FINDING A JOB Make a personal website Free hosting options GitHub Pages Google

Finding a job make a personal website free hosting

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FINDING A JOB Make a personal website. Free hosting options: GitHub Pages, Google Sites Pay for your own URL (but not the hosting). Make a clean website, and make sure it renders on mobile: Bootstrap: Foundation: Highlight relevant coursework, open source projects, tangible work experience, etc Highlight tools that you know (not just programming languages, but also frameworks like TensorFlow and general tech skills) 11
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“REQUIREMENTS” Data science job postings – and, honestly, CS postings in general – often have completely nonsense requirements 1. The group is filtering out some noise from the applicant pool 2. Somebody wrote the posting and went buzzword crazy In most cases (unless the position is a team lead, pure R&D, or a very senior role) you can work around requirements: A good, simple website with good, clean projects can work wonders here … Reach out and speak directly with team members Alumni network, internship network, online forums 12
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SURVEY Has anyone worked in a data scientist position here? Interview questions? 13 ?
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INTERVIEWING We saw that there is no standard for being a “data scientist” – and there is also no standard interview style … … but, generally, you’ll be asked about the five “chunks” we covered in this class, plus core CS stuff: Software engineering questions Data collection and management questions (SQL, APIs, scraping, newer DB stuff like NoSQL, Graph DBs, etc) General “how would you approach …” EDA questions Machine learning questions (“general” best practices, but you should be able to describe DTs, RFs, SVM, basic neural nets, KNN, OLS, boosting , PCA, feature selection , clustering) Basic “best practices” for statistics, e.g., hypothesis testing Take-home data analysis project (YMMV) 14
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GRADUATE SCHOOL, ACADEMIA, R&D, … Data science isn’t really an academic discipline by itself, but it comes up everywhere within and without CS Modern science is built on a “CS and Statistics stack” … Academic work in the area: Outside of CS, using techniques from this class to help fundamental research in that field Within CS, fundamental research in: Machine learning Statistics (non-pure theory) Databases and data management Incentives, game theory, mechanism design Within CS, trying to automate data science (e.g., Google Cloud’s Predictive Analytics, “Automatic Statistician,” …) 15
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UNDERGRADUATE RESEARCH AT UMD Outside of CS: Is there an application area you really like? Most researchers will be thrilled to talk about how data science/ML can be applied to their domain You will have to learn about their domain J Inside of CS: Many of us will want you to have taken or be taking at least CMSC422, or CMSC424, or both 16
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