Build a Career in Data Science

Description

Summary 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. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. Table of Contents: PART 1 - GETTING STARTED WITH DATA SCIENCE 1. What is data science? 2. Data science companies 3. Getting the skills 4. Building a portfolio PART 2 - FINDING YOUR DATA SCIENCE JOB 5. The search: Identifying the right job for you 6. The application: Résumés and cover letters 7. The interview: What to expect and how to handle it 8. The offer: Knowing what to accept PART 3 - SETTLING INTO DATA SCIENCE 9. The first months on the job 10. Making an effective analysis 11. Deploying a model into production 12. Working with stakeholders PART 4 - GROWING IN YOUR DATA SCIENCE ROLE 13. When your data science project fails 14. Joining the data science community 15. Leaving your job gracefully 16. Moving up the ladder Build a Career in Data Science brief contents contents preface acknowledgments about this book Who should read this book How this book is organized: a roadmap liveBook discussion forum about the authors Emily Robinson Jacqueline Nolis about the cover illustration Saint-Sauver 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 Analytics 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 Summarizing academic degrees 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 Summarizing data science bootcamps 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 Books Blog posts 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 5.3 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 Chapter 5–8 resources Books Blog posts and courses 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 Chapters 13–16 resources Books Blogs Epilogue Appendix—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 index Numerics A B C D E F G H I J K L M N O P Q R S T U V W Y Z