Class GitHub Repository: https://github.com/CUNYTechPrep/2023-fall-data-science-fridays
CTP | CUNY Tech Prep
CUNY Tech Prep is a year long technical and professional development program for CUNY computer science and related majors to learn in-demand technologies, master professional soft skills, and land great tech jobs in NYC.
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Table of Contents
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Our mission is to equip CUNY students with the resources they need to jumpstart their careers in tech.
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Developed and delivered with the CUNY Institute for Software Design and Development and industry leaders, CTP is designed to provide students with industry exposure to software development and a connection to tech jobs post-graduation.
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The NYC Tech Talent Pipeline is a multi-million industry partnership designed to support the growth of the City's tech sector and deliver quality jobs for New Yorkers and quality talent for New York’s businesses.
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CUNY Tech Prep was established in 2015 to serve computer science and computing students from the 11 four-year CUNY Colleges that are preparing to enter the workforce. Students accepted into CTP each academic year participate in a program designed to prepare them for the challenges of the job search and technical work in the industry.
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The curriculum is designed around three pillars:
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Our technical curriculum offers two tracks: full stack web development using modern JavaScript and data science using Python. The curriculums are developed, evaluated, and taught by industry experts.
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In both tracks, students work in teams to design, develop, and deploy projects where they learn and apply tools, concepts, and processes — Git, TDD and automated testing, CI/CD, agile, web security, etc. — desired by high-tech employers.
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Students work in teams on projects they propose, design, and implement. Each team is assigned a mentor that supervises the work, introduces the software development life cycle and best practices, and performs code reviews.
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CTP instructors, TA's, and mentors all have extensive current and past industry experience as software engineers and data scientists.
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It takes more than technical skills to land a job in tech! That's why CUNY Tech Prep takes a holistic approach to professional development. Our fellows have access to:
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1:1 Career Coaching, including behavioral interview practice In-class workshops led by our career coaches on topics such as networking, project pitching, job search strategy, resumes, and communication skills
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Mock technical interviews conducted by industry professionals
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Workshops led by industry professionals
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Assistance with crafting technical resumes
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In this repository you can find all of my code examples as the Teaching Assistant for the Fall 2023 Data Science Track Cohort 9 at CUNY Tech Prep (CTP).
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You can also use NBViwer to see all of my code example notebooks interactively.
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE for more information.
MIT License
Copyright (c) 2023 Georgios Ioannou
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Georgios Ioannou - @LinkedIn
Georgios Ioannou - @georgiosioannoucoder - Please contact me via the form in my portfolio.
Project Link: https://github.com/GeorgiosIoannouCoder/2023-fall-data-science-ta