Developing an AI project development life cycle involves five distinct tasks. No single individual has enough skills (or time) to carry out all tasks in AI project development. Thus, teams include individuals who focus on part of the cycle. Here is a visual representation of six technical roles and how they relate to various tasks.
I What tasks does a data analyst carry out?
Data analysts carry out data engineering and business analysis as shown in Figure 1. This includes:
- data engineering subtasks such as defining data requirements, collecting, labeling, inspecting, cleaning, augmenting, and moving data.
- business analysis subtasks such as building data visualizations, dashboards for business intelligence, presenting technical work to clients or colleagues, translating statistics into actionable business insights, running A/B tests, and analyzing datasets.
Their skills complement those of people who deploy models and build software infrastructure.
II What skills does a data analyst need?
Data analysts demonstrate solid analytical skills as well as business acumen (see Figure 2). They are accomplished in query languages such as SQL and commonly use spreadsheet software tools. They also need some algorithmic coding skills. Communication skills are usually required, but the level depends on the team.
If you’re interested in comparing your skills to other data analysts, we recommend taking the standardized data science, mathematics, and algorithmic coding tests on Workera. If you’re a company hiring data analysts, you can administer computerized tests to AI job applicants for free using Workera Test and connect with AI practitioners using Workera Connect.
III What tools does a data analyst use?
Data analysts in different companies use different tools, but some tools stand out. The following tools grouped by task are the most frequently used tools identified in our research.
- Data engineering in Python and/or SQL or other domain-specific query languages.
- Business analysis in Python, R, other domain- specific tools such as Tableau and Excel, presentation software applications such as PowerPoint and Keynote, and external software services for A/B testing.
IV In what team structure does a data analyst fit?
Building an AI team requires bringing together complementary individuals who can progressively carry out the tasks of the AI project development lifecycle. AI teams focus on data engineering and modeling from the beginning, because they need to validate the feasibility of an AI project or idea. As the project becomes more mature, the team starts focusing on deployment, business analysis, and AI infrastructure.
Data analysts combine well with scientists and engineers. Their skills complement those of people who train models, deploy them, and build software infrastructure.
This article aims to clarify what a data analyst is, what tasks they carry out, and what skills they need. If you’re an AI practitioner, we hope it helps you choose a career track.
Companies may refer to this role as data analyst, data analyst, machine learning engineer, research scientist, statistician, quantitative analyst, full-stack data analyst, and other titles. If you’re a hiring manager, we hope that it helps you define your job requirements.
AI organizations are constantly evolving, so this article is a work in progress. We intend to revise it as our team learns more about new roles.