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🕵️♀️My First Deep Dive into Data (And What I Learned About My Future Career)
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📝 TL;DR
How It Started
As I am getting into data analytics, I wanted to figure out: which analyst path should I go for?
Data Source
15,000+ analyst job postings from Arbetsförmedlingen (2016–2024).
Skills Demonstrated
SQL, Excel, Tableau, AI assisted Python, end-to-end project design.
Key Findings
Digital Analysts make up the largest share of postings (38.5%), followed by Business Analysts (29.9%) and Data Analysts (15%).The job market exploded during COVID recovery (a 100% surge in 2021-2022), but it cooled off in 2023–2024 as the market normalized and the economy weakened.Half of all opportunities are in Stockholm, but Västra Götaland offers a stronger concentration of Business Analyst roles.SQL is the most in-demand technical skill, and Looker and Python show the fastest growth.
▶1. ❓ Introduction: Why This Project
The Motivation
When I first got into data analytics, I realized there are tons of different analyst positions and I wasn't sure what makes them different.
This project serves three purposes:
Figure out which analyst path makes the most sense for someone with my background and interests.Get hands-on practice with a complete analytics workflow - from extracting and cleaning data to analyzing and visualizing it.Test out AI-assisted coding, even though I only know basic SQL and have zero Python experience.
▶2. 🛠️ Data Source & Methods
Data Source
Arbetsförmedlingen (Swedish Public Employment) API https://arbetsformedlingen.se/om-webbplatsen/apier-och-oppna-data. /emCoverage: 2016-2024, ~15,000 analyst job postings, 6 analyst roles (Digital, Business, Data, BI, Operations, Commercial) chosen based on personal career interests.Methods
AI-assisted coding: I used AI to help write the Python scripts, then adapted and validated the code myself. Since I'm still early in my learning journey, I focused on understanding the overall data pipeline rather than spending excessive time on syntax. This gave me a solid big-picture view of what data professionals actually do day-to-day.Role classification: Job titles were mapped to analyst categories using a Python keyword mapping script (covering both Swedish and English).Skill extraction: Python text-mining rules were applied, with synonym mapping (e.g., “SQL” vs. “Structured Query Language”).Handling duplicates: For time-series analysis, I kept duplicate postings with unique IDs since they show real demand patterns; But for skill analysis, I consolidated duplicates by content to avoid skewing the results. Data removal: Headhunter companies were removed from employer analysis since they don't represent the actual hiring companies.Validation: I manually reviewed 100 job postings to check data accuracy.Process
Data Extraction: Used AI-assisted Python to script job postings from the API.Data Cleaning: SQL queries to pull relevant field and Python text mining to extract skills from job descriptions.Validation A: Manual review of 100 postings. Analysis: Excel.Visualization: Tableau dashboards.Validation B: Light comparison with industry reports.Limitations
Single data source: Arbetsförmedlingen tends to over-represent large companies and public sector jobs, while under-representing startups and international companies.Time lag: Data covers only 2016–2024, so may miss the most recent market changes.Limited Validation.
▶3. 🧐 Analysis: Job Market Reality Check
Market Demand Hierarchy:
Digital Analyst: 38.5% (highest demand, driven by companies' focus on marketing analytics).Business Analyst: 29.9% (second largest, but sensitive to economic conditions. When the economy slowed in 2023-2024, Business Analyst postings dropped significantly).Data Analyst: 15.0% (smaller share, but growing fast at +27% CAGR).BI Analyst: 11.4%.Operations Analyst: 2.9%.Commercial Analyst: 2.4%.Economic Impact Patterns:
COVID Boom (2020-2021): Job postings doubled due to company expansions and digitalization.Reality Check (2023-2024): Job market cooled off significantly as the market normalized and the economy weakened.Seasonality: 40% fewer postings in July and August (Swedish summer effect).Dashboard A - Market Overview
▶4. 📍 Analysis: Geographic Intelligence
Analyst opportunities are highly concentrated: nearly 90% of all postings are clustered in just three regions - Stockholm, Västra Götaland, and Skåne.
Regional Specialization
Stockholm (≈50% of all jobs):
Strong focus on Digital Analysts (+8% vs Västra Götaland)Under-represented in Business Analysts (-11% vs Västra Götaland)Reasons: tech and startup ecosystemVästra Götaland (≈25%):
Leads in Business Analyst opportunitiesReasons: concentration of manufacturing and traditional industriesSkåne (≈15%):
BI Analyst specialization (+6% vs Stockholm and Västra Götaland)A balanced and diversified analyst job marketDashboard B - Regional Analysis
▶5. 🏢 Analysis: Employer Landscape
Top Hiring Companies (100+ postings)
Traditional Enterprises: Swedbank, IKEA, Volvo, Electrolux, Scania
Digital Agencies: Noor Digital, Leadstar Media, Mild Media
Consulting Firms: Columbus, Sogeti, Sopra Steria
Role-Specific Employer Patterns
Demand varies significantly by employer type.
BI Analysts: Concentrated in consulting firms (Columbus, Sogeti), suggesting project based demand for specialized expertise.Business Analysts: Dominated by large enterprises (Volvo, IKEA, SAAB), where process optimization, stakeholder alignment, and operational excellence are business priorities.Digital Analysts: Split among specialized agencies, e-commerce companies and startups, where digital performance and customer acquisition are fundamental.Data Analysts: Retail and automotive leaders (H&M, ICA, Volvo), where customer insights and operational data drive competitive advantage.Dashboard C - Employer Landscape
▶6. 🎓 Analysis: Skills & Requirement
Skills Analysis
Universal Requirements:
Languages: Swedish (39% of all postings), English (38%)Soft Skills: Collaboration (27%), Communication (25%) Tools: SQL (15%), Excel (15%) Growth Skills (2022-2024 trend):
Python: +8% growthLooker: +70% (challenging Tableau/PowerBI)Role Differentiation
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Dashboard D - Skill Matrix
The size of each square represents the frequency of a skill within a role, the larger the square, the more frequently the skill is required.
The color of each square shows how over- or underrepresented a skill is across roles, red indicates overrepresentation, blue indicates underrepresentation.
Dashboard E - Key Skills Evolution
▶7. 💬 Takeaways & Reflections
What I discovered through this project:
Business Analyst roles appear highly communication-driven and more vulnerable to economic fluctuations. Given my non-Swedish background, these roles might be more challenging in the Swedish work environment.Data Analyst roles are more technical and show stronger long-term growth. Given my background in business, I can differentiate myself in Data Analyst positions where technical and business skills intersect.Although I had hesitations about relocating to Stockholm, the data clearly shows that it remains the central hub for analyst careers in Sweden. This insight is shaping how I think about location choices.I discovered Looker for the first time - a tool I now want to explore.Regardless of role, I need to strengthen SQL, Python, and soft skills.This project confirmed that I enjoy the investigative aspect of data work.
▶8. 📈 What Could Be Improved
This project was designed as a first exploration, not a final product. Several areas could be improved to strengthen both reliability and depth:
Data Quality and Validation
Expand validation beyond a 100-posting sample and cross check more industry reports.Test the impact of duplicate handling.Compare Arbetsförmedlingen postings with other sources (e.g. LinkedIn) to assess representativeness.Analytical Depth
Break down results by industry to understand sector-specific demand patterns.Analyze skill co-occurrence (e.g., SQL + Python vs. SQL + Excel) to identify common skill bundles.Benchmark Sweden’s results against the EU and global market to find Swedish specific patterns.
▶9. 🗂️ Documentations & Codes