Uncodemy Alumni Story: From Fresher to Data Analyst

from collections import Counter

# Skills noted from 10 entry-level analyst listings you read
listings = [
    ["excel", "sql", "power bi"], ["excel", "sql"], ["sql", "tableau", "python"],
    ["excel", "power bi"], ["sql", "excel", "communication"], ["sql", "power bi"],
    ["excel", "sql", "statistics"], ["sql", "python"], ["excel", "power bi", "sql"],
    ["sql", "excel"],
]
for skill, n in Counter(s for l in listings for s in l).most_common():
    print(f"{skill:14} {n}/10 listings")

Week 1-2 practice list (Excel)

=SUMIFS(Sales[Amount], Sales[Region], "North")
=COUNTIFS(Sales[Status], "Delivered", Sales[Region], "South")
=XLOOKUP([@ProductID], Products[ID], Products[Name], "Not found")
=IFERROR([@Profit] / [@Sales], 0)
Insert > PivotTable: Rows = Region, Values = Sum of Amount

Goal: answer 'which region sells most?' without help.

-- A milestone query: repeat customers by city
WITH per_customer AS (
  SELECT customer_id, city, COUNT(*) AS orders
  FROM orders
  GROUP BY customer_id, city
)
SELECT city,
       COUNT(*) AS customers,
       SUM(CASE WHEN orders >= 2 THEN 1 ELSE 0 END) AS repeat_customers
FROM per_customer
GROUP BY city
ORDER BY repeat_customers DESC;
-- Being able to write this alone is a good sign of SQL progress.

Project: Why are repeat orders falling? (public dataset)

Question: Which customer groups stopped ordering again?
Data: 12 months of orders; cleaned duplicates and city names
Method: SQL cohorts by first-order month; Power BI drill-down
Finding: Drop concentrated among customers with late first orders
Recommendation: Prioritise first-order delivery speed
Limitations: One dataset; correlation, not proof of cause

import csv

rows = [
    ["date", "title", "source", "tailored", "stage"],
    ["2026-11-02", "Junior Data Analyst", "LinkedIn", "yes", "applied"],
    ["2026-11-03", "MIS Analyst", "Job portal", "yes", "interview"],
    ["2026-11-04", "Reporting Analyst", "Career page", "yes", "applied"],
]
with open("applications.csv", "w", newline="") as f:
    csv.writer(f).writerows(rows)
print("Review weekly: which sources lead to interviews?")

First 30 days in an analyst role

Week 1  Learn key metrics, data sources and where reports live
Week 2  Recreate one existing report to understand the data
Week 3  Take a small request end to end; double-check numbers
Week 4  Share a short summary of what you learned and one
        improvement idea

Priority: accuracy and clear communication over speed.

Stage 1 (Weeks 1-3)   Listing research, Excel foundations, decision
Stage 2 (Weeks 4-10)  SQL, Power BI, statistics basics, 2-3 projects
Stage 3 (Weeks 11-14) Resume, LinkedIn, applications, interview practice
Ongoing               One SQL exercise daily; one project update weekly

Week: ___
What I learned: ______________________
What I built or finished: ______________________
Where I got stuck: ______________________
Who I asked for help: ______________________
Next week's one goal: ______________________

Use this outline later to tell your own real journey:

1. Where I started (degree, skills, doubts)
2. Why I chose data analytics
3. What I learned and in what order
4. Projects I built and what they showed
5. How I searched and prepared for interviews
6. What my first role involves
7. Advice for the next fresher