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You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager.Full of useful advice, real-case scenarios, and contributions from professionals in the industry.
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About the Technology
What are the keys to a data scientist’s long-term success? Blending your technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career.About the book
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 book.
Table of Contents takes you straight to the bookdetailed table of contents
Part 1: Getting started with data science
1 What is data science?
1.1 What is data science?
1.1.1 Mathematics/statistics
1.1.2 Databases/programming
1.1.3 Business understanding
1.2 Different types of data science jobs
1.2.1 Business Intelligence
1.2.2 Machine Learning
1.2.3 Decision Science
1.2.4 Related jobs
1.3 Choosing your path
1.4 Interview with Robert Chang, data scientist at Airbnb
What was your first data science journey?
What should people look for in a data science job?
What skills do you need to be a data scientist?
Summary
2 Data science companies
2.1 MTC: Massive Tech Company
2.1.1 Your team: one of many in MTC
2.1.2 The tech: advanced, but siloed across the company
2.1.3 The pros and cons of MTC
2.2 HandbagLOVE: The established retailer
2.2.1 Your team: a small group struggling to grow
2.2.2 Your tech: a legacy stack that’s starting to change
2.2.3 The pros and cons of HandbagLOVE
2.3 Seg-Metra: The early-stage startup
2.3.1 Your team (what team?)
2.3.2 The tech: Cutting-edge technology that’s taped together
2.3.3 Pros and cons of Seg-Metra
2.4 Videory: The late-stage, successful tech startup
2.4.1 The team: Specialized but with room to move around
2.4.2 The tech: Trying to avoid getting bogged down by legacy code
2.4.3 The pros and cons of Videory
2.5 Global Aerospace Dynamics: The giant government contractor
2.5.1 The team: A data scientist in a sea of engineers
2.5.2 The tech: Old, hardened, and on security lockdown
2.5.3 The pros and cons of GAD
2.6 Putting it all together
2.7 Interview with Randy Au, quantitative user experience researcher at Google
Are there big differences between large and small companies?
Are there differences based on the industry of the company?
What’s your final piece of advice for beginning data scientists?
Summary
3 Getting the skills
3.1 Earning a data science degree
3.1.1 Choosing the school
3.1.2 Getting into an academic program
3.1.3 Academic degree summary
3.2 Going through a bootcamp
3.2.1 What you learn
3.2.2 Cost
3.2.3 Choosing a program
3.2.4 Data science bootcamp summary
3.3 Getting data science work within your company
3.3.1 Summarizing learning on the job
3.4 Teaching yourself
3.4.1 Summarizing self-teaching
3.5 Making the choice
3.6 Interview with Julia Silge, data scientist and software engineer at RStudio
Before becoming a data scientist, you worked in academia; how have the skills learned there helped you as a data scientist?
When deciding to become a data scientist, what did you use to pick up new skills?
Did you know going into data science what kind of work you wanted to be doing?
What would you recommend to people looking to get the skills to be a data scientist?
Summary
4 Building a Portfolio
4.1 Creating a project
4.1.1 Finding the data and asking a question
4.1.2 Choosing a direction
4.1.3 Filling out a GitHub README
4.2 Starting a Blog
4.2.1 Potential topics
4.2.2 Logistics
4.3 Working on example projects
4.3.1 Data science freelancers
4.3.2 Training a neural network on offensive license plates
4.4 Interview with David Robinson, data scientist
How did you start blogging?
Are there any specific opportunities you have gotten from public work?
Are there people you think would especially benefit from doing public work?
How has your view on the value of public work changed over time?
How do you come up with ideas for your data analysis posts?
What’s your final piece of advice for aspiring and junior data scientists?
Summary
Chapters 1—4 resources
BooksBlog posts
Books
Part 2: Finding your data science job
5 The search: Identifying the right job for you
5.1 Finding jobs
5.1.1 Decoding descriptions
5.1.2 Watching for Red Flags
5.1.3 Setting your expectations
5.1.4 Attending meetups
5.1.5 Using social media
5.2 Deciding which jobs to apply for
5.2.1 Researching the company online
5.3 Interview with Jesse Mostipak, developer advocate at Kaggle
What recommendations do you have for starting a job search?
How can you build your network?
What do you do if you don’t feel confident applying to data science jobs?
What would you say to someone who thinks “I don’t meet the full list of any job’s required qualifications?”
What’s your final piece of advice to aspiring data scientists?
Summary
6 The application: Résumés and cover letters
6.1 Résumé: The basics
6.1.1 Structure
6.1.2 Deeper into the experience section: generating content
6.2 Cover letters: The basics
6.2.1 Structure
6.3 Tailoring
6.4 Referrals
6.5 Interview with Kristen Kehrer, data science instructor and course creator
How many times would you estimate you’ve edited your résumé?
What are common mistakes you see people make?
Do you tailor your résumé to the position you’re applying to?
What strategies do you recommend for describing jobs on a résumé?
What’s your final piece of advice for aspiring data scientists?
Summary
7 The interview: What to expect and how to handle it
7.1 What do companies want?
7.1.1 The interview process
7.2 Step 1: The initial phone screen interview
7.3 Step 2: The on-site interview
7.3.1 The technical interview
7.3.2 The behavioral interview
7.4 Step 3: The case study
7.5 Step 4: The final interview
7.6 The offer
7.7 Interview with Ryan Williams, senior decision scientist at Starbucks
What are the things you need to do to knock an interview out of the park?
How do you handle the times where you don’t know the answer?
What should you do if you get a negative response to your answer?
What has running interviews taught you about being an interviewee?
Summary
8 The offer: Knowing what to accept
8.1 The process
8.2 Receiving the offer
8.3 Negotiation
8.3.1 What is negotiable?
8.3.2 How much you can negotiate
8.4 Negotiation Tactics
8.5 How to choose between two “good” job offers
8.6 Interview with Brooke Watson Madubuonwu, senior data scientist at the ACLU
What should you consider besides salary when you’re considering an offer?
What are some ways you prepare to negotiate?
What do you do if you have one offer but are still waiting on another one?
What’s your final piece of advice for aspiring and junior data scientists?
Summary
Part 3: Settling into data science
9 The first months on the job
9.1 The first month
9.1.1 Onboarding at a large organization: A well-oiled machine
9.1.2 Onboarding at a small company: What onboarding?
9.1.3 Understanding and setting expectations
9.1.4 Knowing your data
9.2 Becoming productive
9.2.1 Asking questions
9.2.2 Building relationships
9.3 If you’re the first data scientist
9.4 When the job isn’t what was promised
9.4.1 The work is terrible
9.4.2 The work environment is toxic
9.4.3 Deciding to leave
9.5 Interview with Jarvis Miller, data scientist at Spotify
What were some things that surprised you in your first data science job?
What are some issues you faced?
Can you tell us about one of your first projects?
What would be your biggest piece of advice for the first few months?
Summary
10 Making an effective analysis
10.1 The request
10.2 The analysis plan
10.3 Doing the analysis
10.3.1 Importing and cleaning data
10.3.2 Data exploration and modeling
10.3.3 Important points for exploring and modeling
10.4 Wrapping it up
10.4.1 Final presentation
10.4.2 Mothballing your work
10.5 Interview with Hilary Parker, data scientist at Stitch Fix
How does thinking about other people help your analysis?
How do you structure your analyses?
What kind of polish do you do in the final version?
How do you handle people asking for adjustments to an analysis?
Summary
11 Deploying a model into production
11.1 What is deploying to production, anyway?
11.2 Making the production system
11.2.1 Collecting data
11.2.2 Building the model
11.2.3 Serving models with APIs
11.2.4 Building an API
11.2.5 Documentation
11.2.6 Testing
11.2.7 Deploying an API
11.2.8 Load testing
11.3 Keeping the system running
11.3.1 Monitoring the system
11.3.2 Retraining the model
11.3.3 Making changes
11.4 Wrapping up
11.5 Interview with Heather Nolis, machine learning engineer at T-Mobile
What does “machine learning engineer” mean on your team?
What was it like to deploy your first piece of code?
If you have things go wrong in production, what happens?
What’s your final piece of advice for data scientists working with engineers?
Summary
12 Working with stakeholders
12.1 Types of stakeholders
12.1.1 Business stakeholders
12.1.2 Engineering stakeholders
12.1.3 Corporate leadership
12.1.4 Your manager
12.2 Working with stakeholders
12.2.1 Understanding the stakeholder’s goals
12.2.2 Communicating constantly
12.2.3 Being consistent
12.3 Prioritizing work
12.3.1 Both innovative and impactful work
12.3.2 Not innovative but still impactful work
12.3.3 Innovative but not impactful work
12.3.4 Neither innovative nor impactful work
12.4 Concluding remarks
12.5 Interview with Sade Snowden-Akintunde, data scientist at Etsy
Why is managing stakeholders important?
How did you learn to manage stakeholders?
Was there a time where you had difficulty with a stakeholder?
What do junior data scientists frequently get wrong?
Do you always try to explain the technical part of the data science?
What’s your final piece of advice for junior or aspiring data scientists?
Summary
Chapters 9—12 resources
Books
Blogs
Part 4: Growing in your data science role
13 When your data science project fails
13.1 Why data science projects fail
13.1.1 The data isn’t what you wanted
13.1.2 The data doesn’t have a signal
13.1.3 The customer didn’t end up wanting it
13.2 Managing risk
13.3 What you can do when your projects fail
13.3.1 What to do with the project
13.3.2 Handling negative emotions
13.4 Interview with Michelle Keim, head of data science and machine learning at Pluralsight
When was a time you experienced a failure in your career?
Are there red flags you can see before a project starts?
How does the way a failure is handled differ between companies?
How can you tell if a project you’re on is failing?
How can you get over a fear of failing?
Summary
14 Joining the data science community
14.1 Growing your portfolio
14.1.1 More blog posts
14.1.2 More projects
14.2 Attending conferences
14.2.1 Dealing with social anxiety
14.3 Giving talks
14.3.1 Getting an opportunity
14.3.2 Preparing
14.4 Contributing to open source
14.4.1 Contributing to other people’s work
14.4.2 Making your own package or library
14.5 Recognizing and avoiding burnout
14.6 Interview with Renee Teate, director of data science at HelioCampus
What are the main benefits of being on social media?
What would you say to people who say they don’t have the time to engage with the community?
Is there value in producing only a small amount of content?
Were you worried the first time you published a blog post or gave a talk?
Summary
15 Leaving your job gracefully
15.1 Deciding to leave
15.1.1 Take stock of your learning progress
15.1.2 Check your alignment with your manager
15.2 How the job search differs after your first job
15.2.1 Deciding what you want
15.2.2 Interviewing
15.3 Finding a new job while employed
15.4 Giving notice
15.4.1 Considering a counteroffer
15.4.2 Telling your team
15.4.3 Making the transition easier
15.5 Interview with Amanda Casari, engineering manager at Google
How do you know it’s time to start looking for a new job?
Have you ever started a job search and decided to stay instead?
Do you see people staying in the same job for too long?
Can you change jobs too quickly?
What’s your final piece of advice for aspiring and new data scientists?
Summary
16 Moving up the ladder
16.1 The management track
16.1.1 Benefits of being a manager
16.1.2 Drawbacks of being a manager
16.1.3 How to become a manager
16.2 Principal data scientist track
16.2.1 Benefits of being a principal data scientist
16.2.2 Drawbacks of being a principal data scientist
16.2.3 How to become a principal data scientist
16.3 Switching to independent consulting
16.3.1 Benefits of independent consulting
16.3.2 Drawbacks of independent consulting
16.3.3 How to become an independent consultant
16.4 Choosing your path
16.5 Interview with Angela Bassa, head of data science, data engineering, and machine learning at iRobot
What’s the day-to-day life as a manager like?
What are the signs you should move on from being an independent contributor?
Do you have to eventually transition out of being an independent contributor?
What advice do you have for someone who wants to be a technical lead but isn’t quite ready for it?
What’s your final piece of advice to aspiring and junior data scientist?
Summary
Epilogue
Appendixes
Appendix A: Interview questions
A.1 Coding and software development
A.1.1 FizzBuzz
A.1.2 Tell whether a number is prime
A.1.3 Working with Git
A.1.4 Technology decisions
A.1.5 Frequently used package/library
A.1.6 R Markdown or Jupyter Notebooks
A.1.7 When should you write functions or packages/libraries?
A.1.8 Example manipulating data in R/Python
A.2 SQL and databases
A.2.1 Types of joins
A.2.2 Loading data into SQL
A.2.3 Example SQL query
A.2.4 Example SQL query continued
A.2.5 Data types
A.3 Statistics and machine learning
A.3.1 Statistics terms
A.3.2 Explain p-value
A.3.3 Explain a confusion matrix
A.3.4 Interpreting regression models
A.3.5 What is boosting?
A.3.6 Favorite algorithm
A.3.7 Training vs. test data
A.3.8 Feature selection
A.3.9 Deploying a new model
A.3.10 Model behavior
A.3.11 Experimental design
A.3.12 Flaws in experimental design
A.3.13 Bias in sampled data
A.4 Behavioral
A.4.1 Project that had the most impact
A.4.2 Data surprises
A.4.3 Previous job reflections
A.4.4 Senior person making a mistake based on data
A.4.5 Disagreements with teammates
A.4.6 Difficult problems
A.5 Brain teasers
A.5.1 Estimation
A.5.2 Combinatorics
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