SQL vs Deep Learning: What Kerala’s Infopark Really Wants From Data Scientists

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Everyone wants to build the next ChatGPT. Infopark employers want someone who can extract answers from their databases. If you are in Kochi or Trivandrum, prioritise SQL, ETL, and dashboards first. Add deep learning later.

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    Most entry-level roles in Infopark are with service companies. These firms deliver data work for clients across retail, banking, healthcare, and logistics. Their immediate need is reliable answers to business questions. That means clean data, efficient queries, and clear dashboards. Deep learning is valuable. But it is often a secondary requirement.

    The Resume Trap: why deep learning alone can hurt freshers

    Many students invest months in deep learning projects. They show up to interviews with impressive models. They cannot write a LEFT JOIN. Recruiters see a mismatch. They worry the candidate will not do the routine work that pays the bills. This mismatch reduces interview callbacks. The safe path is simple: master core analytics first, then add deep learning as a growth skill.

    The Service‑Company Factor: what Infopark employers test

    In a typical 45-minute technical screen, interviewers focus on practical skills. Expect the following split: 30 minutes on SQL and Python basics, 10 minutes on data modelling and BI, 5 minutes on ML or deep learning overview. The exact split varies by company. But SQL dominates most early-career screens.

    What to prepare:

    • SQL: joins, window functions, aggregation, subqueries, performance basics.

    • ETL sense: data ingestion, cleaning steps, basic pipeline logic.

    • BI tools: Power BI or Tableau; dashboards that answer business questions.

    • Python: pandas for data manipulation, simple scripts, code hygiene.

    Communication: one-page case studies and clear metric definitions.

    The Job Pyramid: Hybrid Analyst vs Deep‑Learning Specialist

    Most hiring in Kochi fits a clear pyramid shape. At the base are hybrid analyst roles. At the top are specialist AI roles.

    Role type

    Volume

    Day-to-day focus

    Hybrid Analyst

    High (~70–80%)

    SQL, ETL, dashboards, reporting

    Mid-level Data Engineer / ML Ops

    Medium

    Pipelines, automation, reproducibility

    Deep Learning Specialist

    Low (~10–20%)

    Research, model development, scaled deployment

    If you want to maximise hires quickly, aim for the hybrid analyst profile.

    Skills matrix (where to spend your time)

    • Priority (0–3 months): SQL (daily), Power BI/Tableau (weekly), Python for data (daily).
    • Mid-term (3–9 months): ETL/pipelines, basic ML, code hygiene (Git).
    • Long-term (9+ months): Deep learning frameworks, MLOps, large-scale model deployment.

    Rule: Be 80% strong in the first group before investing heavily in deep learning.

    Portfolio playbook: three recruiter‑grade artifacts

    1. SQL + BI case study (priority).
      • Use a real or realistic dataset.
      • Include 10–15 SQL queries that show joins, windows, and business metrics.
      • Build one dashboard and a one-page case note with the key insight and recommended action.
    2. Mini ETL + pipeline project.
      • Ingest raw CSVs, clean, transform, and store results.
      • Automate the job with a scheduled script or a simple Airflow DAG.
      • Document data lineage and a failure-recovery plan in the README.
    3. Applied ML prototype (optional for analyst roles).
      • A reproducible notebook, evaluation metrics, and a short deployment note.
      • If you target a DL role, replace this with an end-to-end DL project and model-monitoring notes.

    Host all projects on GitHub. Add READMEs and 60‑ to 120‑second demo videos.

    How to talk about your work in interviews

    Use a short, consistent script for every project:

    1. One-line business question.
    2. Data sources and transformations.
    3. Key metric and why it matters.
    4. Technical highlight (e.g., optimized query or model metric).
    5. One business action that follows from the insight.

    Practice this script until you can deliver it in 90 seconds. It beats vague descriptions.

    The Deep‑Learning Rule of Thumb

    Learn DL to future-proof your career. But do not let it replace core analytics. Two safe strategies:

    • T‑shaped approach: Broad analytics skills across SQL, BI, and Python, plus a deep skill in one ML/DL area.

       

    • Project timing: Build core artifacts first. Add a DL capstone only after you have shipped the SQL + BI and pipeline projects.

       

    Employers prefer hire-ready analysts. They train specialists later if they need them.

    How Techolas Kochi prepares you differently

    Techolas designs its curriculum to match Infopark hiring realities. Key features that matter to recruiters:

    • SQL-first curriculum. Complex joins, window functions, query optimisation, and test-style problems are core.
    • Project-first learning. Messy, scenario-based projects that mirror the real work in Infopark teams.
    • Pre-placement program. Mock interviews focused on SQL whiteboarding, role-based resume reviews, and placement drives.
    • Small batches and mentor access. Personalised feedback on projects and interview readiness.

    Quick checklist before you apply to Infopark roles

    • Can you write window functions and optimize a slow query? 
    • Do you have a Power BI or Tableau dashboard with a one-page case note? 
    • Can you sketch an ETL pipeline and describe its failure modes? 
    • Have you completed two recruiter-style mock interviews? 
    • Are your projects on GitHub with READMEs and demo videos? 

    If you can check most of these, apply confidently.

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