Technics Publications

Data Scientist Bedside Manner

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Data Scientist Bedside Manner: Redefining your Organization with Data, by Dr. Zacharias Voulgaris and Yunus Emrah Bulut

Embrace the holistic set of skills and experiences required for data science success. (HINT: It’s much more than just knowing math!)


Chapter 1: Data Science Landscape

Business and data science
Data scientist progression
Data scientist skills
Hard skills
Soft skills


Chapter 2: Data Science and AI

About data science
Data science insights and data products
Data science pipeline
What data science is not
Data science projects
About AI
AI as automation
AI algorithms
What AI is not
Data science versus AI
Leveraging AI for data science
AI-based data science models
AI-based data generation
Key points


Chapter 3: Where AI Fits

The way of data science
Natural language processing
Computer vision
Key points


Chapter 4: Questions

Business questions and problems
Types of business questions
Examples of questions
Issues with business questions
Data science questions and tasks
Types of data science questions
Examples of questions
Types of data science tasks
From a business to a data science question
Finding a target variable
Selecting a data science method
“What If” questions and sensitivity analysis
The parameter search space
Useful “what if” questions
The butterfly effect
Robust answers
Key points


Chapter 5: Key Qualities

Standard qualities
Emerging qualities
Additional qualities
Tackling a business problem
Key points


Chapter 6: How Data Science Transforms Business

Data science impact
Reflections on a centuries-old industry
How to acquire domain knowledge
Data scientist’s role in digital transformation
Key points


Chapter 7: Hiring Data Science Professionals

Start with the team lead
The technical framework for hiring data scientists
1. Initial phone screening
2. Live online coding challenge
3. A machine learning task
4. Personal Interview
Key points


Chapter 8: Managing a Data Science Project

Agile methods
Scrum framework
Iterative nature of a data science project
Ambiguities and unknowns
Fitting a data science project into scrum
Key points


Chapter 9: Managing Data Scientists

Evaluate a data science project
Evaluate a system as a whole
Let data scientists experiment
Learn by doing
Generalists versus specialists
Key points


Chapter 10: Interview Highlights

Interview structure
Interview questions
Individuals interviewed
Data science-specific material
Skills and attributes
Value of data science
Building a data science team
AI’s role
Remaining relevant
Business-specific material
Business acumen
Domain knowledge
Business processes related to data science
Facilitating business decisions
Tips for data scientists
Tips for business people
Key points


Chapter 11: Educational Resources

Selecting educational resources
Types of educational resources
Criteria for selecting an educational resource
Educational resources to avoid
Options for educational resources
Educational resources for data science
Educational resources for AI
General educational resources
Key points


Chapter 12: Putting it Together

The confusion between data science and AI
How AI fits in the whole picture
Business-savvy data scientist
How data science transforms business
Hiring data scientists
How to manage a data science project
Evaluating and managing data scientists
Educational resources
Key points

Know what it takes to become a star data scientist, and how data science compares with and leverages other disciplines such as artificial intelligence (AI). Explore how data science adds value by focusing on business questions and how to graduate from being a good technical professional to becoming an invaluable member of a business team.

For those of us who are not data scientists, learn how to best leverage data science skills within your organization, how to hire a data scientist, and how to evaluate the outcome of a data science project.

The approach provided in this book is supported by the rich experiences of the authors, combined with findings from interviews with top data science professionals.

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.

About Yunus

Yunus Emrah Bulut was born in Amasya, Turkey. After he studied Computer Science in Bilkent University, he has worked as a computer scientist at several corporations including Turkey’s biggest telecom operator and the Central Bank of Turkey. After he completed his Master of Science degree at the Economics department of the Middle East Technical University (METU), he worked several years in the research department of the Central Bank of Turkey as a research economist. More recently, he has started to work as a Data Science consultant for companies in Turkey and USA. He is also a Data Science instructor at Datajarlabs and Data Science mentor in Thinkful.


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