What Ztwin does

01

AI mock interviews

Three formats, each scored a different way — and none of them a black box.

Multiple choice, graded against an answer key. Same answers, same mark.
Coding really executes, in a locked-down sandbox.
Voice scored on the whole transcript, against a published rubric.
Unlimited attempts — the twentieth costs what the first did.

The live call, and the report it produced

02

Interview tracks your institute owns

Four tracks ship today. The part that matters is that you are not limited to them.

Author your own from scratch, or clone a platform track and edit it.
Bulk-upload questions from CSV or Excel, with per-row errors.
Publish on your own schedule; assign to specific student groups.

More platform tracks are being added. Institutes do not have to wait for us — anything you author is available to your students immediately.

Edit interview · Data Analytics 2026Analytics & Python MCQsFive modules · ten rounds · private to Northfield Institute of Technology

Import CSVAdd question

Track structure

Data Analytics 2026Interview

Intro to Analytics + Python Pr…2 rounds

Analytics & Python MCQsmcq

Python Programming Codin…coding_only

SQL + NumPy + Pandas + Mat…2 rounds

SQL & Libraries MCQsmcq

SQL & DataFrame Coding R…coding_only

Statistics + Data Preparation2 rounds

Statistics & Data Prep MCQsmcq

Statistical Reasoning & Dat…voice_only

Machine Learning2 rounds

Machine Learning MCQsmcq

Machine Learning Coding R…coding_only

Excel + R + Tableau + Power BI2 rounds

Tools & Visualisation MCQsmcq

Dashboard Design & Data S…voice_only

Questions (20)mcq · single round

01In the standard analytics workflow, which step immediately precedes building a model?analytics_processjunior

02What does [x*x for x in range(4) if x % 2 == 0] produce?comprehensionsmid

03A Python decorator is essentially:decoratorsmid

04What does functools.wraps preserve when writing a decorator?decoratorsmid

05The main goal of Exploratory Data Analysis (EDA) is to:edajunior

06In Python, which block always runs whether or not an exception is raised?exception_handlingjunior

07Which statement raises an exception explicitly?exception_handlingjunior

08Why is "with open(path) as f:" preferred for file I/O?file_handlingmid

09list(map(lambda x: x+1, [1,2,3])) returns:functionaljunior

10list(filter(lambda x: x % 2 == 0, [1,2,3,4])) returns:functionaljunior

11What does a generator function use to yield values lazily?generatorsmid

03

Resume intelligence

Upload a resume and it becomes structured, editable content. Everything after that is a suggestion.

19 templates across single-column, two-column and sidebar layouts.
ATS analysis against a pasted job description: score, matched and missing keywords.
Tailoring saves a new variant — the original is never overwritten.

ATS analysis · read-onlyIshita Desai — RésuméOptionally paste the job description for keyword matching. Analysis does not alter the résumé.

CloseRun analysis

Job description · pastedRequirements
— Strong fundamentals in data structures and algorithms
— Experience with Python and React
— Familiarity with Java and Docker
— Bachelor’s degree in Computer Science

ATS53Keywords64Format40

JD keyword coverage

Present 6DockerJavaPostgreSQLPythonReactdata structures

Missing 3Bachelor’s degree in Computer ScienceCIalgorithms

Recommendations

highBachelor’s degree in Computer ScienceJob description keyword “Bachelor’s degree in Computer Science” is missing from the résumé.Add it to a relevant section if you have that experience.

highalgorithmsJob description keyword “algorithms” is missing from the résumé.Add it to a relevant section if you have that experience.

mediumCIJob description keyword “CI” is missing from the résumé.Add it to a relevant section if you have that experience.

Format scores 40 because the résumé’s two-column header confuses parsers. Fixing structure lifts every keyword score with it.

The ATS check

04

One job feed, curated by your cell

Three job boards in a single feed, and your office decides who sees what.

Officers configure the searches; students never see an unfiltered scrape.
The same role posted to two boards merges into one entry.
Students search, filter and bookmark; apply-clicks are measurable.

05

Placement drive operations

The full campus lifecycle in one workspace, with every state visible to the students waiting on it.

Eligibility computed from the student record, not assembled by hand.
Panels and time slots, with clashes detected before they bite.
Bulk resume packs for recruiters, and a funnel through to placed.

06

Analytics that end in an action

Every metric is defined once, scoped to the role that should see it, and opens to the students behind it.

Readiness is a published formula, not a black box.
Officer view: who is at risk, who has no resume, who has not practised.
CSV export on every dashboard, and drill-down into any figure.

Seeing it beats reading about it.

Thirty minutes, on your batch sizes and your departments.

Book a demo

One system, from enrolment to offer.