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The field of data science is having a renaissance right now. All that talk about artificial intelligence? The people behind that tech are, more often than not, data scientists. And if you’re here, then you’re probably curious about that profession. What are the most important skills for a data scientist? How can you acquire these required skills?

You’re in the right place. Here’s our list of the skills you need to become a data scientist and thrive.

Data scientist technical skills

Programming languages

Let’s just start with the data scientist skills you’re expecting. You’re going to need to know some programming languages regardless of where you end up working — in cloud computing, artificial intelligence, cybersecurity, etc. In terms of skills required for data scientists, these are your bedrock.

Which languages? We’ll mention three specific ones:

  • Python

PythonIs Programming Hard? Make It Easy with Python is the language in which the majority of machine learning algorithms are written. It’s robust, one of the easiest languages to learn, and has a wide community behind it. Every day, someone seems to come out with a piece of tech augmenting this language’s data science capabilities.

  • R

R focuses on statistical analysis and data visualization. And while that means it has perhaps a narrower focus than Python, it’s still valuable. After all, you can look at the section below to see how important stats are to data science.

  • SQL

If you’re going to interact with data (which as a data scientist, you will), you’re going to be working with databases. SQL is the language that will let you do that. With it, you can pull data from a repository and transform it into a format useful to whatever new, wild project you come up with. And good news: you can learn it for free.

Statistics and probability theory

Data science helps build systems that seem to understand you intimately. That great song recommendation? That on-point suggestion for the next show to binge? It’s uncanny how well a computer knows you. 

But here’s a secret: the predictive algorithms powered by data science don’t know what show you’ll like or what song to play next. They are just quite adept at making remarkably accurate guesses. This is where a data scientist’s knowledge of statistics, probability, and queueing theory come in.

Skills in probability theory, linear algebra, descriptive statistics, regression analysis, and time series analysis allow data scientists to build models that turn data into actual insight. Applying these mathematical approaches uncovers correlations, patterns, and trends. 

This math know-how is the foundation of the repeatable, statistical analysis techniques that help data scientists build the generative algorithms that differentiate data science from data analysis.

A love for applying these skills was even the impetus for one of our grads to join TripleTen’s Data Science program in the first place. Read his story hereChanging His Career, Not His Company: Gor Mikayelyan’s TripleTen Story.

Gor Mikayelyan joined TripleTen, mastered a new field, and found the vocation he had been seeking, all without leaving Amazon.

Machine learning

Now we get to what makes people excited about data science. Machine learning algorithms grab the most headlines. It’s easy to see why — you can train a computer to do a thing! It learns!

Of course, you can’t just tell a computer to learn in your most commanding voice and expect it to comply. As a skill for data scientists, this actually can be broken down into unique sub-skills:

  • Interpreting models

You’ve got the model — congrats! Now you just have to know how to figure out what it’s saying.

  • Reinforcement learning

If the model is starting to follow the right path, you need to be able to make sure it keeps going. Reinforcement learning provides positive feedback to make sure the model works as intended.

  • Anomaly detection

A data scientist needs to weed out bad data to make sure outcomes aren’t skewed by hidden outliers.

  • Deep learning

Data scientists should be familiar with deep learning frameworks such as TensorFlow and PyTorch, as they can power neural networks.

  • Model evaluation

Knowing how to evaluate and balance a model for accuracy, precision, recall, and other parameters is vital.

But if you want to hear more about this field from someone who actually works as a machine learning engineer, check out this testimonial from a TripleTen grad:

Big data

The information that modern companies process is truly staggering. Here’s an example: Spotify tracks multiple context-reliant listening data points and abstracts them to generate diverse recommendations. And this service, worldwide, has 574 million active users each month. Let’s say that each user skips three songs, and that’s the exclusive interaction they have with the app throughout one whole month. That is already 1.7 billion data points. So when we say data science skills in big data are important, we mean big amounts of data.

But, as with machine learning, there are specific data scientist skills that will help techies handle this deluge of data:

  • Programming languages

We said it above, and we’ll repeat it: these are bedrock skills. R and Python specifically have libraries for big data processing.

  • Compliance and cybersecurity

Likely, this data is going to be about people. That means that when you handle big data, it will contain sensitive information that needs to be protected from bad actors. As part of this, you’ll have to be up-to-date on the latest regulations that can even occasionally be industry-specific.

  • Data warehousing

All that data has to come from and go somewhere. That makes tech such as Snowflake or Amazon Redshift vital for a data scientist who wants to focus on big data.

Data preparation and visualization

These data scientist skills include loading, cleaning, integrating, and exploring dataA Brief Guide to Data Loading, Cleaning, and Exploration (Part 1). They’re all about the first stages of the process. Data science starts with mountains of unstructured data. It’s messy, noisy, and inconsistent, and in its raw state, is unusable. It needs refining.

This is where these skills come in. The data needs to be loaded to be manipulable. Then, it needs to be cleansed of duplicate or erroneous information, merged from diverse formats into one unified dataset, and examined for patterns to inform the models that will be built from it. Following that, data visualization can help ensure consistency and help a data scientist check the model’s efficacy. And that is just the most superficial description of data preparation. There are numerous other skills that dovetail with one another within this category. 

Data know-how is fundamental for data scientists. These skills make raw data usable, leading to more accurate and better-trained models.

Algorithms

This is where data scientist skills approach wizardry, and algorithms are essential to the work that data scientists do. They are the decision-making engines formed by neural networks, deep learning, and natural language processing. So that makes skills in algorithm generation both crucial and uniquely poised to bring you into the most exciting segments of data science.

The problem is that, since algorithm skills get so technical, we get into terms that might make your eyes glaze over. Hyperparameter tuning with Bayesian optimization! Dimensionality reduction!

So, to make this section useful to someone just beginning to explore data science, we’ll keep it out of the weeds.

In short, algorithms form the basis of a wide swath of the technical work that data scientists do on a daily basis. They’re really, really important.

Depending on your position, you might apply one type of algorithm more often than another. But no matter what, a good bootcamp will give you a knowledge base that will let you get started with algorithms.

Data scientist soft skills

Communication

We will keep recommending communication skills. Not only are they among the most crucial data science skills, they are crucial skills period. Throughout tech, you’re going to have to refine your ability to formulate and express your ideas. But let’s get specific for data science.

Take one tech skill from above: data visualization. You will generate an attractive illustration describing how well your model is doing, how it’s meeting business objectives, etc. While the visuals themselves will indeed be convincing, the way you’re going to get their core meaning across is by being clear, direct, and unambiguous in how you describe them. This is where skills in communication play a major role.

And as a data scientist, you’re going to be interacting with numerous stakeholders. You’re going to have to know how to come to consensus, how to tell stories with data, and how to convey the insights you’re gathering. Importantly, you will do this with people who both share your technical understanding and those who don’t, so catering your style to each group will also be vital.

Business awareness

Business awareness is what helps data scientists take their wild algorithm-crafting skills, their programming knowledge, and their aptitude for data and align it all with larger business goals. Can you abstract your individual work and see how it feeds into larger business objectives? Then, at a basic level, you’ve got skills in business awareness.

This can also take the form of domain knowledge, though. If you’re coming from a previous career, then you can take that experience and merge it with new data science skills. For example TripleTen grad Evgeniia UnzhakovaHow an Immigrant Landed a Career in the US: Evgeniia Unzhakova’s TripleTen Story had been a teacher for over a decade. When she switched from teaching to tech, she didn’t change her industry; she’s now tackling data for a local university.

Evgeniia, TripleTen grad, is currently working on a natural language processing project.

And if you’re entirely new to tech, that gives you an opportunity to develop this domain knowledge as you choose. Interested in video games? That industry needs data scientists. Curious about using your data science skills to benefit the world? Like one of our gradsData Science as the Smart Investment: Jacques Diambra Odi’s TripleTen Story, you can lend your skills to a project that can help in disaster relief.

Problem solving

For different professions, this skill expresses itself differently. So, naturally, data science skills in problem solving are specific to the field.

For example, data scientists may be asked to develop a model that increases engagement on a new video platform. The first task here is to break down this overarching goal into discrete, specific tasks. Then, a data scientist needs to see how each smaller aspect can be addressed with a data solution. And then, they need to choose the proper algorithmic approach and tune their models to achieve their specific target. On top of that, all of this has to be tracked and documented for stakeholders, so a data scientist needs to choose the proper metrics that will be significant, transparent, and insightful.

At each stage (and many, many more we haven’t included here for the sake of brevity), problem solving is a vital skill for data scientists. But, to not put too serious a note on this, problem solving within data science also allows these techies to take a creative approach; each smaller task will vary in scope and subject and ask data scientists to think up new, unexpected solutions. Problem solving with data keeps the profession fresh.

Flexibility

This is one of the skills data scientists use nearly every day. If, in a morning stand-up meeting, they discover that a component is behind or someone on their data team needs a hand, skills in adaptability will allow data scientists to fill small gaps that open up in the normal course of work. That will help the project and team at large operate more smoothly and thus, more successfully.

Likewise, after communicating with stakeholders or users, something within an algorithm might need tweaking or refocusing. Flexibility will ensure that this feedback can be integrated easily so that the product better meets user or business needs.

But flexibility also helps make a data scientist resilient to change and employable throughout their career.

Tech is constantly evolving and developing, so new approaches to the day-to-day work of a data scientist might appear.

If data workers remain receptive to these shifting, novel pieces of technology, they can find new ways of solving problems more efficiently and get new lines in their resumes. This soft skill can lead to more hard skills, and both can lead to better and more interesting job offers.

Curiosity

This is a complementary skill to flexibility. Curiosity can help you discover new approaches when old ideas just aren’t cutting it. By practicing active engagement in advances in the field, you can find fresh ideas to bring to your team. Maybe some new methodology is perfect to get rid of the latest thorny bug you’re trying to squash. Maybe a new library has the capabilities you’ve been struggling to develop. In any case, without curiosity, you’re not going to stumble on these workflow upgrades.

And just like with flexibility, curiosity can also make you more and more employable as you progress in your career. Uncover something new you want to dig into? Go for it. Your curiosity can lead you to develop expertise you might not have pursued otherwise. That means you’ll develop additional aptitudes naturally, and this is something that employers love to see.

But there’s another dimension to curiosity for a data scientist, too. See something fishy in a data set? Some anomaly that doesn’t quite look like a typical blip to be scrubbed away in data preprocessing?

Your curiosity can lead you down a path that can reveal something valuable — that anomaly could be something truly significant.

Without curiosity, you might have left that one key crumb of data unexplored.

Data ethics

The discrete data ethics soft skills you need can be hard to pin down — not because their actual applications are vague, but more because subjectivity can be so important here. They’re ethics, after all. 

But before we get to that, we do need to say that there are a few hard-and-fast skills you need. There are laws and regulations surrounding data handling. You need to know about informed consent — i.e., clearly saying how people’s data will be used and asking for their permission. You need to announce and operate under clear data governance principles that say how, for how long, and under what circumstances data can be collected and leveraged. And you need to have a general understanding of the broader implications of GDPR, HIPAA, or any other industry-specific regulations for the field you find yourself in.

And while those skills do indeed fit under this category, the ethics they contain are codified and external to you, the data scientist. So an internal set of ethics in algorithm development is also paramount. A data scientist needs to grasp the implications of their work in a uniquely comprehensive way. For example, if you train a model on data that reflects unethical practices, you can’t expect that model to produce ethical results. So, throughout data science, a healthy sense of skepticism and justice will help you craft ethical algorithms. There’s a lot more to talk about here, though, so if you want to dive deeper, check out our article about ethics and AI.

How to improve your data science skills

If all of these sound like skills you’d like to develop or refine, then our Data Science Bootcamp might be just the thing for you. In fact, if you don’t find yourself earning a data scientist salary within six months of graduation, you’ll get a full refund.

And if you’re still wondering if data science is right for you, check out video testimonials from our grads who were once right where you are now.

The tech scoop

Sign up for our newsletter to get the inside info on getting a career in tech - straight from our industry experts.

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TechStart podcast

Explore the realities of changing careers and getting into tech.

Listen now