Day 54: Resume, LinkedIn & Job Search

weak = ["Did EDA on sales data", "Made a churn model", "Worked on SQL queries"]
strong = [
    "Analysed 2,000 e-commerce orders in pandas and found the festive season drove 27% of revenue; "
    "recommended a 6-week-earlier stock plan",
    "Built a telecom churn model (scikit-learn, ROC-AUC 0.83) and deployed it as a Streamlit app; "
    "calling the top-risk 20% reached 2.3× more churners than random calls (≈ ₹12 lakh net per cycle)",
    "Designed a star-schema SQLite warehouse and wrote CTE/window-function queries that cut a weekly "
    "Excel report from 3 hours to 5 minutes",
]
for w, s in zip(weak, strong):
    print("✗", w); print("✓", s, "\n")
verbs = ["Analysed", "Built", "Designed", "Automated", "Reduced", "Increased", "Forecast", "Deployed", "Presented"]
print("Start bullets with:", ", ".join(verbs))

import re
from collections import Counter
jd = """Data Analyst – Bengaluru. Requirements: strong SQL (joins, window functions), Python (pandas, NumPy),
Excel (pivot tables, XLOOKUP), Power BI dashboards, statistics, A/B testing, stakeholder communication.
Nice to have: machine learning, scikit-learn, Git, Tableau."""
resume = """Data Analyst (Fresher). Skills: Python, pandas, Matplotlib, SQL, Excel, machine learning, scikit-learn.
Projects: BazaarKart sales EDA in pandas; churn prediction with scikit-learn and Streamlit."""
SKILLS = ["sql", "window functions", "python", "pandas", "numpy", "excel", "pivot tables", "xlookup", "power bi",
          "statistics", "a/b testing", "communication", "machine learning", "scikit-learn", "git", "tableau", "streamlit"]
def found(text):
    t = text.lower()
    return {s for s in SKILLS if re.search(r"(?<![a-z])" + re.escape(s) + r"(?![a-z])", t)}
need, have = found(jd), found(resume)
print(f"JD asks for {len(need)} skills | resume shows {len(need & have)} → match {len(need & have) / len(need):.0%}")
print("✅ matched:", sorted(need & have))
print("❌ missing:", sorted(need - have))

Hi Priya, I enjoyed your post on Power BI at <Company>. I'm a data analyst
(SQL, Python, Power BI) and recently built a churn-prediction app:
github.com/<username>/telecom-churn. I saw the Data Analyst opening (Job ID 12345)
and believe my A/B-testing and dashboard projects match it well.
Would you be open to referring me, or sharing any tips about the team?
Thank you for your time!

import pandas as pd
apps = pd.DataFrame([
    ("Fintech start-up", "Data Analyst", "LinkedIn", "2026-10-01", "Applied"),
    ("E-commerce co.", "Business Analyst", "Referral", "2026-10-02", "Interview"),
    ("IT services", "Junior Data Scientist", "Naukri", "2026-10-03", "Applied"),
    ("Edtech", "Data Analyst Intern", "Internshala", "2026-10-03", "Rejected"),
    ("Bank", "MIS / Data Analyst", "Company site", "2026-10-05", "Assessment"),
    ("Logistics", "Data Analyst", "Referral", "2026-10-06", "Interview"),
    ("SaaS", "Product Analyst", "LinkedIn", "2026-10-07", "Applied"),
    ("Retail chain", "Data Analyst", "Naukri", "2026-10-08", "Applied"),
], columns=["company", "role", "source", "applied_on", "stage"])
apps["applied_on"] = pd.to_datetime(apps["applied_on"])
progress = apps["stage"].isin(["Assessment", "Interview"])
print(apps.groupby("source").agg(applications=("company", "count"), progressed=("stage", lambda s: s.isin(["Assessment", "Interview"]).sum())))
print(f"\nOverall response rate: {progress.mean():.0%} → referrals convert best – ask your network!")
apps.to_csv("job_tracker.csv", index=False)