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Data Scientist: The Definitive Guide to Becoming a Data Scientist

Book Description

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.

Table of Contents

  1. Introduction
  2. Chapter 1 Data Science and Big Data
    1. 1.1 Digging into Big Data
    2. 1.2 Big Data Industries
    3. 1.3 Birth of Data Science
    4. 1.4 Key Points
  3. Chapter 2 Importance of Data Science
    1. 2.1 History of the Data Science Field
    2. 2.2 The New Paradigms
    3. 2.3 The New Mindset and the Changes It Brings
    4. 2.4 Key Points
  4. Chapter 3 Types of Data Scientists
    1. 3.1 Data Developers
    2. 3.2 Data Researchers
    3. 3.3 Data Creatives
    4. 3.4 Data Businesspeople
    5. 3.5 Mixed/Generic Type
    6. 3.6 Key Points
  5. Chapter 4 The Data Scientist’s Mindset
    1. 4.1 Traits
    2. 4.2 Qualities and Abilities
    3. 4.3 Thinking
    4. 4.4 Ambitions
    5. 4.5 Key Points
  6. Chapter 5 Technical Qualifications
    1. 5.1 General Programming
    2. 5.2 Scientific Background
    3. 5.3 Specialized Know-How
    4. 5.4 Key Points
  7. Chapter 6 Experience
    1. 6.1 Corporate vs. Academic Experience
    2. 6.2 Experience vs. Formal Education
    3. 6.3 How to Gain Initial Experience
    4. 6.4 Key Points
  8. Chapter 7 Networking
    1. 7.1 More than Just Professional Networking
    2. 7.2 Relationship with Academia
    3. 7.3 Relationship with the Business World
    4. 7.4 Key Points
  9. Chapter 8 Software Used
    1. 8.1 Hadoop Suite and Friends
    2. 8.2 OOP Language
    3. 8.3 Data Analysis Software
    4. 8.4 Visualization Software
    5. 8.5 Integrated Big Data Systems
    6. 8.6 Other Programs
    7. 8.7 Key Points
  10. Chapter 9 Learning New Things and Tackling Problems
    1. 9.1 Workshops
    2. 9.2 Conferences
    3. 9.3 Online Courses
    4. 9.4 Data Science Groups
    5. 9.5 Requirements Issues
    6. 9.6 Insufficient Know-How Issues
    7. 9.7 Tool Integration Issues
    8. 9.8 Key Points
  11. Chapter 10 Machine Learning and the R Platform
    1. 10.1 Brief History of Machine Learning
    2. 10.2 The Future of Machine Learning
    3. 10.3 Machine Learning vs. Statistical Methods
    4. 10.4 Uses of Machine Learning in Data Science
    5. 10.5 Brief Overview of the R Platform
    6. 10.6 Resources for Machine Learning and R
    7. 10.7 Key Points
  12. Chapter 11 The Data Science Process
    1. 11.1 Data Preparation
    2. 11.2 Data Exploration
    3. 11.3 Data Representation
    4. 11.4 Data Discovery
    5. 11.5 Learning from Data
    6. 11.6 Creating a Data Product
    7. 11.7 Insight, Deliverance and Visualization
    8. 11.8 Key Points
  13. Chapter 12 Specific Skills Required
    1. 12.1 The Data Scientist’s Skill-Set in the Job Market
    2. 12.2 Expanding Your Current Skill-Set as a Programmer / SW Developer
    3. 12.2.1 OO Programmer
    4. 12.2.2 Software Developer
    5. 12.2.3 Other Programming-Related Career Tracks
    6. 12.3 Expanding Your Current Skill-Set as a Statistician or Machine Learning Practitioner
    7. 12.3.1 Statistics Background
    8. 12.3.2 Machine Learning / A.I. Background
    9. 12.3.3 Mixed Background
    10. 12.4 Expanding Your Current Skill-Set as a Data-Related Professional
    11. 12.4.1 Database Administrator
    12. 12.4.2 Data Architect/Modeler
    13. 12.4.3 Business Intelligence Analyst
    14. 12.5 Developing the Data Scientist’s Skill-Set as a Student
    15. 12.6 Key Points
  14. Chapter 13 Where to Look for a Data Science Job
    1. 13.1 Contact Companies Directly
    2. 13.2 Professional Networks
    3. 13.3 Recruiting Sites
    4. 13.4 Other Methods
    5. 13.5 Key Points
  15. Chapter 14 Presenting Yourself
    1. 14.1 Focus on the Employer
    2. 14.2 Flexibility and Adaptability
    3. 14.3 Deliverables
    4. 14.4 Differentiating Yourself from Other Data Professionals
    5. 14.5 Self-Sufficiency
    6. 14.6 Other Factors to Consider
    7. 14.7 Key Points
  16. Chapter 15 Freelance Track
    1. 15.1 Pros and Cons of Being a Data Science Freelancer
    2. 15.2 How Long You Should Do It for
    3. 15.3 Other Relevant Services You Can Offer
    4. 15.4 Example of a Freelance Data Science Opportunity
    5. 15.5 Key Points
  17. Chapter 16 Experienced Data Scientists Case Studies
    1. 16.1 Dr. Raj Bondugula
    2. 16.2 Praneeth Vepakomma
    3. 16.3 Key Points
  18. Chapter 17 Senior Data Scientist Case Study
    1. 17.1 Basic Professional Information and Background
    2. 17.2 Views on Data Science in Practice
    3. 17.3 Data Science in the Future
    4. 17.4 Advice to New Data Scientists
    5. 17.5 Key Points
  19. Chapter 18 Call for New Data Scientists
    1. 18.1 Ads for Entry-Level Data Scientists
    2. 18.2 Ads for Experienced Data Scientists
    3. 18.3 Ads for Senior Data Scientists
    4. 18.4 Online Job Searching Tips
    5. 18.5 Key Points
  20. Final Words
  21. Glossary of Computer and Big Data Terminology
  22. Appendix 1 Useful Websites
  23. Appendix 2 Relevant Articles
  24. Appendix 3 Offline Resources
  25. Index