Muutke küpsiste eelistusi

Build A Career in Data Science [Pehme köide]

4.37/5 (349 hinnangut Goodreads-ist)
,
  • Formaat: Paperback / softback, 354 pages, kõrgus x laius x paksus: 235x186x22 mm, kaal: 660 g, Illustrations
  • Ilmumisaeg: 22-Jun-2020
  • Kirjastus: Manning Publications
  • ISBN-10: 1617296244
  • ISBN-13: 9781617296246
Teised raamatud teemal:
Build A Career in Data ScienceSuurem pilt
  • Formaat: Paperback / softback, 354 pages, kõrgus x laius x paksus: 235x186x22 mm, kaal: 660 g, Illustrations
  • Ilmumisaeg: 22-Jun-2020
  • Kirjastus: Manning Publications
  • ISBN-10: 1617296244
  • ISBN-13: 9781617296246
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781617296246.html
Märksõnad:

Build a Career in Data Science is the top guide to help readers get their first data science job, then quickly becoming a senior employee.

Industry experts Jacqueline Nolis and Emily Robinson lay out the soft skills readers need alongside their technical know-how in order to succeed in the field.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.



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.

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.

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

About the reader
For readers who want to begin or advance a data science career.

About the author
Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor.

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
Preface xvii
Acknowledgments xix
About this book xxi
About the authors xxiv
About the cover illustration xxvii
PART 1 GETTING STARTED WITH DATA SCIENCE
1(70)
1 What is data science?
3(15)
1.1 What is data science?
5(5)
Mathematics/statistics
6(1)
Databases/programming
7(2)
Business understanding
9(1)
1.2 Different types of data science jobs
10(4)
Analytics
11(1)
Machine learning
11(1)
Decision science
12(1)
Related jobs
13(1)
1.3 Choosing your path
14(1)
1.4 Interview with Robert Chang, data scientist at Airbnb
15(3)
What was your first data science journey'?
15(1)
What should people look for in a data science job?
16(1)
What skills do you need to be a data scientist?
16(2)
2 Data science companies
18(53)
2.1 MTC: Massive Tech Company
19(3)
Your team: One of many in MTC
19(1)
The tech: Advanced, but siloed across the company
20(1)
The pros and cons of MTC
21(1)
2.2 HandbagLOVE: The established retailer
22(2)
Your team: A small group struggling to grow
22(1)
Your tech: A legacy stack that's starting to change
23(1)
The pros and cons of HandbagLOVE
23(1)
2.3 Seg-Metra: The early-stage startup
24(4)
Your team (what team?)
25(1)
The tech: Cutting-edge technology that's taped together
26(1)
Pros and cons of Seg-Metra
26(2)
2.4 Videory: The late-stage, successful tech startup
28(3)
The team: Specialized but with room to move around
28(1)
The tech: Trying to avoid getting bogged down by legacy code
29(1)
The pros and cons of Videory
29(2)
2.5 Global Aerospace Dynamics: The giant government contractor
31(2)
The team: A data scientist in a sea of engineers
31(1)
The tech: Old, hardened, and on security lockdown
32(1)
The pros and cons of GAD
32(1)
2.6 Putting it all together
33(1)
2.7 Interview with Randy Au, quantitative user experience researcher at Google
34(4)
Are there big differences between large and small companies?
34(1)
Are there differences based on the industry of the company?
35(1)
What's your final piece of advice for beginning data scientists?
35(2)
Getting the skills
37(1)
3.1 Earning a data science degree
38(6)
Choosing the school
39(3)
Getting into an academic program
42(1)
Summarizing academic degrees
43(1)
3.2 Going through a bootcamp
44(3)
What you learn
44(2)
Cost
46(1)
Choosing a program
46(1)
Summarizing data science bootcamps
47(1)
3.3 Getting data science work within your company
47(2)
Summarizing learning on the job
49(1)
3.4 Teaching yourself
49(2)
Summarizing self-teaching
50(1)
3.5 Making the choice
51(1)
3.6 Interview with Julia Silge, data scientist and software engineer at RStudio
52(4)
Before becoming a data scientist, you worked in academia; how have the skills learned there helped you as a data scientist?
52(1)
When deciding to become a data scientist, what did you use to pick up new skills?
53(1)
Did you know going into data science what kind of work you wanted to be doing?
53(1)
What would you recommend to people looking to get the skills to be a data scientist?
53(2)
A Building a portfolio
55(1)
4.1 Creating a project
56(4)
Finding the data and asking a question
56(3)
Choosing a direction
59(1)
Filling out a GitHub README
60(1)
4.2 Starting a blog
60(3)
Potential topics
60(1)
Logistics
61(2)
4.3 Working on example projects
63(2)
Data science freelancers
63(1)
Training a neural network on offensive license plates
64(1)
4.4 Interview with David Robinson, data scientist
65(6)
How did you start blogging?
66(1)
Are there any specific opportunities you have gotten from public work?
66(1)
Are there people you think would especially benefit from doing public work?
66(1)
How has your view on the value of public work changed over time?
66(1)
How do you come up with ideas for your data analysis posts?
67(1)
What's your final piece of advice for aspiring and junior data scientists?
67(4)
PART 2 FINDING YOUR DATA SCIENCE JOB
71(64)
5 The search: Identifying the right job for you
73(28)
5.1 Finding jobs
74(7)
Decoding descriptions
75(2)
Watching for red flags
77(1)
Setting your expectations
77(1)
Attending meetups
78(2)
Using social media
80(1)
5.2 Deciding which jobs to apply for
81(2)
5.3 Interview with Jesse Mostipak, developer advocate at Haggle
83(3)
What recommendations do you have for starting a job search?
83(1)
How can you build your network?
83(1)
What do you do if you don't feel confident applying to data science jobs?
83(1)
What would you say to someone who thinks "I don't meet the full list of any job's required qualifications?"
84(1)
What's your final piece of advice to aspiring data scientists?
84(1)
The application: Resumes and cover letters
85(1)
6.1 Resume: The basics
86(8)
Structure
88(5)
Deeper into the experience section: generating content
93(1)
6.2 Cover letters: The basics
94(2)
Structure
95(1)
6.3 Tailoring
96(1)
6.4 Referrals
97(2)
6.5 Interview with Kristen Kehrer, data science instructor and course creator
99(2)
How many times would you estimate you to edited your resume?
99(1)
What are common mistakes you see people make?
99(1)
Do you tailor your resume to the position you're applying to?
100(1)
What strategies do you recommend for describing jobs on a resume?
100(1)
What's your final piece of advice for aspiring data scientists?
100(1)
7 The interview: What to expect and how to handle it
101(18)
7.1 What do companies want?
102(2)
The interview process
103(1)
7.2 Step 1: The initial phone screen interview
104(2)
7.3 Step 2: The on-site interview
106(7)
The technical interview
108(3)
The behavioral interview
111(2)
7.4 Step 3: The case study
113(2)
7.5 Step 4: The final interview
115(1)
7.6 The offer
116(1)
7.7 Interview with Ryan Williams, senior decision scientist at Starbucks
117(2)
What are the things you need to do knock an interview out of the park?
117(1)
How do you handle the times where you don't know the answer?
117(1)
What should you do if you get a negative response to your answer?
118(1)
What has running interviews taught you about being an interviewee?
118(1)
8 The offer: Knowing what to accept
119(16)
8.1 The process
120(1)
8.2 Receiving the offer
120(2)
8.3 Negotiation
122(5)
What is negotiable?
122(3)
How much you can negotiate
125(2)
8.4 Negotiation tactics
127(1)
8.5 How to choose between two "good" job offers
128(1)
8.6 Interview with Brooke Watson Madubuonwu, senior data scientist at the ACLU
129(6)
What should you consider besides salary when you're considering an offer?
130(1)
What are some ways you prepare to negotiate?
130(1)
What do you do if you have one offer but are still waiting on another one?
130(1)
What's your final piece of advice for aspiring and junior data scientists?
131(4)
PART 3 SETTLING INTO DATA SCIENCE
135(78)
9 The first months on the job
137(18)
9.1 The first month
138(6)
Onboarding at a large organization: A well-oiled machine
138(1)
Onboarding at a small company: What onboarding?
139(1)
Understanding and setting expectations
139(2)
Knowing your data
141(3)
9.2 Becoming productive
144(4)
Asking questions
145(1)
Building relationships
146(2)
9.3 If you're the first data scientist
148(1)
9.4 When the job isn't what was promised
149(3)
The work is terrible
149(1)
The work environment is toxic
150(1)
Deciding to leave
151(1)
9.5 Interview with Jarvis Miller, data scientist at Spotify
152(3)
What were some things that surprised you in your first data science job?
153(1)
What are some issues you faced?
153(1)
Can you tell us about one of your first projects?
153(1)
What would be your biggest piece of advice for the first few months?
154(1)
10 Making an effective analysis
155(19)
10.1 The request
158(2)
10.2 The analysis plan
160(2)
10.3 Doing the analysis
162(7)
Importing and cleaning data
162(2)
Data exploration and modeling
164(2)
Important points for exploring and modeling
166(3)
10.4 Wrapping it up
169(3)
Final presentation
170(1)
Mothballing your work
171(1)
10.5 Interview with Hilary Parker, data scientist at Stitch Fix
172(2)
How does thinking about other people help your analysis?
172(1)
How do you structure your analyses?
172(1)
What kind of polish do you do in the final version?
172(1)
How do you handle people asking for adjustments to an analysis?
173(1)
11 Deploying a model into production
174(39)
11.1 What is deploying to production, anyway?
175(2)
11.2 Making the production system
177(10)
Collecting data
178(1)
Building the model
178(1)
Serving models with APIs
179(1)
Building an API
180(2)
Documentation
182(1)
Testing
183(1)
Deploying an API
184(3)
Load testing
187(1)
11.3 Keeping the system running
187(2)
Monitoring the system
187(1)
Retraining the model
188(1)
Making changes
189(1)
11.4 Wrapping up
189(1)
11.5 Interview with Heather Nolis, machine learning engineer at T-Mobile
189(4)
What does "machine learning engineer" mean on your team?
189(1)
What was it like to deploy yourfirst piece of code?
190(1)
If you have things go wrong in production, what happens?
190(1)
What's your final piece of advice for data scientists working with engineers?
191(1)
Working xvith stakeholders
192(1)
12.1 Types of stakeholders
193(4)
Business stakeholders
193(1)
Engineering stakeholders
194(1)
Corporate leadership
195(1)
Your manager
196(1)
12.2 Working with stakeholders
197(6)
Understanding the stakeholder's goals
197(2)
Communicating constantly
199(2)
Being consistent
201(2)
12.3 Prioritizing work
203(3)
Both innovative and impactful work
204(1)
Not innovative but still impactful work
205(1)
Innovative but not impactful work
205(1)
Neither innovative nor impactful work
206(1)
12.4 Concluding remarks
206(1)
12.5 Interview with Sade Snowden-Akintunde, data scientist at Etsy
207(6)
Why is managing stakeholders important?
207(1)
How did you learn to manage stakeholders?
207(1)
Was there a time where you had difficulty with a stakeholder?
207(1)
What do junior data scientists frequently get wrong?
208(1)
Do you always try to explain the technical part of the data science?
208(1)
What's your final piece of advice for junior or aspiring data scientists?
208(5)
PART 4 GROWING IN YOUR DATA SCIENCE ROLE
213(67)
13 When your data science project fails
215(13)
13.1 Why data science projects fail
216(5)
The data isn't what you wanted
217(1)
The data doesn't have a signal
218(2)
The customer didn't end up wanting it
220(1)
13.2 Managing risk
221(1)
13.3 What you can do when your projects fail
222(4)
What to do with the project
223(1)
Handling negative emotions
224(2)
13.4 Interview with Michelle Keim, head of data science and machine learning at Pluralsight
226(2)
When was a time you experienced a failure in your career?
226(1)
Are there red flags you can see before a project starts?
226(1)
How does the way a failure is handled differ between companies?
226(1)
How can you tell if a project you're on is failing?
227(1)
How can you get over a fear of failing?
227(1)
14 Joining the data science community
228(18)
14.1 Growing your portfolio
230(1)
More blog posts
230(1)
More projects
231(1)
14.2 Attending conferences
231(4)
Dealing with social anxiety
234(1)
14.3 Giving talks
235(4)
Getting an opportunity
236(3)
Preparing
239(1)
14.4 Contributing to open source
239(3)
Contributing to other people's work
240(1)
Malong your own package or library
241(1)
14.5 Recognizing and avoiding burnout
242(1)
14.6 Interview with Renee Teate, director of data science at Helio Campus
243(3)
What are the main benefits of being on social media?
243(1)
What would you say to people, who say they don `I have the lime to engage with the community?'
244(1)
Is there value in producing only a small amount of content?
244(1)
Were you worried the first time you published a blag post or gave a talk?
244(2)
15 Leaving your job gracefully
246(15)
15.1 Deciding to leave
247(3)
Take stock of your learning progress
247(1)
Cheek your alignment with your manager
248(2)
15.2 How the job search differs after your first job
250(2)
Deciding what you want
250(1)
Interviewing
251(1)
15.3 Finding a new job while employed
252(2)
15.4 Giving notice
254(4)
Considering a counteroffer
255(1)
Telling your team
255(2)
Making the transition easier
257(1)
15.5 Interview with Amanda Casari, engineering manager at Google
258(3)
How do you know it's time to start looking/or a new job?
258(1)
Have you ever started a Job search and decided to stay instead?
258(1)
Do you see people slaying in the same job for loo long?
258(1)
Can you change jobs too quickly?
259(1)
What's-your final piece of adviee for aspiring and new data scientists?
259(2)
16 Moving up the ladder
261(19)
16.1 The management track
263(4)
Benefits of being a manager
264(1)
Drawbacks of being a manager
264(1)
How to become a manager
265(2)
16.2 Principal data scientist track
267(4)
Benefits of being a principal data scientist
268(1)
Drawbacks of being a principal data scientist
269(1)
How to become a principal data scientist
270(1)
16.3 Switching to independent consulting
271(3)
Benefits of independent consulting
272(1)
Drawbacks of independent consulting
272(1)
How to become an independent consultant
273(1)
16.4 Choosing your path
274(1)
16.5 Interview with Angela Bassa, head of data science, data engineering, and machine learning at iRobot
275(5)
What's the day-to-day life as a manager like?
275(1)
What are the signs you should move on from being an independent contributor?
275(1)
Do you have to eventually transition out of being an independent contributor?
275(1)
What advice do you have for someone who wants to be a technical lead but isn't quite ready for it?
276(1)
What's your final piece of advice to aspiring and junior data scientist?
276(4)
Epilogue 280(2)
Appendix Interview questions 282(29)
Index 311
Jacqueline Nolis is a data science consultant and co-founder of Nolis, LLC, with a PhD in Industrial Engineering. Jacqueline has spent years mentoring junior data scientists on how to work within organizations and grow their careers.

 

Emily Robinson is a senior data scientist at Warby Parker, and holds a Master's in Management. Emily's academic background includes the study of leadership, negotiation, and experiences of underrepresented groups in STEM.