English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 14h 54m | 6.29 GB
Full of useful advice, real-case scenarios, and contributions from professionals in the industry.
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.
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.
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
Table of Contents
1 PART 1
2 What is data science
3 Databases programming
4 Different types of data science jobs
5 Choosing your path
6 Data science companies
7 HandbagLOVE – The established retailer
8 Seg-Metra – The early-stage startup
9 Videory – The late-stage, successful tech startup
10 Global Aerospace Dynamics – The giant government contractor
11 Putting it all together
12 Getting the skills
13 Choosing the school
14 Getting into an academic program
15 Going through a bootcamp
16 Getting data science work within your company
17 Teaching yourself
18 Interview with Julia Silge, data scientist and software engineer at RStudio
19 Building a portfolio
20 Choosing a direction
21 Starting a blog
22 Working on example projects
23 Interview with David Robinson, data scientist
24 PART 2
25 The search – Identifying the right job for you
26 Decoding descriptions
27 Attending meetups
28 Deciding which jobs to apply for
29 The application – Résumés and cover letters
30 Structure
31 Deeper into the experience section – generating content
32 Cover letters – The basics
33 Referrals
34 The interview – What to expect and how to handle it
35 Step 1 – The initial phone screen interview
36 Step 2 – The on-site interview
37 The technical interview
38 The behavioral interview
39 Step 3 – The case study
40 The offer
41 The offer – Knowing what to accept
42 Negotiation
43 How much you can negotiate
44 Negotiation tactics
45 Interview with Brooke Watson Madubuonwu, senior data scientist at the ACLU
46 PART 3
47 The first months on the job
48 Understanding and setting expectations
49 Knowing your data
50 Becoming productive
51 Building relationships
52 If you’re the first data scientist
53 The work environment is toxic
54 Interview with Jarvis Miller, data scientist at Spotify
55 Making an effective analysis
56 The request
57 Doing the analysis
58 Important points for exploring and modeling
59 Wrapping it up
60 Deploying a model into production
61 Making the production system
62 Building an API
63 Deploying an API
64 Keeping the system running
65 Working with stakeholders
66 Working with stakeholders
67 Communicating constantly
68 Prioritizing work
69 Concluding remarks
70 PART 4
71 When your data science project fails
72 The data doesn’t have a signal
73 Managing risk
74 Interview with Michelle Keim, head of data science and machine le- earning at Pluralsight
75 Joining the data science community
76 Attending conferences
77 Giving talks
78 Contributing to open source
79 Recognizing and avoiding burnout
80 Leaving your job gracefully
81 How the job search differs after your first job
82 Finding a new job while employed
83 Giving notice
84 Interview with Amanda Casari, engineering manager at Google
85 Moving up the ladder
86 The management track
87 Principal data scientist track
88 Switching to independent consulting
89 Choosing your path
90 Epilogue
91 Appendix. Interview questions – A.1. Coding and software development
92 Appendix. Interview questions – A.1.5. Frequently used package library
93 Appendix. Interview questions – A.2. SQL and databases
94 Appendix. Interview questions – A.3. Statistics and machine learning
95 Appendix. Interview questions – A.3.7. Training vs. test data
96 Appendix. Interview questions – A.4. Behavioral
97 Appendix. Interview questions – A.5. Brain teasers
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