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Getting Started with BioCAD
How It Works:
- Course Access: Each course requires a unique access code to enter. If your program includes the intermediate series courses, you must complete the required introductory courses before moving on to the intermediate courses.
- Course Structure: Each course contains a mixture of lecture videos, short check-in quizzes, and/or Python practice problems, with occasional supplementary material. For the videos that provide Python practice problems, you are encouraged to try to solve the problem on your own first and/or follow along before a solution is provided.
Required Free Materials:
- Python programs such as Google Colab or Python (version 3.0 or newer).
- Google Colab can be accessed through your web browser using a Gmail/Google Suite account (no download required, but syntax may differ slightly from the actual Python program).
- Python can be downloaded for your operating system using the links below:
Download for: Windows, Linux/UNIX, macOS, Other.
- Course Access: Each course requires a unique access code to enter. If your program includes the intermediate series courses, you must complete the required introductory courses before moving on to the intermediate courses.
Available courses
Prerequisite: None
Estimated time to complete: 6-7 hours
This course is meant to provide an introduction to the Python coding language and some of its commonly used libraries, focusing on applications in biomanufacturing and bioprocessing data analysis.
Prerequisite: Introduction to Python in Biomanufacturing
Estimated time to complete: 5-6 hours
This course focuses on key aspects of biomanufacturing supply chain management. You'll learn to calculate performance indicators, evaluate outsourcing, address cold chain logistics challenges, explore reverse logistics and closed-loop supply chains, and optimize inventory strategies.
Prerequisite: Introduction to Python in Biomanufacturing
Estimated time to complete: 5-6 hours
In this course, you'll learn the importance of data analytics in biomanufacturing-specific business operations. You'll gain hands-on experience with Python libraries for time-series demand forecasting and explore Six Sigma data analytics for enhancing operational efficiency through biomanufacturing case studies.Prerequisite: Introduction to Python in Biomanufacturing
Estimated time to complete: 6-7 hours
In this course, you'll work with diverse analytical techniques. Explore univariate analysis with t-tests and regression models linking cell growth to osmolarity. Dive into supervised and unsupervised machine learning, evaluating bioreactor performance, identifying critical parameters, and processing Raman spectra data to predict metabolites, using PCA analysis principles and multivariate analysis.