9 Qualities the Best Data Scientists Share: Do You Have What It Takes?

Experts called data scientist the sexiest job of the 21st century in 2012.

They knew the best data scientists would lead the world forward by harnessing the growing power of Big Data.

Nearly 10 years later, data science is also considered one of the most misunderstood fields. What separates top data scientists from the mediocre?

Today, data science gurus are still in high demand at leading companies but finding them still poses quite a challenge. That’s why the Bureau of Labor Statistics expects operations research jobs, such as data scientists, to grow by 25% over the next decade.

If you’re thinking of becoming a data scientist, it’s important to know what qualities the best data scientists share so you can decide if it’s the right industry for you. Some skills you can practice and learn, but others either come naturally or don’t.

This post will cover topics like:

• What a data scientist is and what they do
• 10 qualities the best data scientists share
• A few famous data scientists you should know
• How to become a data scientist with bootcamp

What is a Data Scientist and What Do They Do Best?

Data scientists live on the fence between statistics and programming.

A data scientist uses their knowledge of mathematics, probability, and statistics to draw insights from patterns in data and visualizations. However, they also use advanced programming experience to collect, manipulate, and transform massive data sets, prepare data for machine learning, and manage data infrastructure.

As a highly technical role, businesses hire data scientists to develop predictive algorithms for guiding operational decisions and understanding future possibilities. Data scientists might also fill research and development roles for machine learning apps and user-facing programs.

That’s why many companies look for data science gurus with overlapping skills in other fields like those in the chart below.

Data scientists know how to use data for:

• Answering questions
• Predicting the future
• Solving problems
• Uncovering the unknown

The best data scientists are natural problem-solvers who understand that things aren’t always as they seem and perfection is a farce.

Top 9 Qualities the Best Data Scientists Share

A data scientist’s job is often extremely tedious: searching for answers in data, developing useful processes, and running tests.

Not only do data scientists need the right skills to complete tasks, but they also need the right personality and interpersonal skills.

Think you’re ready to put your skills to work learning how to become the best data scientist of tomorrow? NYC Data Science Bootcamp supplies live projects and hands-on experience so you can build the vital yet less-tangible skills that you can’t learn in classrooms.

1. Mastery of Python and R Programming

Anyone can learn Python and R programming, but today’s top data scientists have mastered these languages and – as with fluency in other types of language – use them every day.

In fact, the best data scientists in the world have even developed new programming languages.

You don’t need to pioneer a language to succeed in data science. However, comfort with advanced Python and R is an absolute must.

2. Proficiency in Statistics and Data Analysis

Data scientists apply probability and statistics to massive data sets using Python and R programming. The goal is to solve problems or answer questions – not produce armchair research.

That’s why most data scientists aren’t expert mathematicians, nor do they hold degrees in statistics. However, they are proficient in basic formulas and theories of statistics because these lay the foundation of data analysis.

3. Natural Curiosity and Skepticism

Do you have an advanced degree in psychology, journalism, or natural sciences? With the right skills, you’d make a better data scientist than you might think.

The best data scientists know it’s their job to analyze business operations and the associated data objectively.

You look at the known facts to find unknown factors. This requires a mind that questions everything – and critically so.

4. A Realistic Approach to Risk and Where It Comes From

Businesses hire top data scientists to help them predict, study, learn from, and – most importantly – avoid risk. Even the most innovative machine learning technology at Google and Facebook will no doubt get sold to businesses for those purposes.

Today’s most famous data scientists respect the huge risks associated with “the unknown.” They use their knowledge and data to understand the worst-case scenarios, mitigate the damage, and find the bright spots to shape positive futures.

Career data scientists tend to stick within a certain industry once they find their footing. They learn what businesses of various niches, sizes, and ages within the industry need from data science so they can hone their skills.

5. Prioritizing Answers Over Perfection

Data scientists with the most achievements on paper – the best data scientists in the world – are not perfectionists. Those that keep their perfectionist qualities tucked away for the right situations.

As a data scientist, you must acknowledge there’s no such thing as a perfect answer or solution to anything in life.

Otherwise, you end up stuck in analysis paralysis overthinking and re-analyzing in circles – like a Java infinite loop error.

6. Understanding Why Details Matter for Broader Goals

Data scientists spend their days wrapped up in granular details of code and data. They don’t brush off outliers – they investigate and understand them.

However, top data scientists don’t get wrapped up in the granular details either. They know micro analysis matters but only as it fits into the project’s macro goals.

That’s why experience is critical: Only learning from your mistakes and wins can train you to identify important vs. meaningless details (within reason). NYC Data Science Bootcamp offers this type of first-hand experience with live projects and more, teaching the important trial-and-error side of data science.

7. A Power to Read the Room and Work Their Interpersonal Skills

At most organizations, data scientists work in high-level positions where multiple departments and/or job roles rely on their insights. Your project outcomes could supply actionable insights for everyone from sales reps and marketing managers to C-suite and even outside shareholders.

In other words, everyone has their own agenda for their work. Top data scientists understand how to “read the room” in that respect and wield some charisma as they produce solutions for everyone.

8. Excellent Storytelling and Knack for Teaching

Building on the point above, data science gurus also acknowledges they work in an incredibly technical and complex field. They make the most confusing concepts digestible to coworkers who only know Python as a type of snake.

You also need a talent for telling a story through data and how/why you arrived at the answer. That’s why today’s most famous data scientists are also avid writers.

9. Intuition for Human Behavior and What It Means for Data

You become less of a perfectionist as you see the role human behavior plays in data science. Top data scientists aren’t confident in their solutions – they’re confident in their ability to explain the process and identify all the potential answers.

You’ll succeed in data science with a natural intuition for fluid human behavior along with the data that represents it – because that’s all data really is.

Famous Data Scientists You Should Know

If you want to learn how to become the best data scientist, start by following these top data scientists and their achievements. Data science is a dynamic industry, so you can learn quite a bit through their successes and mistakes. (The best data scientists know mistakes are inevitable.)

Dean Abbott

https://twitter.com/deanabb

Dean Abbott is the chief data scientist and co-founder of SmarterHQ, a cross-channel personalization platform for eCommerce marketers.

Over his 30-plus year career in data science, Abbott has authored Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst and coauthored IBM’s SPSS Modeler Cookbook.

He also shares his decades of knowledge and experience by teaching workshops on machine learning and data science to professionals.

Nando de Freitas

https://twitter.com/NandoDF

Meet the top data scientist for Google’s DeepMind machine learning team: Nando de Freitas.

You can find his research for the Canadian Institute for Advanced Research (CIFAR) and DeepMind on topics like Deep Learning, object recognition, and communication networks via Google Scholar.

Prior to leading Google’s machine learning wing and joining CIFAR, de Freitas taught advanced neural networks at Oxford University and has received several awards throughout his career.

Vivian Zhang

https://twitter.com/NYCDataSci

Vivian Zhang is the founder of NYC Data Science Academy and co-founder of SupStat, a data science consultancy offering customized training and project help to industry-leading organizations.

Over her impressive career, Zhang has honed her expertise in data science’s most important programming languages and created one of the largest data science communities: NYC Open Data Meetup with conferences, meetups, and newsletters.

All of this earned Zhang elite recognition from Forbes in 2016 as one of the top-nine women leading the data science industry.

Fei-Fei Li

https://twitter.com/drfeifei

Dr. Fei-Fei Li is a Professor of Computer Science at Stanford. She’s also the co-director of Stanford’s Human-Centered AI research department, where Li and her team strive to set the standards for government policies and education – ethical data science is a staple throughout Li’s career.

Li also created the ImageNet visual recognition database during her tenure at Princeton – a project often cited as the catalyst for Deep Learning as we know it today.

You can browse her extensive collection of nearly 200 writings and research papers as well.

Andrew Ng

https://twitter.com/AndrewYNg

Andrew Ng boasts a prestigious background as the former head of Baidu’s AI Group, a global AI research group uniting top data scientists and gurus from HQs in Beijing, Seattle, and Silicon Valley.

His Baidu leadership and background as the founder of the Google Brain Project make Andrew Ng one of the best data scientists around the world today.

Currently, Ng leads the way in online education as the co-founder and chairman of Coursera and an adjunct professor of Computer Science at Stanford.

Sebastian Thrun

https://twitter.com/SebastianThrun

Sebastian Thrun pushes the boundaries of modern transportation as the CEO of Kitty Hawk, a company leading advances in electric air mobility and vertical flight.

He earned recognition by founding the Google X Project and winning a DARPA contest for his self-driving robotic vehicles.

Today, Thrun is also the founder and president of Udacity, an adjunct professor at Stanford, and the author of countless papers on topics like probabilistic algorithms and robotics.

John Myles White

https://twitter.com/johnmyleswhite

You might recognize John Myles White as the lead engineer of Facebook’s data infrastructure.

White also pioneered the Julia programming language as a core developer early in his career, later working in MIT’s Computer Science and AI Lab.

You can read White’s extensive collection of papers and books like Machine Learning for Hackers and Bandit Algorithms for Website Optimization.

Those with degrees outside of technology and computer science will appreciate that White is a psychology graduate, highlighting the importance of unique perspectives in today’s best data scientists.

How to Become the Best Data Scientist with a Bootcamp

NYC Data Science Academy consistently ranks as one of the best data scientist bootcamps on SwitchUp for five years running and has graduated over 2,000 students, so you can enroll with confidence.

In just 12 weeks, you’ll learn the skills and gain the hands-on experience you’ll need to apply for entry-level data science roles with topics like:

• Writing functions, generating graphs, basic statistical models, dynamic reports, and more in R Shiny and Knitr for data analysis.
  
  
• Scraping web data and working with data structures, analytics, and visualizations in Python and iPython for data analysis like NumPy, SciPy, pandas, and matplotlib.
  
  
• Theoretical foundations of machine learning algorithms and practical application techniques of machine learning with R and Python.
• Data mining, linear models, non-linear models, and more using R for machine learning.
  
  
• Data modeling, supervised learning algorithms, and unsupervised learning algorithms using Python for machine learning.
  
  
• Exploring future career paths in Big Data and Deep Learning with Hadoop, Spark, Hive, TensorFlow, Natural Language Processing, and more.
  
  
• Solving a real business’s operational problem with the data science skills, tools, and knowledge you’ve learned – your capstone project.

As one of the best data scientist bootcamps, NYC Data Science Academy also prepares you for job searches with lifelong career assistance like:

• A community of over 7,000 alumni and working data scientists, students, and experts.
  
  
• Over 500 hiring partners for potential live projects, internships, and job opportunities.
  
  
• 1:1 mock interviews and mock coding challenges during your job search.
  
  
• Expert resume, LinkedIn reviews, and advice for building your portfolio.
  
  
• Events and workshops with corporate partners and top data scientists.

If you have the right qualifications, NYC Data Science Bootcamp can help you learn how to be one of the world’s next top data scientists.