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Banking Quant Mastery: Arithmetic to Data Sufficiency
Module 5: Data Handling and Decision Technique
1. Number System, Simplification and Approximation for Banking Exams
2. Ratio, Proportion and Partnership Without Slow Algebra
3. Percentage Mastery for Speed and Accuracy
4. Profit, Loss, Discount and Marked Price
5. Simple Interest vs Compound Interest
6. Average and Ages Problem Framework
10. Mixture and Alligation Made Practical
7. Time and Work, Efficiency, Pipes and Cisterns
8. Speed, Time and Distance Shortcuts
9. Boats and Streams with Relative Speed Logic
11. Mensuration Formulas That Actually Matter
12. Permutation, Combination and Probability Basics
13. Number Series Pattern Recognition
14. Inequality and Order-Based Comparison
15. Data Interpretation for Banking Mocks
16. Data Sufficiency Decision Method
CONTENTS

15. Data Interpretation for Banking Mocks

Read tables, line comparisons, ratios, and percentages efficiently without depending on chart images.

banking quant
data interpretation
tables
charts
percentages
May 18, 202614 views0 likes0 fires
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Why This Chapter Matters

DI is where arithmetic accuracy and time pressure collide. The job is not just to compute; it is to decide what must be computed and what can be approximated.

Core Ideas

  • Read the unit, the base total, and the year labels before solving.
  • If options are wide apart, approximation is usually enough.
  • Tables often become faster than graphs because every value is explicit.
  • Mixed graphs can usually be decomposed into one percentage step and one ratio step.
  • Production-ready DI content can convert chart images into text tables without losing the actual logic of the set.
  • When percentages are based on a total pool, first convert the pool into absolute values before comparing categories.
  • In multi-year tables, identify whether the question needs absolute change, percentage change, ratio, or average before touching the numbers.

High-Value Formulas

ConceptFormula / Rule
Percentage from part and totaltotalpart​×100
Average from grouped valuesnsum of values​
Export-import ratioratio=importsexports​
Net differencedeposits−withdrawals
Weighted category valuetotal×100category %​

How To Approach Questions

  1. Mark the exact dataset the question needs.
  2. Avoid computing unrelated rows or columns.
  3. When totals are given in percentages, convert them one layer at a time.
  4. If a graph is missing, restate the usable values in text form before solving.
  5. If multiple questions use the same set, compute one or two reusable intermediate values once and reuse them.

Worked Examples

Example 1

Prompt: Use a table-based set when the image is not available. Rebuild only the data you need in text form.

Approach: This is the safest production approach for mobile-friendly DI content because it preserves the logic without copying source graphics.

Example 2

Prompt: A ratio chart says company A had export-import ratio 175:100 in a year and imports were later increased by 40%. What is the new export-import ratio if exports stay fixed?

Approach: Treat exports as 175x and imports as 100x. The increased imports become 140x, so the new ratio is 140175​=1.25.

Example 3

Prompt: A budget pie is converted into text: salaries 35%, rent 25%, marketing 20% of a total spend of 24 lakhs. Find the salary spend.

Approach: Salary spend =35% of 24=8.4 lakhs.

Example 4

Prompt: A branch table shows deposits 72 lakh and withdrawals 54 lakh. Find the net inflow.

Approach: Net inflow =72−54=18 lakh.

Common Mistakes

  • Missing the unit scale such as hundreds, thousands, or crores.
  • Doing exact arithmetic when option gaps allow approximation.
  • Reading the wrong row because the chart labels were skimmed.
  • Mixing enrolled counts with passed counts from different parts of the same set.
  • Using a category percentage directly without checking whether it is of the total, of a subgroup, or of the previous year.

Quick Revision

Good DI performance is a mix of selective reading, fast percentage logic, and disciplined approximation.

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hardBanking Quantitative Aptitude
Chapter Mock 15: Data Interpretation
31 questions24 min
Lesson 1 of 2 in Module 5: Data Handling and Decision Technique
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