Finding a job make a personal website free hosting

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

“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

SURVEY
Has anyone worked in a data scientist position here?
Interview questions?
13
?

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

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

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