Quantium Data Analytics

Why complete this job simulation

A risk-free way to experience work on the job with us at Quantium. Practice your skills with example tasks and build your confidence to ace your applications.

Self-paced

4-5 hours

No grades

No assessments

Advanced

Founded in 2002, Quantium has a long history of innovation in data science across all sectors of the economy. As a rapidly growing global leader in data science and AI, we are dedicated to growing our team of ambitious, diverse, and fun employees! Our team of employees work together to harness the power of data to drive revolutionary change that benefits us all and deliver the best results for our clients.

Through this program, you will act as a data analyst at Quantium and take part in the challenging but fun work our data analysts do daily! You will learn practical skills such as data validation, data visualisation, statistical testing and more. After completing each module, you will be able to compare your work with real model solutions created by the Quantium team for a chance to learn directly industry leaders.

We are so thrilled to be a part of your career journey and hope that this virtual job simulation will give you a glimpse of life at Quantium. Start our self-paced program today and showcase your unique talents!

How it works

Complete tasks guided by pre-recorded videos and example answers from our team at Quantium. No live sessions, all self-paced.
Earn a certificate and add it to your resume and LinkedIn as an extra curricular activity.
Stand out in applications. Confidently answer interview questions and explain why you’re a good fit for our team.

Skills you will learn and practice:

Commercial Thinking

Communication Skills

Data Analysis

Data Cleaning

Data Validation

Data Visualization

Presentation Skills

Programming

Statistical Analysis

Introduction from Quantium

Hear Raechel Gavin, Group Executive, People & Culture at Quantium, introduce the simulation.

Tasks

Task One: Data preparation and customer analytics

1-2 hoursAdvanced

Conduct analysis on your client's transaction dataset and identify customer purchasing behaviours to generate insights and provide commercial recommendations.

What you'll learn

Understand how to examine and clean transaction and customer data.
Learn to identify customer segments based on purchasing behavior.
Gain experience in creating charts and graphs to present data insights.
Learn how to derive commercial recommendations from data analysis.

What you'll do

Analyze transaction and customer data to identify trends and inconsistencies.
Develop metrics and examine sales drivers to gain insights into overall sales performance.
Create visualizations and prepare findings to formulate a clear recommendation for the client's strategy.

Task One: Data preparation and customer analytics

1-2 hoursAdvanced

Conduct analysis on your client's transaction dataset and identify customer purchasing behaviours to generate insights and provide commercial recommendations.

What you'll learn

Understand how to examine and clean transaction and customer data.
Learn to identify customer segments based on purchasing behavior.
Gain experience in creating charts and graphs to present data insights.
Learn how to derive commercial recommendations from data analysis.

What you'll do

Analyze transaction and customer data to identify trends and inconsistencies.
Develop metrics and examine sales drivers to gain insights into overall sales performance.
Create visualizations and prepare findings to formulate a clear recommendation for the client's strategy.

Reviews

4.6

Decision: Keep Reasoning: The original labeler correctly assigned Bug/Technical for intent (an authentication error occurring despite a password reset), Negative for sentiment (the user explicitly states "This is really annoying"), and No for PII Flag as no personally identifiable information is present.

Student

The simulation could be improved by providing more varied examples with clearer context, especially cases where intent and sentiment are harder to distinguish. Adding brief feedback after each decision would also help labelers understand why a choice was correct or how their reasoning could be improved.

Student

I enjoyed working on a realistic retail analytics case study that involved data cleaning, customer segmentation, store performance analysis, and presenting business recommendations. The simulation provided practical experience in applying data analytics techniques to solve real business problems and improved my analytical and presentation skills.

Student