Skill Mentions Across 100+ Data Analyst JDs (indicative):

SQL                     ~90 of 100 JDs
Excel / Google Sheets   ~85 of 100 JDs
Power BI or Tableau     ~72 of 100 JDs
Communication skills    ~70 of 100 JDs
Python                  ~55 of 100 JDs
Statistics / analysis   ~45 of 100 JDs
Data cleaning / ETL     ~40 of 100 JDs
Cloud (AWS/Azure/GCP)   ~20 of 100 JDs
Git / version control   ~15 of 100 JDs
GenAI / AI tools        ~12 of 100 JDs

Three Things That Stood Out:

1. Excel is not dead.
   It appears almost as often as SQL, usually with
   pivot tables, lookups and reporting duties.

2. "Communication" is a hard requirement, not a filler.
   Most JDs ask for stakeholder reporting or presenting
   findings to non-technical teams.

3. Machine learning is rarely asked at analyst level.
   Only a small share of JDs mention ML models -
   most want reporting, analysis and clean dashboards.

SQL Topics Repeated Across JDs:
- INNER / LEFT / RIGHT JOIN on multiple tables
- GROUP BY with HAVING and aggregate functions
- Subqueries and CTEs for step-by-step logic
- Window functions: ROW_NUMBER, RANK, running totals
- Date filtering, month-on-month comparisons
- CASE WHEN for segmentation and flags
- Handling NULLs correctly in aggregations

If you can solve a business question in SQL,
you clear the most common screening test.

Excel + Dashboard Expectations in JDs:

Excel:
- Pivot tables and pivot charts
- VLOOKUP / INDEX-MATCH / XLOOKUP
- Text and date functions for cleaning
- Conditional formatting, data validation
- Basic automation with formulas (macros rarely asked)

Power BI / Tableau:
- Connect and clean data before visualising
- Choose the right chart for the question
- Build KPI cards, filters and drill-downs
- Write simple measures (DAX / calculated fields)
- Keep the dashboard readable for business users

Python Topics Analyst JDs Ask For:
- pandas: read_csv, dropna, fillna, groupby, merge, pivot
- numpy basics for numeric operations
- matplotlib / seaborn for quick charts
- Writing small reusable functions
- Reading data from Excel, CSV and databases

Rarely asked at analyst level:
- Deep learning, NLP pipelines, model deployment
Focus on cleaning and analysis, not fancy models.

Statistics Concepts Seen in JDs:
- Descriptive stats: mean, median, mode, variance
- Distribution and skewness basics
- Correlation vs causation
- Sampling and sample size intuition
- Hypothesis testing and p-value basics
- A/B testing design and reading results
- Confidence intervals at a conceptual level

Interviewers test understanding, not formulas.
Be ready to explain each concept with a business example.

Keywords Repeated Across Data Analyst JDs:
SQL, Excel, Power BI, Tableau, Python, pandas,
data cleaning, data visualization, dashboards, KPIs,
reporting, stakeholder management, ad-hoc analysis,
trend analysis, root cause analysis, data quality,
business insights, documentation, A/B testing

How to use them:
- Mirror the exact JD wording in your resume
- Attach each keyword to a project or result
- Never list a keyword you cannot defend in interview

What Soft Skill Lines Really Mean:

"Strong communication skills"
  → Explain a number to someone who hates numbers

"Attention to detail"
  → Your report should not break on edge cases

"Stakeholder management"
  → Ask the right questions before building anything

"Self-starter / ownership"
  → Find the problem yourself, do not wait for tickets

"Business acumen"
  → Know which metric the company actually earns from

60-Day Plan Based on JD Demand:

Weeks 1-3: The non-negotiables
- SQL daily: joins, aggregation, window functions
- Excel: pivots, lookups, cleaning a messy sheet
- Output: one clean analysis of a real dataset

Weeks 4-5: Reporting layer
- Power BI or Tableau (pick one, go deep)
- Build a KPI dashboard with filters and drill-down
- Output: shareable dashboard + short write-up

Weeks 6-7: Analysis layer
- Python pandas for cleaning and grouping
- Statistics: correlation, testing, A/B basics
- Output: a notebook with 3 clear business insights

Week 8-9: Go to market
- Rewrite resume using exact JD keywords
- Push projects to GitHub with README files
- Apply daily and take 2 mock interviews

Helpful Resources:

Free:
- Public datasets for SQL, Excel and BI practice
- SQL practice platforms with interview-style problems
- Job portals to read fresh JDs every week

Paid / Structured:
- Uncodemy - Data Analytics Training Course (Noida)
- Uncodemy - Data Analytics using Python
- Resume, portfolio review and mock interview sessions

Habit that works:
Read 5 new job descriptions every week.
Your syllabus should update as the market updates.