Intake open · 3-month track

Data Science Internship

The job is not the model, it is the decision. You take messy company data and finish with a recommendation someone can act on and defend.

Apply now See the 12-week plan
3Months
HybridFormat
567+Enrolled
FreeTo apply
Revenue. Q3 review
# fit and validate
model.fit(X_train, y_train)
score = evaluate(model, X_val)
Segments analysed14
epoch 1epoch 10
Ends with
A decision, not a chart
Mentor review
Weekly, 1-to-1
  • Python
  • SQL
  • pandas
  • Statistics
  • Power BI
  • A/B Testing
  • Python
  • SQL
  • pandas
  • Statistics
  • Power BI
  • A/B Testing
  • Python
  • SQL
  • pandas
  • Statistics
  • Power BI
  • A/B Testing
  • Python
  • SQL
  • pandas
  • Statistics
  • Power BI
  • A/B Testing
  • Python
  • SQL
  • pandas
  • Statistics
  • Power BI
  • A/B Testing
  • Python
  • SQL
  • pandas
  • Statistics
  • Power BI
  • A/B Testing
  • Python
  • SQL
  • pandas
  • Statistics
  • Power BI
  • A/B Testing
  • Python
  • SQL
  • pandas
  • Statistics
  • Power BI
  • A/B Testing
12Weeks
25 hrsPer week
567+Interns trained
1Analysis report
The work

A chart nobody acts on is a wasted week

You will present findings to people who will push back. If your sample is biased, your metric is vanity or your conclusion does not follow, they will say so, and that is the training.

  • 1You get a business question, not a dataset
  • 2You find, clean and validate the data yourself
  • 3You analyse, then defend the analysis in review
  • 4You present a recommendation with its uncertainty

Weeks 1 to 3 cover Python, SQL and statistics from zero.

analysis / q3-retention.ipynb
Cohort retention38%+6pt QoQ
Sample size12,480users
Lift (A/B)+4.1%p = 0.03
Confidence95%two-tailed
Data completeness9% imputed
Segment coverage14 of 14
Outliers removeddocumented
Reproducible runone command
Skills

Eight things you will actually be able to do

01

Python for analysis

pandas, NumPy and writing analysis someone else can rerun next quarter.

02

SQL

Joins, window functions, CTEs and pulling your own data instead of asking for it.

03

Data cleaning

Missing values, duplicates, inconsistent categories and documenting every decision.

04

Statistics

Distributions, confidence intervals, significance and what a p-value does not mean.

05

Exploratory analysis

Segmenting, cohorting and finding the question behind the question.

06

Predictive modelling

Regression and classification where they genuinely add value over a rule.

07

Visualisation

Charts that answer a question, and dashboards people actually open twice.

08

Communication

Writing and presenting findings to people who will challenge them.

Curriculum

The 12-week plan

Four phases. Each one ends in a deliverable that is reviewed and graded before the next opens.

Foundations

Getting your own data and cleaning it properly.

  • Python, notebooks and reproducible environments
  • pandas: joining, reshaping, grouping, time series
  • SQL: joins, aggregates, window functions, CTEs
  • Data quality checks and documenting assumptions
Deliverable

A cleaned dataset pulled with your own SQL, with a written data quality report.

The capstone

Every intern answers a real business question

You are handed a question from an Ezitech team (why did retention drop, which customers are worth chasing, is this campaign working), and you own it to a presented answer.

  • Data you pulled and cleaned yourself
  • Analysis with stated confidence and limits
  • A recommendation presented and defended live
Start your application
Ezitech seminar session with students at a university auditorium
Ezitech stall at a university open house and job fair
Ezitech receiving a shield at a university project presentation
Ezitech team and interns group photo outside the institute
Ezitech speaker presenting at a talent award ceremony
Ezitech mentors reviewing intern project documents
Ezitech team discussing an intern project brief in the office
01 / 07
Duration12 weeks
Commitment25 hrs / week
FormatOnsite + remote
LocationRawalpindi, PK
LevelJunior / fresher
ReviewsWeekly, 1-to-1
The Ezitech model

What every Ezitech intern gets

Three things that make this different from a course you watch on a screen.

Live client briefs

You work on real Ezitech projects with real deadlines and real revision rounds, not recycled practice exercises.

A mentor, not a playlist

Weekly reviews with someone who does this work for a living, plus a team of interns around you to build with.

Portfolio and experience letter

You finish with shipped work you are allowed to show and a letter that says exactly what you delivered.

And you leave with

  • Verified certificate

    Issued by Ezitech Institute and verifiable by employers.

  • Experience letter

    On company letterhead, stating exactly what you delivered.

  • A decision-ready report

    An analysis with a recommendation and a dashboard behind it.

  • Interview preparation

    CV rewrite, portfolio case study and a technical mock interview.

  • A route into the team

    Top performers are considered first for paid junior roles.

  • Confidence in review

    Twelve weeks of defending your own work to a senior practitioner.

Built for you if

  • You are an undergraduate or fresh graduate in CS, IT, maths, stats, economics or business
  • You are comfortable with numbers and basic programming
  • You can explain a conclusion in plain language
  • You want analyst or data science roles

Not a fit if

  • You want a certificate without doing the work
  • You cannot give 25 hours a week for 12 weeks
  • You are looking for a purely theoretical course
  • You want to skip review and work alone
FAQ

Questions people ask

Everything applicants email us about, answered before you apply.

Still not sure?

Talk to the admissions team before you commit twelve weeks.

Contact Ezitech
Is data science different from machine learning?
There is overlap, but the emphasis differs. Machine learning is about building and shipping models. Data science is about answering a question with data, where a model is one tool among statistics, SQL, segmentation and communication. If you enjoy explaining findings to people, this is the better track.
How many hours a week does it take?
A minimum of 25 hours across 12 weeks, split between onsite sessions, mentor reviews and independent work. Interns who treat it as full-time finish with noticeably stronger portfolios.
Is it remote or onsite?
Hybrid. Onsite days run at the Ezitech office in Rawalpindi; the rest of the week is remote with scheduled check-ins. Candidates outside Rawalpindi and Islamabad can complete the track fully remotely if they attend the weekly review calls.
Do I need a statistics background?
No. Weeks 1 to 3 cover the statistics you actually use (distributions, intervals, significance and sample size), taught around real datasets rather than as abstract theory.
Can final-year students apply?
Yes, and many do. The capstone is regularly used as a final-year project because it is built from a real brief with real requirements. Agree the scope with your mentor in week one if your university has a set submission format.
How do I apply?
Register on the Ezitech intern portal, choose the Data Science track, and complete the three-step form with your education details and CV. Shortlisted candidates get a short screening call before the intake starts.
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Applications open

Turn messy data into a decision

Three months, one shipped piece of real work, a mentor who reviews you every week. Registration takes five minutes.

Apply for this internship Talk to us first
Ezitech Institute · Rawalpindi, Pakistan · info@ezitech.org · +92 345 5555396