Career Guide · Arts & Humanities to Data

Can BA, BSc, Arts and Humanities Students Become Data Analysts? The Complete Roadmap

SECTION 01Is a non‑technical degree really a barrier?

Let us separate the myth from the real obstacle. The myth is that data work requires an engineering degree. The real obstacle is narrower and fixable.

What your degree gave you that engineers often lack

• Writing — most analysis fails at the last step, when nobody can understand the finding.
• Source criticism — history and literature students are trained to ask where a claim came from. That is data validation.
• Research design — a dissertation is a project with a question, a method and a defence. So is an analytics project.
• Reading people — stakeholders rarely ask for what they actually need. Someone has to interpret.

SECTION 02The four skills that make up the job

Learn them in this order. Each one is useful on its own, so you become employable in stages rather than all at once.

SECTION 03Excel — where you actually start

Excel is not the beginner’s toy that internet advice makes it out to be. In most Indian companies the monthly numbers still live in a workbook, and the analyst who can restructure it quickly is immediately useful.

What you’ll learn

• Cleaning — TRIM, PROPER, text‑to‑columns, removing duplicates
• Lookups — XLOOKUP, INDEX‑MATCH, and when each breaks
• Conditional maths — SUMIFS, COUNTIFS, AVERAGEIFS
• Pivot tables and slicers — the fastest analysis tool ever built
• Charts that do not mislead — axis choices, sorting, labels
• Basic automation — named ranges, tables, simple macros

Job titles

MIS Executive Reporting Analyst Operations Analyst Data Entry to Analyst track

' Excel / Google Sheets - the formulas that get asked about in interviews

' 1. Lookup a value from another sheet (modern, safe version)
=XLOOKUP(A2, Customers!$A:$A, Customers!$D:$D, "Not found")

' 2. Conditional total with two criteria
=SUMIFS(Sales[Amount], Sales[Region], $A2, Sales[Month], B$1)

' 3. Count unique customers
=SUMPRODUCT(1/COUNTIF(Sales[Customer], Sales[Customer]))

' 4. Month-on-month growth %
=IFERROR((C2-B2)/B2, "")

' 5. Clean text before analysis
=TRIM(PROPER(CLEAN(A2)))

' Master these five and pivot tables, and Excel stops being a blocker.

SECTION 04SQL — the skill that gets you shortlisted

SQL is the single highest‑return thing on this page. It is a declarative language that reads almost like a sentence, which is why humanities graduates often pick it up faster than they expect.

What you’ll learn

• SELECT, WHERE, ORDER BY — asking a database a question
• JOINs — combining customers, orders and products
• GROUP BY and HAVING — aggregation, the heart of reporting
• Subqueries and CTEs — breaking a hard question into steps
• Window functions — running totals, ranks, month‑on‑month change
• Query debugging — why your row count doubled after a join

Job titles

Data Analyst SQL Analyst BI Analyst Product Analyst

-- SQL: the single most important skill for an analyst
-- Question: which product category earned the most last quarter?

SELECT
    p.category,
    COUNT(DISTINCT o.order_id)      AS orders,
    ROUND(SUM(o.amount), 2)         AS revenue,
    ROUND(AVG(o.amount), 2)         AS avg_order_value
FROM orders o
JOIN products p ON p.product_id = o.product_id
WHERE o.order_date >= '2026-04-01'
  AND o.order_date <  '2026-07-01'
  AND o.status = 'completed'
GROUP BY p.category
HAVING SUM(o.amount) > 100000
ORDER BY revenue DESC
LIMIT 10;

-- If you can read this query, you can already do 40% of an analyst's daily work.

# Python + pandas: clean a messy sales file and answer a business question
import pandas as pd

df = pd.read_csv("sales_2026.csv")

# 1. Clean
df.columns = df.columns.str.strip().str.lower()
df["order_date"] = pd.to_datetime(df["order_date"], errors="coerce")
df["amount"] = pd.to_numeric(df["amount"], errors="coerce")
df = df.dropna(subset=["order_date", "amount"])

# 2. Answer: monthly revenue per region
monthly = (
    df.groupby([df["order_date"].dt.to_period("M"), "region"])["amount"]
      .sum()
      .unstack(fill_value=0)
      .round(0)
)
print(monthly.tail(6))

# 3. Flag the drop that a manager will actually ask about
change = monthly.pct_change().iloc[-1] * 100
print(change[change < -10].sort_values())

SECTION 05Power BI — making your work visible

A dashboard is how the rest of the company sees your work. It is also the easiest thing to put in a portfolio, because a recruiter can understand it in ten seconds.

What you’ll learn

• Connecting to Excel, CSV and SQL sources
• Power Query — cleaning and shaping before you chart
• Data modelling — relationships, star schema basics
• DAX essentials — measures, CALCULATE, time intelligence
• Design — one question per page, no decoration
• Publishing and scheduled refresh

Job titles

BI Analyst Power BI Developer Dashboard Analyst MIS Analyst

SECTION 06Statistics and Python — the confidence layer

You do not need a mathematics degree. You need enough statistics to avoid embarrassing yourself, and enough Python to handle files Excel chokes on.

What you’ll learn

• Descriptive statistics — mean vs median, spread, outliers
• Distributions and why averages hide things
• Correlation, and why it is not causation
• Sampling and basic hypothesis testing / A‑B test reading
• Python basics — variables, lists, loops, functions
• pandas — read, clean, group, merge, export

Job titles

Data Analyst Marketing Analyst Research Analyst Junior Data Scientist

SECTION 07Roles that hire non‑technical graduates first

Not every door is the same width. These four open earliest for BA, BSc and humanities candidates.

SECTION 08Three portfolio projects that suit your background

Pick topics where your degree gives you an edge. A humanities graduate analysing language, policy or behaviour is more interesting than another Titanic notebook.

• Project 1 — Public policy dashboard. Take census, education or health data from a government open‑data portal. Clean it, build a Power BI dashboard comparing districts, and write a one‑page brief on what a state official should look at first.
• Project 2 — Text and sentiment analysis. Scrape or download 2,000 product reviews or news headlines. Categorise them, chart what people complain about most, and quantify how complaints changed over time. This project uses exactly the reading skills your degree trained.
• Project 3 — Business SQL case study. Load a retail or subscription dataset into PostgreSQL. Answer ten questions a manager would ask — repeat purchase rate, churn by cohort, revenue concentration — and publish the queries with your findings.

SECTION 09Step‑by‑step roadmap — six months

Built for 10 hours a week alongside college or a job. Weeks, not months, so you can see progress.

1. Weeks 1–4: Excel — formulas, lookups, pivot tables, charts. Deliverable: clean one messy public dataset and write five findings.
2. Weeks 5–12: SQL — joins through window functions, on a real multi‑table schema. Deliverable: 100 solved queries pushed to GitHub.
3. Weeks 13–16: Power BI — Power Query, data model, DAX measures. Deliverable: Project 1, the policy dashboard.
4. Weeks 17–20: statistics and Python — pandas plus descriptive statistics. Deliverable: Project 2, the text analysis.
5. Weeks 21–22: Project 3 and portfolio — the SQL case study, a GitHub profile, and a LinkedIn headline written around what you can do.
6. Weeks 23–26: apply and interview — 10 applications a week, one timed SQL practice test daily, and a rehearsed two‑minute story for each project.

SECTION 10Skills to learn — the complete list

The core list is the same for everyone. Choose one specialisation row based on the role you want first.

Core skills (needed on every path)

• Excel / Google Sheets — formulas, lookups, pivot tables, charts
• SQL — SELECT, JOIN, GROUP BY, window functions, CTEs
• Python — variables, loops, functions, pandas, numpy
• Statistics — mean, median, distribution, correlation, hypothesis testing
• Visualisation — Power BI or Tableau, plus matplotlib / seaborn
• Business sense — asking the right question before touching the data
• Communication — explaining a number to someone who did not build it
• Version control — Git and GitHub for your portfolio

Path‑specific skills

SECTION 11Interview Q&A — for arts and humanities candidates

SECTION 12Test yourself — are you ready to start as an analyst?

SECTION 13Frequently asked questions

SECTION 14Continue from here