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Build a Career in Data Science

  • Course Code: Data Science - Build a Career in Data Science
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 4 Days Audience: This course is geared to know you that what are the keys to a data scientist’s long-term success.

Course Snapshot 

  • Duration: 4 days 
  • Skill-level: Foundation-level Data Science skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared to know you that what are the keys to a data scientist’s long-term success. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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. 

Working in a hands-on learning environment, led by Data Science expert instructor, students will learn about and explore: 

  • You’ll 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. 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Creating a portfolio of data science projects 
  • Assessing and negotiating an offer 
  • Leaving gracefully and moving up the ladder 
  • Interviews with professional data scientists 

Audience & Pre-Requisites 

This course is for readers who want to begin or advance a data science career. 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills.  
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. What is data science? 
  • Different types of data science jobs 
  • Choosing your path 
  • Interview with Robert Chang, Data Scientist at Airbnb 
  1. Data science companies 
  • 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 
  1. Getting the Skills 
  • 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 
  1. Building a Portfolio 
  • Creating a project 
  • Starting a Blog 
  • Example Projects 
  • Interview with David Robinson, Data Insights Engineering Manager at Flatiron Health 
  1. The Search: Identifying the Right Job for You 
  • Finding jobs 
  • Deciding which jobs to apply for 
  • Interview with Jesse Mostipak, Managing Director of Data Science at Teaching Trust 
  1. The Application: Resumes and Cover Letters 
  • Resume: the basics 
  • Cover letters: the basics 
  • Tailoring 
  • Referrals 
  • Interview with Kristen Kehrer, a data science instructor and course creator 
  1. The interview: what to expect and how to handle it 
  • 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 
  1. The Offer: Knowing What to Accept 
  • 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 
  1. The First Months on the Job 
  • 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 
  1. Making an effective analysis 
  • The request 
  • The analysis plans 
  • Doing the analysis 
  • Wrapping it up 
  • Interview with Hilary Parker, a Data Scientist at Stitch Fix 
  1. Deploying a model into production 
  • 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 
  1. Working with stakeholders 
  • Types of stakeholders 
  • Working with stakeholders 
  • Prioritizing work 
  • Concluding remarks 
  • Interview with Sade Snowden-Akintunde, a Data Scientist at Etsy 
  1. When your data science project fails 
  • 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 
  1. Joining the Data Science Community 
  • 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 
  1. Leaving your job gracefully 
  • Deciding to leave 
  • How the job search differs after your first job 
  • Giving notice 
  • Interview with Amanda Casari, Engineering Manager at Google 
  1. Moving up the ladder 
  • 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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