MSc

MSc Data Analytics

ماجستير تحليل البيانات. 6 assessed courses, 1,800 notional hours, tuition-free.

Online & New Capital Campus

4 Semesters (2 Years)180 creditsEnglish

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Overview

ماجستير تحليل البيانات. MSc Data Analytics at Imperial University in Cairo: 6 assessed courses, 180 credits and 1,800 notional learning hours, at 10 hours per credit. The hours on each course add up to that total. The award is fully online. There is no campus to attend. MSc Data Analytics is taught as one award, not a list of unrelated modules. The published length is 4 Semesters (2 Years), and the credit total is 180. Students move from Questions, measures, and dirty data through Questions, measures, and dirty data; Analysis and the claim it supports; Dashboards for a decision; Data responsibility; Briefing a non-specialist; Capstone: analytics file, and finish on Capstone: analytics file. Each course has a question, a method, and a submission. In this field, students define a measurable question, fit a method that can be checked, and report where it fails. Reading is a dataset description, a methods note, and one published evaluation. The artefact a marker expects is a project note with the question, the method, the result, and the limitation. The award is built so that a graduate can do the following in practice: Produce a professional file in Data Analytics Choose a method and write what the evidence cannot support Keep a record a third party can review Teaching assumes the student can read a source, attempt a problem before the seminar, and revise after feedback. Attendance at live seminars is part of the design. The capstone or final course must use the methods of the earlier courses; a project that ignores them does not pass. The pages for each course name the topics that are examined. Those topics are the syllabus. A brochure line is not a substitute for them.

Each credit is 10 notional learning hours. The courses add up to 180 credits and 1,800 hours.

Who This Programme Is For

People already working around Data Analytics
Career-changers who can study without leaving their city
Teams who need a file they can review, not a slogan

Faculty spotlight

Imperial University in Cairo supervises the courses and the final assessed file. Supervision is assigned from the school that owns the award.

How you can study

Digital only

Study online

100% tuition-free online

Online tuition is completely free. Study from anywhere in Egypt, the Arab region, and worldwide with full digital flexibility.

✦ Tuition: £0 / $0
✦ Examinations are free. There is no exam fee.
✦ Learn from anywhere · Live online seminars

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What You Will Learn

Produce a professional file in Data Analytics
Choose a method and write what the evidence cannot support
Keep a record a third party can review
State the limit of the award
Submit the final project inside the published hours

Curriculum

Stage 1 — 900 hours

Code	Course	Credits · hours
S108201	Questions, measures, and dirty data Questions, measures, and dirty data (S108201) is a 30-credit course on MSc Data Analytics, with 300 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Questions, measures, and dirty data as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. S108201-M1 · The question, the unit of analysis, and data quality — Questions, measures, and dirty data S108201-M2 · Features, baselines, and error — Questions, measures, and dirty data S108201-M3 · Validation, leakage, and what the model must not decide — Questions, measures, and dirty data S108201-M4 · Monitoring, drift, and human review — Questions, measures, and dirty data S108201-M5 · Interpretation for a non-technical decision maker — Questions, measures, and dirty data S108201-M6 · A supervised project with method, result, and limitation — Questions, measures, and dirty data	30 · 300 h
SZY2I02	Analysis and the claim it supports Analysis and the claim it supports (SZY2I02) is a 30-credit course on MSc Data Analytics, with 300 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Analysis and the claim it supports as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. SZY2I02-M1 · The question, the unit of analysis, and data quality — Analysis and the claim it supports SZY2I02-M2 · Features, baselines, and error — Analysis and the claim it supports SZY2I02-M3 · Validation, leakage, and what the model must not decide — Analysis and the claim it supports SZY2I02-M4 · Monitoring, drift, and human review — Analysis and the claim it supports SZY2I02-M5 · Interpretation for a non-technical decision maker — Analysis and the claim it supports SZY2I02-M6 · A supervised project with method, result, and limitation — Analysis and the claim it supports	30 · 300 h
SZO2X03	Dashboards for a decision Dashboards for a decision (SZO2X03) is a 30-credit course on MSc Data Analytics, with 300 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Dashboards for a decision as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. SZO2X03-M1 · The question, the unit of analysis, and data quality — Dashboards for a decision SZO2X03-M2 · Features, baselines, and error — Dashboards for a decision SZO2X03-M3 · Validation, leakage, and what the model must not decide — Dashboards for a decision SZO2X03-M4 · Monitoring, drift, and human review — Dashboards for a decision SZO2X03-M5 · Interpretation for a non-technical decision maker — Dashboards for a decision SZO2X03-M6 · A supervised project with method, result, and limitation — Dashboards for a decision	30 · 300 h

Code

Course

Credits · hours

S108201

Questions, measures, and dirty data

Questions, measures, and dirty data (S108201) is a 30-credit course on MSc Data Analytics, with 300 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Questions, measures, and dirty data as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.

S108201-M1 · The question, the unit of analysis, and data quality — Questions, measures, and dirty data
S108201-M2 · Features, baselines, and error — Questions, measures, and dirty data
S108201-M3 · Validation, leakage, and what the model must not decide — Questions, measures, and dirty data
S108201-M4 · Monitoring, drift, and human review — Questions, measures, and dirty data
S108201-M5 · Interpretation for a non-technical decision maker — Questions, measures, and dirty data
S108201-M6 · A supervised project with method, result, and limitation — Questions, measures, and dirty data

30 · 300 h

SZY2I02

Analysis and the claim it supports

Analysis and the claim it supports (SZY2I02) is a 30-credit course on MSc Data Analytics, with 300 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Analysis and the claim it supports as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.

SZY2I02-M1 · The question, the unit of analysis, and data quality — Analysis and the claim it supports
SZY2I02-M2 · Features, baselines, and error — Analysis and the claim it supports
SZY2I02-M3 · Validation, leakage, and what the model must not decide — Analysis and the claim it supports
SZY2I02-M4 · Monitoring, drift, and human review — Analysis and the claim it supports
SZY2I02-M5 · Interpretation for a non-technical decision maker — Analysis and the claim it supports
SZY2I02-M6 · A supervised project with method, result, and limitation — Analysis and the claim it supports

30 · 300 h

SZO2X03

Dashboards for a decision

Dashboards for a decision (SZO2X03) is a 30-credit course on MSc Data Analytics, with 300 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Dashboards for a decision as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.

SZO2X03-M1 · The question, the unit of analysis, and data quality — Dashboards for a decision
SZO2X03-M2 · Features, baselines, and error — Dashboards for a decision
SZO2X03-M3 · Validation, leakage, and what the model must not decide — Dashboards for a decision
SZO2X03-M4 · Monitoring, drift, and human review — Dashboards for a decision
SZO2X03-M5 · Interpretation for a non-technical decision maker — Dashboards for a decision
SZO2X03-M6 · A supervised project with method, result, and limitation — Dashboards for a decision

30 · 300 h

Stage 2 — 900 hours

Code	Course	Credits · hours
S11M004	Data responsibility Data responsibility (S11M004) is a 30-credit course on MSc Data Analytics, with 300 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Data responsibility as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. S11M004-M1 · The question, the unit of analysis, and data quality — Data responsibility S11M004-M2 · Features, baselines, and error — Data responsibility S11M004-M3 · Validation, leakage, and what the model must not decide — Data responsibility S11M004-M4 · Monitoring, drift, and human review — Data responsibility S11M004-M5 · Interpretation for a non-technical decision maker — Data responsibility S11M004-M6 · A supervised project with method, result, and limitation — Data responsibility	30 · 300 h
S11C005	Briefing a non-specialist Briefing a non-specialist (S11C005) is a 30-credit course on MSc Data Analytics, with 300 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Briefing a non-specialist as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. S11C005-M1 · The question, the unit of analysis, and data quality — Briefing a non-specialist S11C005-M2 · Features, baselines, and error — Briefing a non-specialist S11C005-M3 · Validation, leakage, and what the model must not decide — Briefing a non-specialist S11C005-M4 · Monitoring, drift, and human review — Briefing a non-specialist S11C005-M5 · Interpretation for a non-technical decision maker — Briefing a non-specialist S11C005-M6 · A supervised project with method, result, and limitation — Briefing a non-specialist	30 · 300 h
S112006	Capstone: analytics file Capstone: analytics file (S112006) is a 30-credit course on MSc Data Analytics, with 300 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Capstone: analytics file as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. S112006-M1 · The question, the unit of analysis, and data quality — Capstone: analytics file S112006-M2 · Features, baselines, and error — Capstone: analytics file S112006-M3 · Validation, leakage, and what the model must not decide — Capstone: analytics file S112006-M4 · Monitoring, drift, and human review — Capstone: analytics file S112006-M5 · Interpretation for a non-technical decision maker — Capstone: analytics file S112006-M6 · A supervised project with method, result, and limitation — Capstone: analytics file	30 · 300 h

Code

Course

Credits · hours

S11M004

Data responsibility

Data responsibility (S11M004) is a 30-credit course on MSc Data Analytics, with 300 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Data responsibility as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.

S11M004-M1 · The question, the unit of analysis, and data quality — Data responsibility
S11M004-M2 · Features, baselines, and error — Data responsibility
S11M004-M3 · Validation, leakage, and what the model must not decide — Data responsibility
S11M004-M4 · Monitoring, drift, and human review — Data responsibility
S11M004-M5 · Interpretation for a non-technical decision maker — Data responsibility
S11M004-M6 · A supervised project with method, result, and limitation — Data responsibility

30 · 300 h

S11C005

Briefing a non-specialist

Briefing a non-specialist (S11C005) is a 30-credit course on MSc Data Analytics, with 300 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Briefing a non-specialist as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.

S11C005-M1 · The question, the unit of analysis, and data quality — Briefing a non-specialist
S11C005-M2 · Features, baselines, and error — Briefing a non-specialist
S11C005-M3 · Validation, leakage, and what the model must not decide — Briefing a non-specialist
S11C005-M4 · Monitoring, drift, and human review — Briefing a non-specialist
S11C005-M5 · Interpretation for a non-technical decision maker — Briefing a non-specialist
S11C005-M6 · A supervised project with method, result, and limitation — Briefing a non-specialist

30 · 300 h

S112006

Capstone: analytics file

Capstone: analytics file (S112006) is a 30-credit course on MSc Data Analytics, with 300 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Capstone: analytics file as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.

S112006-M1 · The question, the unit of analysis, and data quality — Capstone: analytics file
S112006-M2 · Features, baselines, and error — Capstone: analytics file
S112006-M3 · Validation, leakage, and what the model must not decide — Capstone: analytics file
S112006-M4 · Monitoring, drift, and human review — Capstone: analytics file
S112006-M5 · Interpretation for a non-technical decision maker — Capstone: analytics file
S112006-M6 · A supervised project with method, result, and limitation — Capstone: analytics file

30 · 300 h

Career Paths

Data analyst
Business intelligence analyst
Reporting lead
Analytics translator
Insight manager

Programme Highlights

180 credits · 1,800 notional hours · 6 courses
Course hours add up to the programme total
ماجستير تحليل البيانات
The award is fully online. There is no campus to attend.

Assessment

Modules are assessed through a published mix of coursework, applied projects, and examinations. Exam windows are announced in advance so students in other time zones are not forced into overnight sittings. Alternative arrangements are available where documented.

Admission requirements

Academic entry

A bachelor's degree from a recognised institution, or equivalent professional standing considered by admissions

Documents

A statement of about 500 words on why this award fits your work
Two references, academic or professional
A CV

Programme questions

How long is the MSc Data Analytics?

The published duration is 4 Semesters (2 Years). Teaching language: English. Actual time-to-complete depends on mode and any recognised prior learning.

Can I study this from my country?

You may study this award fully online from your country, or — where published — on campus at IUC. Online study does not require a student visa. Campus study may.

What does it cost?

Degree tuition for this award is published as £0 / tuition-free on the online pathway. Examination or administrative fees may apply at checkout — never an annual tuition invoice. Check the Fees page for any extras.

What are the entry requirements?

Requirements are grouped on this page (academic, English, documents). Equivalent qualifications are considered. English may be waived after prior English-medium study.

How is the programme assessed?

Assessment is typically a mix of coursework, projects, and examinations. Doctoral awards include a thesis or dissertation and an oral examination. Details sit in the programme specification and module outlines.

Will this award be recognised where I live?

Recognition of the award for local employment, professional licence, or ministry attestation is decided by your employer or regulator. Imperial University in Cairo publishes verification pages for certificates. We do not claim automatic equivalence in every country.

How do I apply?

Start an application on this website. Progress is saved from the first step. Admissions: admissions@iucairo.co.uk.

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