How to Become a Data Analyst
John Wiley & Sons Inc (Verlag)
978-1-394-20223-2 (ISBN)
In How to Become a Data Analyst: My Low-Cost, No Code Roadmap for Breaking into Tech, data analyst and analytics consultant Annie Nelson walks you through how she took the reins and made a dramatic career change to unlock new levels of career fulfilment and enjoyment. In the book, she talks about the adaptability, curiosity, and persistence you’ll need to break free from the 9-5 grind and how data analytics—with its wide variety of skills, roles, and options—is the perfect field for people looking to refresh their careers.
Annie offers practical and approachable data portfolio-building advice to help you create one that’s manageable for an entry-level professional but will still catch the eye of employers and clients. You’ll also find:
Deep dives into the learning journey required to step into a data analytics role
Ways to avoid getting lost in the maze of online courses and certifications you can find online—while still obtaining the skills you need to be competitive
Explorations of the highs and lows of Annie’s career-change journey and job search—including what was hard, what was easy, what worked well, and what didn’t
Strategies for using ChatGPT to help you in your job search
A must-read roadmap to a brand-new and exciting career in data analytics, How to Become a Data Analyst is the hands-on tutorial that shows you exactly how to succeed.
ANNIE NELSON is a self-taught data analyst and Tableau consultant, as well as a popular commentator on TikTok (@anniesanalytics) where she shares her experiences and journey through the world of data analytics. She works remotely, balancing her self-scheduled work with frequent trips abroad and growing her substantial following on social media.
Preface xiii
Introduction xix
Part I The Fun Part
Chapter 1 Is Data Analytics Right for Me? 3
What Does a Data Analyst Do Every Day? 4
Hours/Time 6
In-Person Data Jobs 9
What Makes a Good Analyst? 10
Planning 12
Organization 13
Critical Thinking/Strategy 14
Collaboration/Communication 15
What Tools Should I Learn? 17
Excel/Google Sheets 17
SQL 19
Tableau/Power BI 21
Python 24
R 25
Which Entry-Level Tech Job Is Right for Me? 25
What’s Next 29
Chapter 2 Understanding the Paths into Data 31
How Hard Is It to Become a Data Analyst? 32
What Are My Options for Getting into Data Analytics? 34
Transitioning from an Analyst-Adjacent Role 35
Getting a Degree 35
Boot Camps 36
When a Boot Camp May Be the Right Option for You 37
How to Pick a Good Boot Camp 38
DIY Approach 40
How I Decided on the DIY Approach 41
Chapter 3 Designing Your Data Analyst Roadmap 45
Can You Shows Me Your Data Analyst Roadmap? 46
Building Your Roadmap 46
Step 1: Skill Development 47
Step 2: Building a Portfolio 49
Step 3: Getting Yourself Ready to Job Search 52
How Do I Choose the Best Course? 53
What Makes a Good Course 55
Learning Styles 55
Budget 56
Support 57
Interests 58
Time Constraints 59
Getting Started for Free 60
When Not to Pick a Course: How to Avoid Course Hopping 61
Chapter 4 My Experience with Data Analytics Courses 63
The Beginning 63
The Google Certificates Course 64
Learning SQL 65
Learning Tableau and R 68
Finishing the Course 70
What Came Next 72
Changing Careers 72
Course Hopping: When Is Taking Another Course Worth It? 73
Part II The Scary Part 77
Chapter 5 Introduction to Portfolios 79
What Is a Data Analytics Portfolio? 79
Can I See an Example? 80
Why Do I Need a Portfolio? 81
As an Analyst 81
As a Job Seeker 82
If I Have Experience from Another Job, Do I Still Need a Portfolio? 83
Chapter 6 Portfolio Project FAQ 85
How Do I Find Free Data? 86
Maven Analytics 87
Real World Fake Data 89
Your Data 89
Data from Me! 90
SQL Practice 91
Other Places 92
Can You Tell Me More about Completing Projects? 93
How Do I Get Started on Projects? 93
Does My Project Need to Be Original and Industry Specific? 95
How Do I Know When a Project Is Ready? 96
Where Do I Publish and Store My Work? 96
How Many Projects Do I Need? 98
Should I Share My Work Publicly? 99
Project Time! 100
Chapter 7 Portfolio Project Handbook 101
Project Levels: What Separates a Beginner from an Intermediate Project? 102
First Project 102
Beginner Project 103
Intermediate Project 103
Regular Tableau User 104
Guided Projects 104
New Year’s Eve Resolutions Project 104
Case Study: New Year’s Eve Resolutions Project 105
Semi-Structured Case Study with Hints 106
Final Thoughts 108
Help Desk Project 108
Case Study: Help Desk Project 109
Semi-Structured Prompts 109
Pizza Sales Project 111
Case Study: Pizza Sales Project 111
Dirty Data + Case Study 112
Dirty Data + Case Study + Hints 113
Clean Data + Case Study 114
Semi-Structured Case Study with Hints 115
Busy Times 116
Pizzas During Peak Periods 116
Best- and Worst-Selling Pizzas 116
Average Order Value 117
Seating Capacity 117
Final Thoughts 118
SQL Project Creation Advice 119
From the Portfolio to the Job Search 121
Getting in the Mindset for Projects 122
Part III The Hard Part 125
Chapter 8 Starting Your Job Search 127
How Do I Know When I Am Ready to Start My Job Search? 127
Where and How Should I Look for Jobs? 129
Searching Posts 129
Job Titles 130
Where Can I Find Salary Information? 131
What Is the Data Analyst Career Progression? 131
Chapter 9 Résumé Building and Setting Your Public Image 137
How Do I Write a Résumé? 138
Length 138
Technical Skills 141
Relevant History 142
Formatting 142
Use Metrics 143
How Do I Optimize My LinkedIn? 144
History 144
Connections 146
Headline 149
Profile Photo 150
Can You Tell Me How to Network? 151
What Is Networking (and What Is It Not)? 151
Networking and Messaging on LinkedIn 153
Messaging Jobs Directly 154
Networking Events 156
Interviewing 157
Bonus Tip: An Idea for Your First LinkedIn Post 158
Chapter 10 Stages of Data Interviews 161
Why Do Interviews Take So Long? 161
Can You Tell Me More about the Interview Stages? 162
Phone Screen 163
Meeting the Hiring Manager 165
Behavioral Interview 166
Technical Interview 166
Panel Interview 169
Culture Fit 170
Follow-up 171
How I Handled Some Common How-Tos 172
Tell Me about Yourself 173
How to Come Up with Good Questions 175
Resources 180
Teal 180
Maven Analytics 181
Content Creators/Small Businesses 182
Working with Data Creators 183
Using AI 184
Chapter 11 How to Use ChatGPT to Aid Your Job Search 185
Writing a Résumé 185
Writing Cover Letters 186
Practicing for Interviews 186
Phone Screen 186
Technical Interview 189
Behavioral Interview 189
Writing Follow-Up Emails 191
Be Specific 192
Chapter 12 My Job Search 195
“Open to Work?” 195
Beginning to Search 197
Getting Reponses (and Rejections) 200
Pivoting 202
Interviewing 204
Decision Day 207
Part IV The Bonus Part 209
Chapter 13 After the Job Offer 211
Starting the Job 212
Dealing with Imposter Syndrome 213
Steps to Success 214
What It’s Like Working Remotely 215
Some Things About Tech That Surprised Me 217
121s 217
Home Office Stipend 217
Company Party/Offsites 218
Meetings 218
Referrals 219
Layoffs 220
Problem‐ Solving 221
Travel 222
Data Has Changed My Life 224
Chapter 14 Preparing for/Recovering from a Layoff 225
Don’t Ignore Red Flags 225
Resumes and Networking—Restarting the Job Search 226
Updating my Portfolio 229
The Layoff 230
Adjusting for Your Situation 235
Closing Thoughts 236
Appendix A Data Analytics Roadmap Checklist 239
Appendix B Tableau Tips 241
Appendix C My Data Analyst Journey 249
Acknowledgments 257
About the Author 259
Index 261
| Erscheinungsdatum | 15.12.2023 |
|---|---|
| Verlagsort | New York |
| Sprache | englisch |
| Maße | 152 x 229 mm |
| Gewicht | 295 g |
| Themenwelt | Sachbuch/Ratgeber ► Beruf / Finanzen / Recht / Wirtschaft ► Bewerbung / Karriere |
| Informatik ► Theorie / Studium ► Algorithmen | |
| Wirtschaft ► Betriebswirtschaft / Management | |
| ISBN-10 | 1-394-20223-7 / 1394202237 |
| ISBN-13 | 978-1-394-20223-2 / 9781394202232 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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