Technics Publications

Data Scientist


Data Scientist: The Definitive Guide to Becoming a Data Scientist, by Dr. Zacharias Voulgaris

Learn what a data scientist is and how to become one.


Chapter 1: Data Science and Big Data

1.1 Digging into Big Data
1.2 Big Data Industries
1.3 Birth of Data Science
1.4 Key Points


Chapter 2: Importance of Data Science

2.1 History of the Data Science Field
2.2 The New Paradigms
2.3 The New Mindset and the Changes It Brings
2.4 Key Points


Chapter 3: Types of Data Scientists

3.1 Data Developers
3.2 Data Researchers
3.3 Data Creatives
3.4 Data Businesspeople
3.5 Mixed/Generic Type
3.6 Key Points


Chapter 4: The Data Scientist’s Mindset

4.1 Traits
4.2 Qualities and Abilities
4.3 Thinking
4.4 Ambitions
4.5 Key Points


Chapter 5: Technical Qualifications

5.1 General Programming
5.2 Scientific Background
5.3 Specialized Know-How
5.4 Key Points


Chapter 6: Experience

6.1 Corporate vs. Academic Experience
6.2 Experience vs. Formal Education
6.3 How to Gain Initial Experience
6.4 Key Points


Chapter 7: Networking

7.1 More than Just Professional Networking
7.2 Relationship with Academia
7.3 Relationship with the Business World
7.4 Key Points


Chapter 8: Software Used

8.1 Hadoop Suite and Friends
8.2 OOP Language
8.3 Data Analysis Software
8.4 Visualization Software
8.5 Integrated Big Data Systems
8.6 Other Programs
8.7 Key Points


Chapter 9: Learning New Things and Tackling Problems

9.1 Workshops
9.2 Conferences
9.3 Online Courses
9.4 Data Science Groups
9.5 Requirements Issues
9.6 Insufficient Know-How Issues
9.7 Tool Integration Issues
9.8 Key Points


Chapter 10: Machine Learning and the R Platform

10.1 Brief History of Machine Learning
10.2 The Future of Machine Learning
10.3 Machine Learning vs. Statistical Methods
10.4 Uses of Machine Learning in Data Science
10.5 Brief Overview of the R Platform
10.6 Resources for Machine Learning and R
10.7 Key Points


Chapter 11: The Data Science Process

11.1 Data Preparation
11.2 Data Exploration
11.3 Data Representation
11.4 Data Discovery
11.5 Learning from Data
11.6 Creating a Data Product
11.7 Insight, Deliverance and Visualization
11.8 Key Points


Chapter 12: Specific Skills Required

12.1 The Data Scientist’s Skill-Set in the Job Market
12.2 Expanding Your Current Skill-Set as a Developer
12.3 Expanding Your Current Skill-Set as a Statistician or Machine Learning Practitioner
12.4 Expanding Your Current Skill-Set as a Data Professional
12.5 Developing the Data Scientist’s Skill-Set as a Student
12.6 Key Points


Chapter 13: Where to Look for a Data Science Job

13.1 Contact Companies Directly
13.2 Professional Networks
13.3 Recruiting Sites
13.4 Other Methods
13.5 Key Points


Chapter 14: Presenting Yourself

14.1 Focus on the Employer
14.2 Flexibility and Adaptability
14.3 Deliverables
14.4 Differentiating Yourself from Other Data Professionals
14.5 Self-Sufficiency
14.6 Other Factors to Consider
14.7 Key Points


Chapter 15: Freelance Track

15.1 Pros and Cons of Being a Data Science Freelancer
15.2 How Long You Should Do It for
15.3 Other Relevant Services You Can Offer
15.4 Example of a Freelance Data Science Opportunity
15.5 Key Points


Chapter 16: Experienced Data Scientists Case Studies

16.1 Dr. Raj Bondugula
16.2 Praneeth Vepakomma
16.3 Key Points


Chapter 17: Senior Data Scientist Case Study

17.1 Basic Professional Information and Background
17.2 Views on Data Science in Practice
17.3 Data Science in the Future
17.4 Advice to New Data Scientists
17.5 Key Points


Chapter 18: Call for New Data Scientists

18.1 Ads for Entry-Level Data Scientists
18.2 Ads for Experienced Data Scientists
18.3 Ads for Senior Data Scientists
18.4 Online Job Searching Tips
18.5 Key Points

As our society transforms into a data-driven one, the role of the Data Scientist is becoming more and more important. If you want to be on the leading edge of what is sure to become a major profession in the not-too-distant future, this book can show you how.

Each chapter is filled with practical information that will help you reap the fruits of big data and become a successful Data Scientist:

  • Learn what big data is and how it differs from traditional data through its main characteristics: volume, variety, velocity, and veracity.
  • Explore the different types of Data Scientists and the skillset each one has.
  • Dig into what the role of the Data Scientist requires in terms of the relevant mindset, technical skills, experience, and how the Data Scientist connects with other people.
  • Be a Data Scientist for a day, examining the problems you may encounter and how you tackle them, what programs you use, and how you expand your knowledge and know-how.
  • See how you can become a Data Scientist, based on where you are starting from: a programming, machine learning, or data-related background.
  • Follow step-by-step through the process of landing a Data Scientist job: where you need to look, how you would present yourself to a potential employer, and what it takes to follow a freelancer path.
  • Read the case studies of experienced, senior-level Data Scientists, in an attempt to get a better perspective of what this role is, in practice.


At the end of the book, there is a glossary of the most important terms that have been introduced, as well as three appendices – a list of useful sites, some relevant articles on the web, and a list of offline resources for further reading.

Hear Zack talk about Data Scientist

About Zack

Dr. Zacharias Voulgaris was born in Athens, Greece. He studied Production Engineering and Management at the Technical University of Crete, shifted to Computer Science through a Masters in Information Systems & Technology, and then to Data Science through a PhD in Machine Learning. He has worked at Georgia Tech as a Research Fellow, at an e-marketing startup in Cyprus as an SEO manager, and as a Data Scientist in both Elavon (GA) and G2 Web Services (WA). He also was a Program Manager at Microsoft on a data analytics pipeline for Bing. Zacharias has authored several books on Data Science, mentors aspiring data scientists, and maintains a Data Science and AI blog. Currently, he works as a consultant at GLG.


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