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How to land an entry level DS job

How to land an entry level DS job

Here's a talk from Amber Teng on her journey to landing an entry level DS job

Shanelle Recheta

March 22, 2021
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  1. 5 Tips for breaking into
    data science
    Amber Teng

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  2. Part 1: Learning the basics
    Part 2: Showcasing your skills
    Part 3: Landing a Job
    Part 4:Collaboration and Beyond

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  3. Part 1: Learning the basics

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  4. Tip #1: Learn how to Code
    Languages:
    - Python
    - SQL
    - *R
    - *HTML/CSS
    - *Excel

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  5. Tip #1: Learn how to Code
    Packages:
    - SciKit-Learn
    - NumPy
    - SciPy
    - Pandas
    - Matplotlib
    - Seaborn
    - NLTK, Gensim

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  6. Tip #2: Brush up on your math
    Probability & Statistics:
    - Random Variables
    - Expectation
    - Parametric Models
    - Bayes’ Theorem
    - Probability
    Distributions
    - Regression Models

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  7. Tip #2: Brush up on your math
    Linear Algebra:
    - Representing problems in
    linear algebra
    - Cosine similarity
    - Matrix Operations
    - PCA and SVD

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  8. Part 2: Showcasing Your Skills

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  9. Tip #3: Build, Build, and Build
    Creating Project Portfolios
    & Sharing Your Ideas
    - Github
    - Personal Website
    (getforge, bootstrap)
    - Medium (towards data
    science)
    - Deployment (Heroku,
    Flask)

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  10. Part 3: Landing a Job

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  11. Tip #4: Be Prepared
    - Have an elevator pitch
    - Practice interviewing
    regularly
    - Know your resume by heart
    - Diligence is key
    - Progress is a process: Be
    prepared for ups and downs

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  12. Part 4: Collaboration and Beyond

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  13. Tip #5: Connect and Collaborate
    Connecting with the Data
    Science Community:
    - Twitter
    - LinkedIn
    - Conferences
    - Slack Groups
    - Hackathons
    Pass it forward:
    - Contribute to
    FTWFoundation!
    - Mentorship + TA Roles
    - Data science for social
    good (ethics)
    - Data Science para sa bayan

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  14. 1. Learn to Code
    2. Learn Math
    3. Keep Building
    4. Be prepared
    5. Pass it forward

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  15. Thank you!
    [email protected]
    @ambervteng
    in/angelavteng

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  16. Appendix: Resources and Links
    - Udemy Courses:
    - https://www.udemy.com/course/python-for-dat
    a-science-and-machine-learning-bootcamp/lea
    rn/lecture/5733180?start=0#overview
    - MIT OCW:
    - https://ocw.mit.edu/courses/electrical-engineeri
    ng-and-computer-science/6-041sc-probabilistic
    -systems-analysis-and-applied-probability-fall-
    2013/index.htm
    - YouTube Channels:
    - https://www.youtube.com/channel/UCxX9wt5F
    WQUAAz4UrysqK9A
    - Online Forums + Websites:
    - https://stackexchange.com/
    - https://www.kaggle.com/
    - https://sqlzoo.net/
    - MOOCS/Course Resources:
    - https://cims.nyu.edu/~cfgranda/pages/DSGA1
    002_fall15/index.html
    - https://github.com/jakevdp/PythonDataScience
    Handbook/blob/master/notebooks/01.00-IPyth
    on-Beyond-Normal-Python.ipynb
    - Medium Articles:
    - https://towardsdatascience.com/dealing-with-m
    ulticlass-data-78a1a27c5dcc
    - https://towardsdatascience.com/15-data-scien
    ce-slack-communities-to-join-8fac301bd6ce
    - Career + Interview Prep Resources:
    - https://www.brown.edu/campus-life/support/car
    eerlab/undergraduate-0/resumes-cover-letters-
    and-online-profiles
    - https://leetcode.com/problemset/all/

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