Skills for Analytics Careers: What You’ll Need and Where to Learn Them

Building a career in the analytics field takes more than just learning how to build a few tools. The strongest candidates combine technical expertise with domain understanding and the ability to communicate decisions.

While all analysts have to be proficient in fundamental areas like statistics and critical thinking, your chosen career path determines the full skillset you’ll need. Analysts in supply chain management need domain knowledge about logistics, for example, while those in governance are likely to require more regulatory knowledge.

No matter what sector or specialization you pursue, learning to interpret and contextualize data will make you a more versatile analyst. Here, we’ll outline the most important analytics career skills you should acquire to excel and grow in the field, and where those skills can take you on the path towards a long and stable career.

Key takeaways

• Strong analytics professionals combine technical skills with communication, judgment, and domain knowledge.
• Statistics, data modeling, programming, and data management remain core skills across many analytics careers.
• Data visualization and storytelling are essential because insights only matter when stakeholders can understand and act on them.
• Human-centered skills like validation, ethical judgment, and asking the right questions can set strong analysts apart.
• Northeastern’s analytics programs help students build these skills in different ways depending on whether they want a more applied, technical, engineering, or social-analysis path.

The technical skills most analytics careers still require

Data analysts are information experts who play the role of architect, detective, and storyteller. They may design structures for storing and using data, examine datasets to find trends or inconsistencies, and translate findings into reports, dashboards, or recommendations that others can understand. From a technical standpoint, there are several core data analytics skills needed for every job.

Statistics and quantitative reasoning

Analysts collect samples of numerical data and categorize them into various sets to see whether relevant trends emerge. They observe how patterns change under different conditions, and they use quantitative reasoning to ask questions and test out theories based on the trends they see. Analysts also consider how real-world conditions influence the statistical findings.

Working with data and models

One of the most important skills for analytics careers is the ability to view data in its crude, unstructured state and conceive a plan to systematize it. Analysts decide how data should be structured in databases—known as data modeling.

To enable analysis, they design models to sort data in multiple ways based on different attributes. Analysts are then able to make decisions and recommendations by using data models to observe existing patterns and predict future outcomes.

“I think there’s always a need for the ability to be able to predict and model for every type of business,” says Gail Fitzgerald, senior director of marketing, recruiting, and digital strategies at Northeastern University’s Khoury College of Computer Sciences.

“You can’t just go based on, ‘Oh, this has been working all along.’ Businesses need to be able to determine what is the next product, what is the next cycle, what should we be thinking about going forward? A good data scientist can really help to build and envision what that looks like for them going forward.”

Programming or computational fluency

Analysts need programming skills to develop databases and algorithms for retrieving and reorganizing data based on relational factors. Having proficiency with tools and coding languages like SQL, Python, and Power BI allows them to clean up unstructured data and transform the information using a variety of computational methods, such as:

• Scripting and automation to simplify repetitive tasks
• Simulations to forecast potential outcomes
• Machine learning to conduct AI-powered analysis
• Data visualization to share insights through dashboards and charts

Computational fluency is the ability to choose and execute the right techniques to solve problems and derive meaningful insights using data.

Data management and movement

The volume and diversity of data available to analysts is growing rapidly because businesses of all sizes now recognize its value, and the public is sharing information more freely. However, this creates a need for precise and secure data management.

In other words, data specialists must be skilled at establishing secure pipelines to store data safely, transfer it to the right places, and limit who has access when it travels between systems and organizations. Using a mix of technology protocols, documentation, and compliance policies, analysts can make sure data is handled appropriately at all stages.

For instance, consider what goes into an e-commerce company setting up a rewards app. Thousands of customers will likely sign up and deliver thousands of diverse data points—addresses, credit card numbers, product size preferences, and more.

A data specialist must make sure that the business is able to review the accurate information it needs to deliver on the promise made, while the customer’s information is kept secure at every step of the journey. Through secure transfer protocols and strict access controls, the specialist turns a massive influx of raw information into a safe, well-managed corporate asset.

Why data visualization and storytelling matter so much

“Good analysis” isn’t enough to be effective as a data specialist. Imagine a mechanic who diagnoses exactly what’s wrong with your car but explains it in dense technical jargon that’s hard to follow. You’d still be unsure if the $2,000 bill for the repair is warranted or not.

The diagnosis may be correct—but it’s useless until it’s translated into something you can act on.

For the same reason, data analysts have to be adept at understanding the practical goals of an organization and framing data within the context of how it can improve performance. Through reports and visuals, they can use storytelling to convey what’s at stake. Joe Reilly, an assistant teaching professor and program director for Northeastern’s Master of Professional Studies in Analytics, explains this further.

“Once you have the code and the statistical test done, you need to actually explain, ‘Okay, so what does this mean? Why should I change my behavior based on this? Why should I spend a million dollars changing my marketing strategy because of this?’ To be able to actually drive impact at an organization, you need the communication skills.”

Data visualization skills allow analysts to transform information into dashboards, charts, and graphs that are easier for stakeholders with a non-technical background to understand. Stakeholders don’t just want data; they need evidence-backed insight to inform their decisions.

To meet this need, analysts have to know what’s important to different stakeholders and present findings that are compelling enough to drive action.

The overlooked skills that separate strong analytics professionals

Health records, telecommunications, financial transactions, resource shortages, animal migration, and weather patterns. At its core, data is just a record of human and ecological activities. Viewing information from an overly technical perspective can get in the way of using data ethically, responsibly, and accurately.

Fitzgerald emphasizes that human-centered skills are essential to responsible and effective analytics work. “I think the other thing to talk about here is: What is the human aspect of things? There is the translational piece of things. There is the quality control piece of things. There is the validation piece of things that a person is going to bring to that—the ability to say, ‘Are we asking the right question? Are we looking at the right data set? Is that really accurate to what we’re trying to solve for?’”

As Fitzgerald notes, it takes human-centric discernment to truly become a well-rounded analyst. This includes skills such as:

• Asking the right question: The questions an analyst asks determine the data collected and prioritized, making it essential to avoid irrelevant information and define project objectives well.
• Validation and quality control: Data can come from flawed, incomplete, or biased sources or be corrupted at different points in the pipeline. It’s crucial to verify the validity of data, be cautious about data that could negatively impact outcomes, and be transparent about known issues in datasets.
• Domain knowledge: Having expertise within a specific domain enables analysts to apply relevant standards, procedures, and use cases. Analysts in healthcare or security need industry-specific knowledge that wouldn’t be necessary for analysts in e-commerce, and vice versa.
• Ethical judgment and responsible use: Analysts should promote fairness and avoid the misuse of data by maintaining confidentiality, being straightforward about data collection practices, and reducing biased interpretations whenever possible.

Think of it this way: analysts shouldn’t just focus on what they can do with data, but also what they should do with it. Privacy and equity remain top concerns, especially in circumstances where sensitive data may be available but is ultimately unnecessary for solving a particular problem.

Gregory M. Zimmerman, PhD, program director of Northeastern’s Master of Science in Applied Quantitative Methods and Social Analysis, makes a similar point from a social-science perspective.

“Students need more than coding skills to succeed in today’s marketplace,” he says. “They need a broad understanding of quantitative methodologies, computational tools, and analytical strategies in the social sciences, as well as an in-depth understanding of a specialized methodological and analytical technique for social analysis.”

A broader methodological foundation can be especially important for analysts working in policy, research, and other public-facing areas, where everything from the quality of the question to the interpretation of findings can shape the decisions made by those in authority.

How AI is changing the skills conversation in analytics

As AI use increases across all industries, people interested in data science may wonder whether it’s still worth it to strengthen their data analytics skills. As Reilly points out, AI functions are now baked into many software programs, allowing people without technical knowledge to complete basic tasks.

“A lot of the functions that were gated behind data science and analytics are now a lot more democratized,” he says. “It’s a lot easier for anybody in a business to make their own dashboard or to visualize something or to write their own SQL query without being a coder by trade. And I think that’s very exciting. But there’s also some risk to that where, just because you can do it, doesn’t mean that you have the domain expertise to actually do it well.”

While AI can streamline tasks, it has to be given instructions and lacks the judgment to manage complex issues. Functional fluency in analysis, statistics, programming and business operations is necessary to recognize when information is inconsistent or incomplete, or when data pipelines aren’t performing well.

Rather than replacing data analysts, AI tools can more appropriately and efficiently be used to handle repetitive tasks that would normally be time-consuming for a professional.

“I think that’s really going to make analysts more efficient and more able to do their ultimate job of answering business questions and figuring out how to communicate those findings to non-technical stakeholders much faster and easier without the kind of bottlenecks of writing code by hand and grappling with syntax and all of those other kinds of stumbling areas,” Reilly says.

Current and future analysts can begin to navigate these industry changes by honing human-centric abilities like reasoning, interpretation, ethics, and strategic thinking. As analysts gain more data science expertise and domain knowledge, they become more valuable for the oversight they can bring to every project.

“What’s going to become a more important question as we kind of go into this is: How can a master’s degree set me up for success with AI’s capabilities right now?” Fitzgerald acknowledges. “From a computational perspective, what we kind of talk about is, AI can do those rote tasks, those things that maybe you don’t really love doing, but the master’s degree is going to give you the ability to work on more interesting problems. The foundational skills you’re going to get are going to allow you to dig deep into the more interesting aspects of analysis and prediction.”

How students actually build analytical skills

Working with data using as many techniques and occupational scenarios as possible will always be the most effective way to build proficiency. New sources of data are emerging every day, and the sheer volume of data can be intimidating if you don’t have diverse experience in information management.

Students can become more skilled at handling data through applied coursework that equally emphasizes practical application and theory. Hands-on projects, co-ops, and capstones allow students to learn by doing, not just by reading.

Reilly highlights how Northeastern uses experiential learning in its data analytics programs: “We actually bring partners into classrooms and work with real datasets and real problems. We have sponsoring organizations, and not just from Boston, but wherever we have campuses. For example, we’re working with the city of Toronto and Telus, a big Canadian telecom company.”

“So, we’ve tried to forge those partnerships so that some of our courses actually work more like a consulting engagement where we’re learning about the client and their data, and then the students are presenting their prototypes and deliverables at the end of that course.”

In real work environments, not all data will be “clean,” organized, and ready for analysis. Projects with real datasets challenge students to manage unstructured data and identify errors, inconsistencies, and redundancies. It’s also crucial to have familiarity with the specific technology and software that professional analysts use in their daily work.

Which Northeastern programs help build which analytics skills

No matter what area of analytics interests you, it’s advantageous to embrace the interdisciplinary nature of the field by developing cross-functional skills. Data analysts increasingly need an adaptable skillset that combines engineering, machine learning, business intelligence, forecasting, and AI, just to name a few.

Northeastern offers four graduate degree programs that help students build core data analytics skills, along with specialized knowledge for specific career paths.

• The Master of Professional Studies in Analytics takes a deep dive into data modeling, business intelligence, information management, and AI for students interested in solving organizational problems.
• The Master of Science in Data Science looks at data management from all angles—engineering the data framework, mastering mathematical and statistical techniques, mining data for critical insights, and more.
• The Master of Science in Data Analytics Engineering is tailored to students who want to design data infrastructure and protocols, emphasizing skills like data mining, visualization, and deep learning
• The Master of Science in Applied Quantitative Methods & Social Analysis focuses on applying quantitative and computational tools to social inquiry. Rather than simply teaching traditional data science skills, the program emphasizes how data can be used to study policy, institutions, human behavior, and social systems.

Graduate study may be the right option if you value guidance from experienced faculty and hands-on learning experiences that include the kinds of challenges you’ll face on the job. All of Northeastern’s programs provide opportunities to build a professional portfolio of real-world projects that demonstrate readiness for data analytics careers.

Develop the skills that will catapult you into a career in analytics

Cultivating technical and analytical skills is a strategic investment for those who want to stay competitive in the field of analytics. By building a solid foundation in core disciplines, analysts are well-positioned to grow in the field and progressively learn new technology skills as the demand arises.

That is why building an analytics career starts with developing the technical skills, practical experience, and human-centered judgment to use data well in the environments where you want to work.

If you’re exploring graduate study in analytics, Northeastern offers several paths to help students build those skills in different ways. Explore Northeastern’s master’s programs in analytics—Master of Professional Studies in Analytics, Master of Science in Data Science, Master of Science in Data Analytics Engineering, and Master of Science in Applied Quantitative Methods & Social Analysis.