Professional Development Courses
Build your knowledge and gain confidence in transforming data sets into actionable insights with this data science program consisting of three courses delivered through remote live instruction.
Throughout the program, you will receive expert-led instruction and work on one hands-on lab and two data science projects to reinforce what you learned while being supported by a dedicated mentor. Upon successful completion of each course, you will earn a certificate that you can share with prospective employers and your professional network.
This bundle of three professional development courses starts by teaching students to write Python, which is readable, portable, and efficient. From there, students will learn the fundamental tools and methods for data analysis in Python and using these skills to distill business insights and communicate them through visualizations. Finally, students will build up their Machine Learning abilities in Python, being able to differentiate between several supervised and unsupervised models, and how to use those models to gain depth, discover structure, and create predictive tools based on observations.
In the real world, a working data scientist may spend a lot of time cleaning and preparing messy data. In this directed lab, students will create their Python scripts and define Python objects while creating a tool to clean messy, real-world data. Students will bolster their skills by following best practices in the object-oriented programming paradigm and control flows of Python, and finish with their Python data cleaning tool.
This project aims to build on top of the new technical Python coding acumen and translate those techniques into part of a more extensive application toolbox learned by the students. The project allows students to experience working with an eye towards a use objective, with enough structure and direction that they gain skills that are transferable to a wide range of industries. Students will read in external data to the appropriate data structures, join multiple sources of data to find insights apparent only by considering the various sources simultaneously, create basic and easily interpretable statistical and visual descriptions of those insights. Furthermore, students will be encouraged to present their findings within a dash app.
This project aims to offer students a glimpse into using machine learning in the context of an insight-driven objective. Unlike many Kaggle-style machine learning projects, there will be an emphasis on the fact that the actual value of a model is not given by a single performance metric of that model. Successful completion will require the application of Data Analysis and Visualization with Python concepts within the context exploration, basic insights, and feature engineering. Students will also apply standard machine learning pipeline practices, including data preparation, model selection, and understanding and communicating the value the model provides within the domain context. Furthermore, students will learn to keep an eye towards the domain objective and how all parts communicate with this objective and play a supporting role in achieving it.
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