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Home » Technology » Big Data & Data Science » Build a Career in Data Science

Course Skill Level:

Foundational to Intermediate

Course Duration:

4 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:

    BACIDSL21E09

Who should attend & recommended skills

  • This course is geared for those who want to begin or advance a data science career and the keys to a data scientist’s long-term success.
  • Skill-level: Foundation-level Data Science skills for Intermediate skilled team members. This is not a basic class.
  • IT skills: Basic to Intermediate (1-5 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su

About this course

Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the course.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by Data Science expert instructor, participants will learn about and explore:
  • The insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the course
  • Creating a portfolio of data science projects
  • Assessing and negotiating an offer
  • Leaving gracefully and moving up the ladder
  • Interviews with professional data scientists

Course breakdown / modules

  • Different types of data science jobs
  • Choosing your path
  • Interview with Robert Chang, Data Scientist at Airbnb

  • MTC – the Massive Tech Company
  • Handbag LOVE – the established retailer Seg-Metra – the early-stage startup
  • Videory – the late-stage, successful tech start-up
  • Global Aerospace Dynamics (GAD) – the massive government contractor
  • Putting it all together
  • Interview with Randy Au, Quantitative User Experience Researcher at Google

  • Earning a data science degree
  • Going through a bootcamp
  • Getting data science work within your company
  • Teaching yourself
  • Making the choice
  • Interview with Julia Silge, Data Scientist at Stack Overflow

  • Creating a project
  • Starting a Blog
  • Example Projects
  • Interview with David Robinson, Data Insights Engineering Manager at Flatiron Health

  • Finding jobs
  • Deciding which jobs to apply for
  • Interview with Jesse Mostipak, Managing Director of Data Science at Teaching Trust

  • Resume: the basics
  • Cover letters: the basics
  • Tailoring
  • Referrals
  • Interview with Kristen Kehrer, a data science instructor and course creator

  • What do companies want?
  • The interview processes
  • Step one: the initial phone screen interview
  • Step two: the on-site interview
  • Step three: the case study
  • Step four: the final interview
  • The offer
  • Interview with Ryan Williams, Senior Decision Scientist at Starbucks

  • The process
  • Receiving the offer
  • Negotiation
  • Negotiation Tactics
  • How to pick between two good job offers
  • Interview with Brooke Watson Madubuonwu, a Senior Data Scientist at the ACLU

  • The First Month
  • Becoming productive
  • If you’re the first data scientist
  • When it’s not what was promised
  • Interview with Jarvis Miller, Data Scientist at Spotify

  • The request
  • The analysis plans
  • Doing the analysis
  • Wrapping it up
  • Interview with Hilary Parker, a Data Scientist at Stitch Fix

  • What is deploying to production anyway?
  • Making the production system
  • Keeping the system running
  • Wrapping up
  • Interview with Heather Nolis, a Machine Learning Engineer at T-Mobile

  • Types of stakeholders
  • Working with stakeholders
  • Prioritizing work
  • Concluding remarks
  • Interview with Sade Snowden-Akintunde, a Data Scientist at Etsy

  • Why data science projects fail
  • Managing risk
  • What you can do when your projects failed
  • Interview with Michelle Keim, Head of Data Science Machine Learning at Pluralsight

  • Growing your portfolio
  • Attending Conferences
  • Giving talks
  • Contributing to open source
  • Recognizing and avoiding burnout
  • Interview with Renee Teate, Director of Data Science at HelioCampus

  • Deciding to leave
  • How the job search differs after your first job
  • Giving notice
  • Interview with Amanda Casari, Engineering Manager at Google

  • The management tracks
  • Principal data scientist track
  • Switching to independent consulting
  • Choosing your path
  • Interview with Angela Bassa, Head of Data Science, Data Engineering, and Machine Learning at iRobot

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