This is how I got paid Internship using linkedin | Rishav Kumar

Main ideas / lessons

• The speaker explains how they secured a paid internship via LinkedIn while in college, using only free resources (no paid courses or purchased materials).
• They describe a period of rejection and frustration with unpaid or inflexible internships, which they attribute to:
  • Low priority for students from Tier 3 / lower-grade colleges (unless you have referrals or strong exam performance).
  • High competition and timing delays in applying.
  • Their earlier projects/resume being perceived as “not special enough.”
• Their eventual success came from upgrading their portfolio, especially by adding more advanced, automation-focused projects.
• They share an application strategy:
  • Use only a small number of job platforms.
  • Keep profiles updated.
  • Apply frequently—often daily or hourly, with LinkedIn as the priority.
• They outline a selection pipeline they faced:
  1. Recruiter asked for project PDFs
  2. They received an assignment
  3. After submitting, they went through technical + normal interviews
• Later, they experienced failure: they were fired during a 10-day probation period due to communication/English and “professionalism” expectations, even though they completed tasks competently.
• Their final guidance:
  • Avoid unpaid internships
  • Avoid paying for “course-like internships”
  • Study using free resources
  • Build strong projects
  • Improve resume representation and communication

---

Methodology / instructions (detailed)

1) Preparation approach (free-only)

• Do not join paid courses or buy paid resources.
• Use free resources, such as:
  • YouTube
  • ChatGPT
  • Google
  • Free PDFs / roadmaps / study guides (mentioned later)
• Study consistently, covering topics part-by-part every day (avoid skipping days).

2) Resume and project strategy

• Diagnose the problem: despite applying widely, they weren’t selected.
• Improve by updating:
  • The projects listed on the resume
  • How projects are written/presented (worsening/wording and representation)
• Shift from basic projects to more advanced automation projects, for example:
  • Learn automation (don’t do everything manually)
  • Build a project using Microsoft Fabric
  • Use SQL + Power BI
  • Build another project using Apache Spark (“Pi Spark”)
  • Add finance/banking-related and other calculation-heavy projects
• Aim for projects recruiters can question deeply (and be ready for cross-questions during interviews).

3) Internship/job search tactics (platform selection + frequency)

• Use only 2–3 platforms max (they used three):
  • LinkedIn (highest priority)
  • Internshala (second priority)
  • Naukri / Naukri “code” (third priority)
• Apply continuously:
  • Prefer daily and also hourly / immediately when new openings appear
• Why they prioritized LinkedIn:
  • More “genuine” companies
  • Listings appear frequently (every hour / sometimes faster)
  • Recruiters check your profile and projects (often via links like GitHub)

4) Profile/portfolio packaging (GitHub + PDFs)

• Keep your LinkedIn profile updated and link your GitHub.
• If a recruiter requests documents:
  • Provide project details as PDFs (so the recruiter can forward them internally)
• If you receive an assignment:
  • Treat it as a required evaluation step
  • Finish it quickly and submit within the timeframe
  • Produce a strong output (e.g., dashboard/report) and respond by email

5) Interview focus (Power BI role)

• For the role they were hired for (Power BI developer), they prepared for:
  • Tool-specific UI/functionality questions
  • Understanding the purpose of various Power BI functions/buttons
  • Broader Power BI concepts coverage

6) Probation/internship performance lessons (what went wrong)

• They describe a 10-day probation.
• During probation, they were given:
  • Small tasks using datasets (often Kaggle datasets)
  • Expectations to work independently and professionally
• They claim the firing reason was mainly:
  • Spoken English / communication
  • A “professional” tone expectation (they believed they were being polite, but a senior interpreted it differently)
• Their recommendations afterward:
  • Don’t over-request to stay if you’re fired
  • Move on and find other opportunities
  • Learn and adapt quickly if you can’t meet professional expectations

7) Resume value after probation/firing (important caution)

• They warn that adding a probation-failed/expelled internship to your resume can backfire:
  • Future companies may verify it and then reject you.
• They also recommend applying elsewhere immediately rather than waiting out probation.

8) Career-domain adjustment (what they plan next)

• After rejection/firing, they decided to shift domain focus:
  • From data analytics toward machine learning / data science / data engineering
• Reasoning:
  • They believe AI tools reduce the value of some basic data analyst tasks, so they want higher-skill areas requiring more human effort.

9) Final guidance / “rules” for internships

• Prefer paid internships.
• Avoid unpaid internships.
• Avoid “internship courses” where you pay for teaching:
  • If the company teaches you like a course, they argue it lacks true internship value.
• Build a resume with:
  • Strong projects
  • Detailed skill descriptions (not just “I know Python”)
  • Certifications (if any)
  • Hobbies/extra skills
• Maintain academic performance:
  • Aim above 60%, noting many companies ask for 70–75%+
• Improve your environment and social circle:
  • They claim better peers increased motivation and understanding.

---

Speakers / sources featured

• Speaker: Rishabh Kumar (also referred to as “Rishabh” / “Rishav” in the text)

Platforms mentioned (sources/tools)

• LinkedIn
• Internshala
• Naukri (referred to as “ncr.com / ncry.com”)
• YouTube
• ChatGPT
• Google

Technologies / tools mentioned

• Microsoft Fabric
• SQL
• Power BI
• Apache Spark (“Pi Spark”)
• Kaggle (datasets)
• GitHub (project hosting/linking)

Other referenced process elements

• CV/assignment workflow via recruiter + HR + senior data scientist/team (no specific individuals named)