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

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.

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.

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. 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: 30 minutes on SQL and Python basics, 10 minutes on data modelling and BI, 5 minutes on ML or deep learning overview.

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

• Hybrid Analyst – High volume (~70–80%): SQL, ETL, dashboards, reporting
• Mid-level Data Engineer / ML Ops – Medium volume: Pipelines, automation, reproducibility
• Deep Learning Specialist – Low volume (~10–20%): Research, model development, scaled deployment

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?