dx+=(x[i]-mx)**2;dy+=(y[i]-my)**2;}return n/Math.sqrt(dx*dy);}; function linreg(x,y){const b=corr(x,y)*std(y)/std(x); const a=mean(y)-b*mean(x);return{slope:b,intercept:a};} const forecast=(m,x)=>m.slope*x+m.intercept; // DOSM: GDP growth, CPI, labour force, population census SELECT year, region, AVG(gdp_per_capita) FROM stats GROUP BY year; df.groupby(['state']).agg({'population':'sum','urban_rate':'mean'}); model.fit(X_train,y_train);preds=model.predict(X_test); kpi = { unemployment: 3.2, inflation: 1.8, gdp_growth: 4.7 }; for (const row of dataset) { totals[row.state] += row.value; } const p95 = quantile(samples, 0.95); const mean=a=>a.reduce((s,x)=>s+x,0)/a.length; const variance=a=>{const m=mean(a);return mean(a.map(x=>(x-m)**2));}; const std=a=>Math.sqrt(variance(a)); const forecast=(m,x)=>m.slope*x+m.intercept; // DOSM: GDP growth, CPI, labour force, population census SELECT year, region, AVG(gdp_per_capita) FROM stats GROUP BY year; df.groupby(['state']).agg({'population':'sum','urban_rate':'mean'}); model.fit(X_train,y_train);preds=model.predict(X_test); kpi = { unemployment: 3.2, inflation: 1.8, gdp_growth: 4.7 }; for (const row of dataset) { totals[row.state] += row.value; } const p95 = quantile(samples, 0.95); const mean=a=>a.reduce((s,x)=>s+x,0)/a.length; const variance=a=>{const m=mean(a);return mean(a.map(x=>(x-m)**2));}; const std=a=>Math.sqrt(variance(a)); const zscore=(x,a)=>(x-mean(a))/std(a); const corr=(x,y)=>{const mx=mean(x),my=mean(y); let n=0,dx=0,dy=0;for(let i=0;i<x.length;i++){n+=(x[i]-mx)*(y[i]-my);

df.groupby(['state']).agg({'population':'sum','urban_rate':'mean'}); model.fit(X_train,y_train);preds=model.predict(X_test); kpi = { unemployment: 3.2, inflation: 1.8, gdp_growth: 4.7 }; for (const row of dataset) { totals[row.state] += row.value; } const p95 = quantile(samples, 0.95); const mean=a=>a.reduce((s,x)=>s+x,0)/a.length; const variance=a=>{const m=mean(a);return mean(a.map(x=>(x-m)**2));}; const std=a=>Math.sqrt(variance(a)); const zscore=(x,a)=>(x-mean(a))/std(a); const corr=(x,y)=>{const mx=mean(x),my=mean(y); let n=0,dx=0,dy=0;for(let i=0;i<x.length;i++){n+=(x[i]-mx)*(y[i]-my); dx+=(x[i]-mx)**2;dy+=(y[i]-my)**2;}return n/Math.sqrt(dx*dy);}; function linreg(x,y){const b=corr(x,y)*std(y)/std(x); const a=mean(y)-b*mean(x);return{slope:b,intercept:a};} for (const row of dataset) { totals[row.state] += row.value; } const p95 = quantile(samples, 0.95); const mean=a=>a.reduce((s,x)=>s+x,0)/a.length; const variance=a=>{const m=mean(a);return mean(a.map(x=>(x-m)**2));}; const std=a=>Math.sqrt(variance(a)); const zscore=(x,a)=>(x-mean(a))/std(a); const corr=(x,y)=>{const mx=mean(x),my=mean(y); let n=0,dx=0,dy=0;for(let i=0;i<x.length;i++){n+=(x[i]-mx)*(y[i]-my); dx+=(x[i]-mx)**2;dy+=(y[i]-my)**2;}return n/Math.sqrt(dx*dy);}; function linreg(x,y){const b=corr(x,y)*std(y)/std(x); const a=mean(y)-b*mean(x);return{slope:b,intercept:a};} const forecast=(m,x)=>m.slope*x+m.intercept; // DOSM: GDP growth, CPI, labour force, population census SELECT year, region, AVG(gdp_per_capita) FROM stats GROUP BY year;

for (const row of dataset) { totals[row.state] += row.value; } const p95 = quantile(samples, 0.95); const mean=a=>a.reduce((s,x)=>s+x,0)/a.length; const variance=a=>{const m=mean(a);return mean(a.map(x=>(x-m)**2));}; const std=a=>Math.sqrt(variance(a)); const zscore=(x,a)=>(x-mean(a))/std(a); const corr=(x,y)=>{const mx=mean(x),my=mean(y); let n=0,dx=0,dy=0;for(let i=0;i<x.length;i++){n+=(x[i]-mx)*(y[i]-my); dx+=(x[i]-mx)**2;dy+=(y[i]-my)**2;}return n/Math.sqrt(dx*dy);}; function linreg(x,y){const b=corr(x,y)*std(y)/std(x); const a=mean(y)-b*mean(x);return{slope:b,intercept:a};} const forecast=(m,x)=>m.slope*x+m.intercept; // DOSM: GDP growth, CPI, labour force, population census SELECT year, region, AVG(gdp_per_capita) FROM stats GROUP BY year; const variance=a=>{const m=mean(a);return mean(a.map(x=>(x-m)**2));}; const std=a=>Math.sqrt(variance(a)); const zscore=(x,a)=>(x-mean(a))/std(a); const corr=(x,y)=>{const mx=mean(x),my=mean(y); let n=0,dx=0,dy=0;for(let i=0;i<x.length;i++){n+=(x[i]-mx)*(y[i]-my); dx+=(x[i]-mx)**2;dy+=(y[i]-my)**2;}return n/Math.sqrt(dx*dy);}; function linreg(x,y){const b=corr(x,y)*std(y)/std(x); const a=mean(y)-b*mean(x);return{slope:b,intercept:a};} const forecast=(m,x)=>m.slope*x+m.intercept; // DOSM: GDP growth, CPI, labour force, population census SELECT year, region, AVG(gdp_per_capita) FROM stats GROUP BY year; df.groupby(['state']).agg({'population':'sum','urban_rate':'mean'}); model.fit(X_train,y_train);preds=model.predict(X_test); kpi = { unemployment: 3.2, inflation: 1.8, gdp_growth: 4.7 };

In conjunction with Hari Statistik Negara · MyStats Day 20 Oct

DOSMDatathon2026

Empowering future data innovators through artificial intelligence, data analytics and digital innovation — organised by the Department of Statistics Malaysia.

Explore Competition

Prize Pool: RM 9,600
Duration: Aug – Oct
Format: Physical Live Pitching

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Department of Statistics Malaysia (DOSM)

Know More About Us

The Department of Statistics Malaysia was established in 1949 under the Statistics Ordinance 1949, and was initially known as the Bureau of Statistics. In 1965 it was renamed and began operating under the Statistics Act 1965 — expanding its role to cover data across every economic and social sector.

Today, DOSM is the leading government agency responsible for collecting, interpreting and disseminating up-to-date statistics that monitor the economic and social performance of a developing nation, with headquarters in Putrajaya and state offices across Malaysia.

AI & Analytics
Data-driven Insight
Student Collaboration
National Reach

Theme 2026

"Leveraging ML and AI to develop innovative solutions for sustainable tourism industry in Malaysia"

Weeks

Timeline

From kickoff to the grand finale

Eight milestones across three months — from promotion to the champions being crowned at the national closing ceremony.

1
3 August 2026
Promotion Begins
Nationwide campaign kicks off across DOSM channels and universities.
2
10 – 28 August 2026
Registration
Team sign-ups open to diploma & undergraduate students nationwide.
3
7 September 2026
Competition Briefing
Full briefing session covering rules, datasets and judging criteria.
4
8 – 22 September 2026
Competition Period
Teams work on their prototypes, reports and dashboards.
5
23 Sept – 2 October 2026
Shortlisting Process
Judges review submissions and shortlist top-performing teams.
6
5 October 2026
Top 10 Finalists Announced
Shortlisted finalist teams revealed across DOSM channels.
7
19 October 2026
Live Pitching
Grand finale — top 10 teams pitch live to expert judges.
8
20 October 2026
Winners Announcement
Champions crowned at the national closing ceremony.

1
3 August 2026
Promotion Begins
Nationwide campaign kicks off across DOSM channels and universities.
2
10 – 28 August 2026
Registration
Team sign-ups open to diploma & undergraduate students nationwide.
3
7 September 2026
Competition Briefing
Full briefing session covering rules, datasets and judging criteria.
4
8 – 22 September 2026
Competition Period
Teams work on their prototypes, reports and dashboards.
5
23 Sept – 2 October 2026
Shortlisting Process
Judges review submissions and shortlist top-performing teams.
6
5 October 2026
Top 10 Finalists Announced
Shortlisted finalist teams revealed across DOSM channels.
7
19 October 2026
Live Pitching
Grand finale — top 10 teams pitch live to expert judges.
8
20 October 2026
Winners Announcement
Champions crowned at the national closing ceremony.

Competition theme

Leveraging ML & AI for sustainable tourism in Malaysia

Explore any of these four tourism dimensions. Every submission is judged on rigour, creativity and real-world impact.

Economy

Economic Dimension

Assess tourism's contribution to the local economy through visitor spending, accommodation rates and local enterprise activity.

Social

Social Dimension

Measure tourism's impact on communities — including employment, resident satisfaction and cultural preservation.

Environment

Environmental Dimension

Examine tourism's environmental impact such as pollution, waste and the conservation of natural areas.

Tech & Governance

Technology & Governance Dimension

Leverage technology and data for smarter, more efficient and responsive tourism management.

Why join

More than a competition

A national stage to sharpen applied analytics, connect with the data community, and turn official statistics into ideas that shape Malaysia's future.

Learn by doing

Work with real DOSM microdata and hone applied analytics skills that matter after graduation.

Build your network

Meet peers, advisors and industry judges from Malaysia's data community.

Win recognition

Cash prizes, certificates and features on the official DOSM channels.

Launch your career

Top finalists are fast-tracked for DOSM internships and industry interviews.

Eligibility

General Requirements

Diploma & Undergraduate students from Malaysian higher institutions
Teams of 4 members and 1 academic advisor

Format

Competition Mode

Video presentation
Report & dashboard submission
Physical live pitching

Prize pool

RM 9,600 in cash prizes

A total cash prize pool of RM 9,600 awaits the top finalist teams — alongside certificates and recognition at the national closing ceremony.

RM 0

Total cash prize pool

Eligibility: Diploma & Undergraduate

RM 9,600

Cash Prize Pool

Distributed across the top finalist teams at the live pitching finale.

Official

Certificates & Recognition

DOSM-endorsed certificates and national recognition for all finalists.

By the numbers

Learn More DOSM In Stats

0Year

DOSM Establishment

0+Year

Digital Publication

0+Million

Downloads Output

0+Million

Interactive Communication

Partners & Collaborators

Built with Malaysia's data community

DOSM

Department of Statistics Malaysia

NBDAC

National Big Data Analytic Centre

Hari Statistik Negara

MyStats Day

Kementerian Ekonomi

Ministry of Economy

FAQ

Answers before you register

Everything you need to know about eligibility, format, prizes and support during the competition.

Registration is open

Ready to shape Malaysia's data future?

Join Malaysia's premier student datathon — free to enter, national-scale impact, and RM 9,600 in cash prizes.

Registration closes 28 August 2026

DOSMDatathon2026

Know More About Us

From kickoff to the grand finale

Promotion Begins

Registration

Competition Briefing

Competition Period

Shortlisting Process

Top 10 Finalists Announced

Live Pitching

Winners Announcement