The Top 11 Skills Data Scientists Need

Did you know the data science field is projected to grow over thirty percent in the next decade? As organizations realize the value Big Data unlocks, the need for data scientists who can uncover its insights increases day by day—and their skills are in high demand. Whether you are a student or a professional looking to switch careers, understanding what skills data scientists need can help you take the plunge into this rewarding career. 

Nicholas Papenburg wasn’t sure what skills data scientists needed; he just knew he loved data and answering big questions. He enrolled at BloomTech to learn all the skills required for data scientist positions—his dream job.

By working through BloomTech’s Data Science course, sprint by sprint, he picked up everything he needed to transition from being a stay-at-home-dad to a data scientist. “Going into this new field of work and having all the tools that I need for the job that I have is awesome,” he says. “Am I set up for this job because of BloomTech? Yes, I am. I am good to go. I'm ready to go.”

Nicholas’s transition into data science would have been much more challenging if he wasn’t prepared. We want you to be prepared, too. That’s why here, we’re outlining the most important skills data scientists need to succeed.

What is Data Science?

In the era of Big Data, data science is everywhere. From targeting your Instagram ads to unlocking your phone via facial recognition, it helps individuals and organizations make decisions. 

So what is data science exactly? Data science is the study of handling large volumes of data to uncover patterns and information via modern tools and techniques. It drives business decisions and solves complex problems in small businesses and multinational organizations alike. 

A data scientist is primarily a data wrangler. They wear many hats—including those of statistician, mathematician, and computer scientist—to process, clean, and analyze large sets of data. 

Let’s take a look at some of the core skills required in a day in the life of a data scientist. These skills can help you build your portfolio as a professional and help you succeed as a data scientist.

The Most In-Demand Skills: Data Scientists, Data Analysts, and More

The use of data has increased exponentially in almost every field and is predicted to keep growing. The demand for data scientists has similarly skyrocketed over the past few years, making it one of the fastest-growing fields in the U.S. Almost every type of organization—from Fortune 500 companies to nonprofits—wants data scientists, and they pay generously for their skills: The median salary of a data scientist in the U.S. exceeds $100,000.

Since the term data science describes a wide, multidisciplinary field, data science roles vary significantly across industries. To succeed, then, you need a variety of skills to land a job. While not every data scientist needs the same data science skill set, developing a strong foundation of data science skills will help you adapt and thrive in your career.

Here are our top 11 skills required for data scientists.

1. Python

From big tech corporations to niche communities on the internet, Python is everywhere. 

Python’s popularity is fuelled by its simple syntax (which makes it a perfect language for beginners) and its incredible versatility. Python is also easily scalable, meaning it could be used in very complex projects as you grow and wouldn’t become redundant once you are no longer a beginner.

Python is well-suited for data scientists. While its popularity alone makes it essential for data scientists to learn, the vast libraries and deep learning support communities available for the language makes it a key skill not to be missed. It also has data visualization capabilities, leading 84% of data scientists in the U.S. to use Python as their primary programming language.

2. SQL and Databases 

SQL, short for structured query language, is another must-know programming language. It is primarily used to handle data within databases. Unlike other programming languages used to build software, SQL is used for data manipulation within relational databases. 

Data scientists work with large sets of data often extracted from databases. This makes relational database management a foundational skill to have. There’s a reason more than 70% of all developers in the U.S. use SQL: Oftentimes, creating an SQL query is the first step of an evaluation in data science. 

Even with the development of NoSQL technology—non-tabular databases useful for storing unstructured data—SQL is still one of the most-used programming languages, and one you need for your data science career. 

3. Data Visualization

As a data scientist, you need to convey the insights you gained from your analysis. This is where data visualization comes in. Data visualization involves using visual aids such as charts and graphs to break down the complex data insights into more digestible pieces. 

A good data scientist uses data visualization to communicate the key insights effectively to everyone, including technical and non-technical people in an organization. This helps explain data-driven suggestions and decisions to individuals less proficient in data science, making it easier to cooperatively develop business strategies and plans. 

4. Linear Algebra and Statistical Analysis

Statistical analysis is the basis of data science. It makes sense, then, that you need a strong foundation in statistics and math to succeed in this field. 

Don’t let that chase you away if you’re not already a math whiz. While data science is a highly methodical and logical field, you do not need to come from a background in math to become a data scientist. With some effort and the right mindset, you’ll be able to gain the baseline math knowledge necessary for your work.

As a data scientist, you may find yourself working with several unknown variables while solving real-world data problems. 

Machine learning is one example of how data science and innovative technologies are based on math. This means linear algebra, statistics, and multivariate calculus are used extensively in machine learning models. Learning these data science math skills can set you up to work in areas such as machine learning, with applications in speech recognition, medical diagnosis, and predictive text.

That’s not all. Learning the required mathematics and statistics concepts can also help you later learn and apply advanced concepts, such as deep learning and computer vision.

5. Machine Learning

More than 2.5 quintillion bytes of data are produced every day. It’s way beyond human capabilities to handle this volume without the use of sophisticated technology and techniques. 

Here’s where machine learning comes in. Using machine learning, it is possible to enable computers to solve problems and analyze data without explicitly programming them. Machine learning models analyze large volumes of data and extract insights from them using neural networks. 

Data scientists use machine learning algorithms to build predictive models which can learn for themselves. In short, you can use past data to predict future possibilities. Since machine learning evolved from statistics, understanding basic concepts such as decision trees and logistic regression models can help you move on to more advanced models like Random Forest and CatBoost. 

BloomTech’s Data Science course teaches Machine Learning and the other data scientist skills in this article!

6. Deep Learning and ML Ops (Machine Learning Operations)

If you’ve used Smart Assistants and seen self-driven cars, you have firsthand experience of deep learning. Deep learning is a machine learning training model which attempts to emulate the human brain. Deep learning algorithms are built vertically as layers on top of each other (hence the term “deeper”), and the layers are connected to each other by neural networks.

Sounds fascinating, right? To work, this automated process of unsupervised learning requires a large volume of data training. Deep learning helps models improve with each extra unit of data. With the amount of data generated growing exponentially, this means infinite possibilities.

ML Ops, on the other hand, uses machine learning in the development of applications with the aim of making them more reliable and productive. The field itself is more experimental at the moment, but its possibilities for the future are endless. 

Both Deep Learning and ML Ops are emerging fields. Adding these data science skills can give you an edge when applying to competitive data science jobs. 

7. Computer Vision

Millions of years of evolution have enabled our eyes and minds to interpret visual information from the world around us. Computer vision is an attempt to emulate that. Instead of a lifetime of context we use to train our eyes, a large amount of data is used to train machines using computer vision.

Computer vision represents an unprecedented opportunity for data science. The flood of visual information from cameras, smartphones, and other visual devices could provide incredible insights for many industries via data science. Learning computer vision gives you a unique addition to your skill set that helps you to stand out to employers.

8. Natural Language Processing (NLP)

If you have used voice-to-text software, digital assistants, or voice-operated GPS, then you are already familiar with Natural Language Processing, also known as NLP. NLP focuses on teaching computers to understand and interpret text and spoken language much like humans do. 

Languages are complex. Think about a time you tried learning a new language and struggled with things like homophones, metaphors, and sarcasm. It’s the same with the computers, but they do not have the lifetime of context you had. 

What they do have: data scientists.

Data scientists build NLPs on deep learning and machine learning models based on convolutional neural networks and recurrent neural networks. The field is just scratching the surface of what’s possible, thanks to NLP. For example, data scientists use NLP to uncover insights from the enormous volume of data available on social media. 

9. Critical Thinking and Problem Solving

Technical skills are important for the multidisciplinary field of data science, but they’re not all you need. Soft skills ensure you know when and how to apply your technical expertise.

Data scientists are problem solvers. Critical thinking and problem-solving skills enable them to objectively analyze problems, understand what questions to ask, and see the big picture from different perspectives. Being able to question assumptions, find alternative options, and identify bias will help make you an invaluable member of your future team. 

Critical thinking and problem solving are not only valued in the field of data science. Employers consider it to be one of the most important skills an individual could have in any industry. 

10. Communication

Good communication skills are crucial for you to succeed as a data scientist, since individuals in this role often work within large organizations and teams. Data scientists need to convey the insights they uncover so their discoveries bring value to the business. 

Ultimately, analyzing and interpreting data won't be helpful if you are unable to communicate your findings to both technical and non technical professionals. 

11. Curiosity and a Learner’s Mindset

Data scientists harbor a deep love of data and finding answers to questions. Data science is quite a challenging field, and nurturing the curiosity and love for data is essential for you to stay motivated. Intellectual curiosity helps data scientists thrive since it inspires them to look beyond initial results and assumptions to get better insights from their data sets.

As data science is multidisciplinary, there’s plenty to learn at any point in your career. In fact, no two data science roles look the same. A data scientist working in marketing would need a different data science skill set compared to a data scientist in healthcare.

If you are transitioning into data science from another career background, this could work in your favor—employers would appreciate the fresh perspective and industry knowledge you bring, in addition to your data science skills. That is why having a learner's mindset and a healthy dose of intellectual curiosity can help you keep the spark burning as you work your way through the industry.

Take the Next Step in Your Data Science Career With BloomTech

While it is exciting to be a data scientist in this information age, working toward gaining all the skills needed can seem daunting. Fortunately, BloomTech’s Data Science course is designed to get you career-ready in a fraction of the time of a university degree. 

"Since starting to ponder getting into data science, choosing BloomTech, and now getting hired, I cannot say one bad thing. I doubled my salary, I will be working less, I will be working remote,” says Emily Flener, who graduated from BloomTech’s Data Science course. She is now working as a data scientist. “I will be working on teams to make a difference and solve problems in an industry where I get paid my worth. And that's only the beginning."

BloomTech’s Data Science course equips you with the skills necessary for you to start a career in data analysis, machine learning, data engineering, and more, with the curriculum covering all the skills in this article. Our flexible Data Science program adjusts to your schedule so you can learn part-time, full-time, any time.

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