Build a career in data science / Emily Robinson and Jacqueline Nolis.

Format:

Book

Author/Creator:

Robinson, Emily, author.

Nolis, Jacqueline, author.

Language:

English

Subjects (All):

Data mining--Vocational guidance.

Data mining.

Big data--Vocational guidance.

Big data.

Physical Description:

1 online resource (354 pages)

Edition:

1st edition

Place of Publication:

Shelter Island, New York : Manning, [2020]

System Details:

text file

Summary:

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.

Contents:

Intro

Copyright

Brief Table of Contents

Table of Contents

Preface

Acknowledgments

About This Book

About the Authors

About the Cover Illustration

Part 1. Getting started with data science

Chapter 1. What is data science?

1.1. What is data science?

1.2. Different types of data science jobs

1.3. Choosing your path

1.4. Interview with Robert Chang, data scientist at Airbnb

Summary

Chapter 2. Data science companies

2.1. MTC: Massive Tech Company

2.2. HandbagLOVE: The established retailer

2.3. Seg-Metra: The early-stage startup

2.4. Videory: The late-stage, successful tech startup

2.5. Global Aerospace Dynamics: The giant government contractor

2.6. Putting it all together

2.7. Interview with Randy Au, quantitative user experience researcher at Google

Chapter 3. Getting the skills

3.1. Earning a data science degree

3.2. Going through a bootcamp

3.3. Getting data science work within your company

3.4. Teaching yourself

3.5. Making the choice

3.6. Interview with Julia Silge, data scientist and software engineer at RStudio

Chapter 4. Building a portfolio

4.1. Creating a project

4.2. Starting a blog

4.3. Working on example projects

4.4. Interview with David Robinson, data scientist

Chapters 1-4 resources

Part 2. Finding your data science job

Chapter 5. The search: Identifying the right job for you

5.1. Finding jobs

5.2. Deciding which jobs to apply for

5.3. Interview with Jesse Mostipak, developer advocate at Kaggle

Chapter 6. The application: Résumés and cover letters

6.1. Résumé: The basics

6.2. Cover letters: The basics

6.3. Tailoring

6.4. Referrals

6.5. Interview with Kristen Kehrer, data science instructor and course creator

Summary.

Chapter 7. The interview: What to expect and how to handle it

7.1. What do companies want?

7.2. Step 1: The initial phone screen interview

7.3. Step 2: The on-site 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

Chapter 8. The offer: Knowing what to accept

8.1. The process

8.2. Receiving the offer

8.3. Negotiation

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

Chapter 5-8 resources

Part 3. Settling into data science

Chapter 9. The first months on the job

9.1. The first month

9.2. Becoming productive

9.3. If you're the first data scientist

9.4. When the job isn't what was promised

9.5. Interview with Jarvis Miller, data scientist at Spotify

Chapter 10. Making an effective analysis

10.1. The request

10.2. The analysis plan

10.3. Doing the analysis

10.4. Wrapping it up

10.5. Interview with Hilary Parker, data scientist at Stitch Fix

Chapter 11. Deploying a model into production

11.1. What is deploying to production, anyway?

11.2. Making the production system

11.3. Keeping the system running

11.4. Wrapping up

11.5. Interview with Heather Nolis, machine learning engineer at T-Mobile

Chapter 12. Working with stakeholders

12.1. Types of stakeholders

12.2. Working with stakeholders

12.3. Prioritizing work

12.4. Concluding remarks

12.5. Interview with Sade Snowden-Akintunde, data scientist at Etsy

Chapters 9-12 resources

Part 4. Growing in your data science role

Chapter 13. When your data science project fails

13.1. Why data science projects fail.

13.2. Managing risk

13.3. What you can do when your projects fail

13.4. Interview with Michelle Keim, head of data science and machine le- earning at Pluralsight

Chapter 14. Joining the data science community

14.1. Growing your portfolio

14.2. Attending conferences

14.3. Giving talks

14.4. Contributing to open source

14.5. Recognizing and avoiding burnout

14.6. Interview with Renee Teate, director of data science at HelioCampus

Chapter 15. Leaving your job gracefully

15.1. Deciding to leave

15.2. How the job search differs after your first job

15.3. Finding a new job while employed

15.4. Giving notice

15.5. Interview with Amanda Casari, engineering manager at Google

Chapter 16. Moving up the ladder

16.1. The management track

16.2. Principal data scientist track

16.3. Switching to independent consulting

16.4. Choosing your path

16.5. Interview with Angela Bassa, head of data science, data engineeri- ing, and machine learning at iRobot

Chapters 13-16 resources

Blogs

Epilogue

Appendix. Interview questions

A.1. Coding and software development

A.2. SQL and databases

A.3. Statistics and machine learning

A.4. Behavioral

A.5. Brain teasers

Index

List of Figures

List of Tables.

Notes:

Description based on print version record.

Includes index.

Includes bibliographical references and index.

ISBN:

9781638350156

1638350159

OCLC:

1184041193

Publisher Number:

9781617296246AU