Introduction
Project Scope, Rationale, and Literature Review
Problem Statement
Students preparing for analytics, data science, and business intelligence careers face a common challenge: labor market information is scattered, generic, and rarely tailored to a specific career pathway or industry. A student searching for “data analyst” roles is confronted with wildly different expectations depending on whether the employer is a bank, a hospital system, a logistics company, or a software company. Without a way to narrow that landscape, job seekers struggle to know which skills to prioritize, which employers are the strongest fit, and where they are likely to be competitive.
Our team is building a career evaluation product that narrows this problem to one specific, well-defined slice of the labor market: Data Analyst roles (with a growth path toward Data Scientist / Analytics Engineer) within the Data Processing, Hosting, and Related Services industry (NAICS 5182).
Product Rationale
We selected the Data Processing, Hosting, and Related Services industry because it sits at the core of the “big data and cloud analytics” space this course is built around. This industry includes cloud computing platforms, data centers, managed hosting providers, and data processing bureaus-companies whose entire business model depends on storing, processing, and distributing large volumes of data (U.S. Bureau of Labor Statistics 2026). As of August 2026, the industry employed approximately 453,300 people (Federal Reserve Bank of St. Louis 2025), giving us a substantial and stable base of job postings to analyze.
The broader data career field also supports this choice. The U.S. Bureau of Labor Statistics projects employment of data scientists to grow 35% from 2025 to 2035, far faster than the average occupation, with roughly 24,800 openings projected per year (U.S. Bureau of Labor Statistics 2025). Average data analyst salaries have also risen sharply, reaching approximately $111,000 in 2025 (365 Data Science 2026), underscoring that this is a career path with real, growing economic value for job seekers.
Literature Review
A review of recent industry and labor-market sources points to a few consistent themes relevant to our product:
Core technical skills remain concentrated. SQL appears in roughly 64% of data analyst job postings, and Python, Excel, Power BI, and Tableau are consistently cited as the tools that most differentiate hireable candidates (Coursera 2026). This supports building a skill-gap analysis component that benchmarks a student’s tool proficiency against real posting requirements.
The market rewards judgment, not just tool proficiency. As AI tools automate repetitive tasks like dashboard building and standard reporting, analysts who rely only on these mechanical skills may see fewer opportunities. Sources agree that critical thinking, communication, and the ability to extract actionable insight-rather than just process data-are increasingly what separates competitive candidates (365 Data Science 2026; Coursera 2026).
Industry context changes what “in demand” means. General occupational outlook data (BLS) describes national trends, but does not capture how skill requirements or compensation differ within a specific industry like data processing and hosting (U.S. Bureau of Labor Statistics 2025). This is precisely the gap our narrowed, industry-scoped approach is designed to fill.
What We Explored
Building on this rationale, our team carried out the work below. Each item links to the page with the details:
- Filtered and cleaned the data. We isolated Data Analyst / Data Scientist pathway roles within the Data Processing, Hosting, and Related Services industry (NAICS 5182) in the course
Jobs_2026job files: 6,964 postings in the industry, 1,232 with pathway titles, and 765 distinct postings. See Data Preparation. - Established a market baseline. Across the 765 postings, salary is disclosed on 40% with a median of $188K, experience requirements (stated in 27%) mostly fall in the 3-8 year range, and California and New York lead on location. See Exploratory Analysis.
- Compared team skills with the market. Each team member rated six skills on a 1-5 scale, and we compared those ratings with how often Data Analyst and Data Scientist / ML postings name each skill in their text, then drafted an improvement plan. See Skill Gap Analysis.
- Modeled pay and ranked roles. A salary regression shows which factors are associated with higher pay, a scorecard ranks the four pathway roles on pay, accessibility, and fit with our skills, and a salary estimator lets a reader try their own combination. See Predictive Modeling.
- Benchmarked the pathway against the whole job market. Across all industries, we compared AI-related and non-AI pay, and our data fields against software engineering and other computer and business fields. See Benchmark Analysis.
- Still to come. Final recommendations and the written report.
Industry and Career Pathway Documentation
- Selected career pathway: Data Analyst, with a growth trajectory toward Data Scientist / Analytics Engineer roles
- Selected industry code: NAICS 5182 - Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services
- Industry label: Data Processing, Hosting, and Related Services (part of NAICS 518 - Data Processing, Hosting, and Related Services, under NAICS 51 - Information)
- How the code appears in the data: In the course
Jobs_2026job files, this industry is the three-digit code518, and all 6,964 postings with that code also carry the four-digit code5182, so filtering on518selects exactly our industry. - Why this industry is relevant: This industry’s entire business model is built around storing, processing, and delivering data at scale-cloud platforms, data centers, and managed hosting providers all fall under this code. Analytics and data roles are not a support function here; they are central to how these companies operate, monitor infrastructure, and serve customers, making it a directly relevant and data-rich slice for a career evaluation product.
Data Fields Used
The analysis uses these fields from the MET Career Compass 2026 job files (Jobs_2026): job title, industry/NAICS classification, salary range, location, work arrangement (remote, hybrid, or onsite), and the full posting text, from which we read required skills, degree wording, and years of experience. The files also provide a skills field, but it is truncated to the first few skills (alphabetically) for each posting, so we did not use it to rank skills. Column-by-column details are in the data dictionary.