SQL vs Git & GitHub for Beginners

-- SQL — a simple analytics query

SELECT
    region,
    DATE_TRUNC('month', created_at) AS month,
    SUM(amount) AS revenue
FROM orders
WHERE status = 'completed'
GROUP BY region, month
ORDER BY revenue DESC;

-- Query = table of results.
-- That's the entire skill.
-- SQL is the language of data.

Why SQL is popular:
- Every analyst job expects it
- Works across every database
- Easy to learn the basics
- Powerful for analysis at any scale
- Foundation for BI (Power BI, Tableau)
- Gateway to data science and engineering
- Highest ROI skill in data careers

# Git — the essentials for beginners

git clone https://github.com/user/repo.git
git status
git add .
git commit -m "Add login feature"
git push origin main

# Feature workflow
git checkout -b feature/login
# ... code ...
git add .
git commit -m "Add login form"
git push origin feature/login
# Open a pull request on GitHub

# Update local copy
git pull

# See history
git log --oneline

Why Git matters:

Every developer role expects:
□ Git basics (commit, branch, push)
□ Pull requests + code review
□ Reading diffs
□ Resolving merge conflicts

Every portfolio needs:
□ GitHub profile
□ Clean repos
□ README on every project
□ Commit history that shows growth

Git is the one skill you cannot skip,
whether you go into dev or data.

Before SQL:
□ None — start from scratch
□ Basic understanding of tables
□ (Optional) Excel for practice data
□ (Helpful) curiosity about data

Before Git:
□ Basic terminal usage
□ A code editor
□ (Optional) GitHub account

Both are extremely beginner-friendly.

Time to learn (realistic estimates):

SQL:
- Basics: 1–2 weeks
- Comfortable with joins + subqueries: 3–4 weeks
- Job-ready analyst: 2–3 months
  (includes Excel + Power BI or Tableau)

Git & GitHub:
- Basics: 1–2 days
- Comfortable with PRs: 1–2 weeks
- Job-ready workflow: 2–4 weeks

Rule of thumb:
Git is the fastest win.
SQL is the biggest career unlock.

SQL career paths:
□ Data Analyst
□ Business Analyst
□ BI Analyst / Developer
□ Data Scientist
□ Data Engineer
□ Analytics Engineer
□ Excel → SQL → Power BI → Analytics

Git & GitHub career paths:
□ Backend Developer
□ Frontend Developer
□ Full-Stack Developer
□ Data Engineer (Git pipelines)
□ DevOps Engineer
□ Any code-writing role
□ Every developer, everywhere

Salary ranges in India (2026):

Data Analyst (SQL):
- Fresher:    ₹3.5–5 LPA
- Mid:        ₹5–10 LPA
- Senior:     ₹10–20+ LPA

Data Scientist (SQL + ML):
- Fresher:    ₹5–9 LPA
- Mid:        ₹10–20 LPA
- Senior:     ₹20–40+ LPA

Developer (with Git):
- Fresher:    ₹4–8 LPA
- Mid:        ₹8–15 LPA
- Senior:     ₹15–30+ LPA

Note: Git is a baseline — it doesn't
"pay" you, it unlocks the roles that do.
SQL is a direct salary lever for analysts.

Skills tracker:

SQL:
□ SELECT, WHERE, ORDER BY
□ GROUP BY + HAVING
□ INNER / LEFT / RIGHT JOINs
□ Subqueries + CTEs
□ Window functions
□ Aggregate functions
□ Query optimisation basics

Git & GitHub:
□ init / clone / status / add / commit
□ push / pull / fetch
□ Branching (checkout -b)
□ Merging + resolving conflicts
□ Pull requests + code review
□ Reading diffs
□ .gitignore basics
□ README + documentation

Best combination:
□ SQL for analysts + Git for their SQL/dbt code
□ Both are baseline in 2026 for data teams