working, user-level knowledge of Unix/Linux, Mac, or Windows. While not required, basic skills with at least one other programming language will be helpful.
This course provides a ramp-up to using Python for scientific and mathematical computing. Starting with the basics, it progresses to the most important Python modules for working with data, from arrays, to statistics, to plotting results. The material is geared towards scientists and engineers.
This is a hands-on programming class. All concepts are reinforced by informal practice during the lecture followed by lab exercises. Many labs build on earlier labs, which helps students retain the earlier material.
Applied Python is 35 percent hands-on, 65 percent lecture, with the longest lecture segments lasting for around 45 minutes. Students “learn by doing,” with immediate opportunities to apply the material they learn to real-world problems.
While there are no programming prerequisites, programming experience is helpful. Students should be comfortable working with files and folders, and should not be afraid of the command line.
Python Programming is a practical introduction to a working programming language, not an academic overview of syntax and grammar. Students will immediately be able to use Python to complete tasks in the real world.
Once students have mastered the basics of Python via our introductory Python course or their own work, it’s time to move on to applying Python to daily programming needs. This course picks up where Python I leaves off, covering some topics in more detail, and adding many new ones, with a focus on enterprise development.
Python II is 40percent hands-on, 60 percent lecture, with the longest lecture segments lasting for around 45 minutes. Students “learn by doing,” with immediate opportunities to apply the material they learn to real-world problems.
All students should be able to write simple Python scripts, using basic data types, program structures, and the standard Python library.
All students will learn to use Python to:
The Python for Data Analytics course is intended to develop data analysts capable of driving data-driven decisions in any organization. The course begins with a refresher on the Python programming language and builds on this knowledge to introduce students to the world of data analytics in Python. Students will learn to use advanced data structures in Python, access data from external sources, use descriptive analytics, perform statistical analysis, and visualize their data in meaningful ways.
Little to some programming knowledge in any programming language, preferably Python.
Upon completion of this course, participants should:
Part 1: Python Environment, Conventions, and Fundamentals Refresher
Part 2: Introduction to Data Analytics
Part 3: Advanced Data Structures
Part 4: Accessing Data
Part 5: Data Wrangling
Part 6: Introduction to Exploratory Data Analytics
Part 7: Introduction to Statistical Analytics
Part 8: Visualizing Data
The Python for Data Analytics II course builds on concepts from the first Python for Data Analytics course. Students will learn to leverage Python generators to conserve memory, serialize data into multiple formats, collect data from the web, integrate with Excel, use regular expressions, and perform predictive analytics. In the end, students will obtain the skills necessary to use data to make projections capable of influencing a diverse array of business decisions.
The following are prerequisites for this course:
Part 1: Python Generators
Part 2: Python Data Serialization
Part 3: Integrating Python with Web Data
Part 4: Integrating Python with Excel
Part 5: Regular Expressions in Python
Part 6: Introduction to Predictive Analytics
Part 7: Predictive & Prescriptive Analytics Techniques
The Python for Data Science and Machine Learning course builds on concepts from the Python for Data Analytics courses. Students will learn to prepare data for machine learning, implement machine learning algorithms, train machine learning models, and use evaluation metrics on their machine learning models. In the end, students will obtain the skills necessary to utilize machine learning for predictive analytics.
Part 1: Introduction to Machine Learning
Part 1: Supervised Machine Learning
Part 2: Unsupervised Machine Learning
Part 4: Training Models
Part 3: Model Evaluation and Improvement