What you need to know to maximize your salary

Data science is a complex profession. Your employer relies on you to sift through massive and often messy data sets to find the information that can save or destroy the business. You need to keep up to date with the latest tools, programming language updates, and data scientist skills that enable you to do your job. And the tools will only get you so far – at some point you have to make logical guesses about the data in front of you.

That being said, data science can prove to be a lucrative profession for those with the right mix of skills and experience. According to Lightcast, which collects and analyzes millions of job postings across the country, the median salary for a data scientist currently stands at $112,359. With specialized skills (such as machine learning and artificial intelligence), that number can climb even further.

But which data scientist skills are important? What do you need to know beyond tools, platforms and languages? Let’s dive in:

What technical skills do I need to become a data scientist?

The projects and goals of a data scientist can vary greatly depending on their organization and mission. However, data science relies on a foundation of technical skills common to all sectors. These include:

At its core, the profession of data science is about analyzing massive data sets to gain insights. It effectively depends on the intuition and analytical skills of the data scientist, which come with education and experience. During the job interview for a data scientist position, you will likely be faced with questions about how you would make decisions or predictions based on incomplete or messy datasets; even if you don’t know the answer, your interviewer will expect you to exhibit good logic, educated guesses, and (unless you’re just starting out) past experience.

Given the popularity of data science, many specialists are moving into data scientist roles. For example, many economists, mathematicians, researchers and statisticians decide to get into data science; in such cases, their existing analytical skills are easily transferable to their new jobs.

Do data scientists need business skills?

Business skills never hurt if you analyze data (and communicate the results) in a business context. Detailed knowledge of a particular industry is often essential.

“Let’s say I work in D&A for an airline, I’m an AI tools wizard, and I create great reports on how many flights people take, average searches before buying a ticket, spend ticket averages, average flights per person per year – the basic data provided by the system,” Kathy Rudy, head of data and analytics at IT research and consultancy firm ISG, told Dice in 2021. “All great information, but what does the company need to know? Perhaps it’s the average number of empty seats on a particular route or the number of people on a waitlist per flight to determine if they need to add flights to an itinerary Just knowing how to operate an Xbox doesn’t mean you know how to play games.

Knowing the needs of a business often means communicating frequently with business stakeholders throughout the organization, such as managers and executives. Other data scientists may choose to pursue formal education opportunities, if not a full degree such as an MBA, then courses in fundamentals of accounting, digital marketing, or any other subject your organization concentrates.

Data scientists also need “soft skills” such as communication and empathy, as they will need to communicate their plans and the results of their analysis to other stakeholders in the organization in a way that they can understand. .

Are data scientists different from data analysts and data engineers?

The short answer to this question is “yes”. These three roles are certainly not interchangeable, although data analysts and data scientists have obvious similarities (a focus on analyzing datasets, etc.). Here is how the roles are distributed, particularly with regard to skills:

Data Scientist: As we discussed, Data scientists combine statistics, big data processing, analytics tools, and machine learning to transform massive datasets into crucial information that organizations can use to survive and thrive. It is a very strategic role that requires many skills, including critical thinking and data analysis.

Data Analyst: Like data scientists, data analysts are tasked with analyzing data for insights, but they often do so on a much more “tactical” and smaller scale. A “typical” analyst might work with end users to determine what they need from the data, then analyze that dataset and communicate the results. Key skills and tools for data analysts include Apache Hadoop, data visualization suites such as Datawrapper and Tableau.

Data Engineer: Data engineers build and maintain (often massive) repositories of data, such as the customer information databases used by large corporations. They also monitor the movement and state of data in these systems and can help data scientists and analysts locate and clean up needed datasets. Key skills and tools include Hadoop, Docker, Scala, and Kubernetes.

What certifications do data scientists need?

The data science industry includes a wide range of certifications. Here are three ultra-popular ones:

  • Certified Analytics Professional (CAP), $495 for INFORMS members, $695 for non-members, in-person at designated testing centers, self-paced
  • Senior Data Scientist (SDS), DASCA: Cost: $775, online, self-paced
  • SAS Certified Data Scientist, $180 per exam, online, self-paced

Keep in mind that many data science certifications are for more advanced data scientists (i.e. those who have spent several years in industry). There are also certifications that verify your proficiency with particular tools and platforms such as SAS, Azure, Google’s TensorFlow and others.

Which Data Scientist Skills Will Boost Your Salary?

Knowing “cutting edge” skills such as machine learning and artificial intelligence (AI) will make you more valuable to potential employers. But if you really want to show how valuable you are, try to master the data science workflow as much as possible.

Why? According to an analysis by SlashData, most data scientists and machine learning specialists know only a few parts of the overall data science/machine learning (DS/ML) workflow. The highest percentage is involved in data exploration and analysis, and far fewer are involved in model deployment, project management, and model health and lifecycle management. They’re very good at what they do, but learning more about the workflow (and the data scientist skills involved in each) can open up a whole world of opportunity.

Sean N. Ayres