Data Analyst Resume Examples and How to Write
Struggling to secure data analyst positions in Australia’s rapidly expanding tech and analytics market? You’re entering a field where technical expertise, analytical thinking, and business acumen are equally critical for success. Whether you’re a recent graduate with strong quantitative skills seeking your first analytics role, an experienced professional transitioning from finance or research into data analysis, or a seasoned analyst looking to advance to senior positions with major corporations, your resume must demonstrate both your technical proficiency and your ability to translate complex data into actionable business insights.
This comprehensive guide will help you create an exceptional data analyst resume tailored specifically for Australia’s dynamic data and technology sector. From highlighting your proficiency with industry-standard analytics tools and programming languages to demonstrating your experience with data visualisation and statistical analysis, we’ll show you how to position yourself as a valuable asset to organisations seeking data-driven decision making. Learn what hiring managers and tech leads at companies advertising on SEEK and LinkedIn are seeking in today’s data-driven business landscape.
Data Analyst Resume (Text Version)
Data Analyst📧 [email protected]
📱 0423 789 456
📍 Melbourne, VIC 3000
💼 linkedin.com/in/jessicachen-data
🌐 github.com/jessicachen-analytics
PROFESSIONAL SUMMARY
Results-driven Data Analyst with 5+ years of experience transforming complex datasets into actionable business insights across finance, retail, and technology sectors. Proven expertise in Python, SQL, and advanced analytics with track record of delivering projects that improved operational efficiency by 25% and increased revenue by $2.3M annually. Strong foundation in statistical analysis, machine learning, and data visualisation with demonstrated ability to communicate technical findings to non-technical stakeholders and drive data-informed decision making.
CORE COMPETENCIES
• Advanced Statistical Analysis & Hypothesis Testing
• SQL Database Querying & Data Manipulation
• Python Programming (Pandas, NumPy, Scikit-learn)
• Data Visualisation (Tableau, Power BI, Matplotlib)
• Machine Learning & Predictive Modelling
• Business Intelligence & Dashboard Development
• A/B Testing & Experimental Design
• Cross-Functional Stakeholder Communication
TECHNICAL SKILLS
Programming Languages: Python, R, SQL, VBA, JavaScript
Analytics Tools: Tableau, Power BI, Excel (Advanced), Google Analytics, SAS
Databases: MySQL, PostgreSQL, MongoDB, Snowflake, BigQuery
Cloud Platforms: AWS (S3, Redshift, EC2), Azure, Google Cloud Platform
Statistical Software: SPSS, Stata, R Studio
Other Tools: Git, Jupyter Notebooks, Docker, Airflow
PROFESSIONAL EXPERIENCE
Senior Data Analyst
CommBank Australia, Sydney NSW
March 2021 – Present
• Lead data analytics initiatives for retail banking division, analysing customer behaviour data from 8M+ active accounts
• Develop predictive models for credit risk assessment, reducing default rates by 18% and saving $4.2M annually in potential losses
• Design and implement automated ETL pipelines processing 2TB+ of transaction data daily using Python and AWS services
• Create executive-level dashboards using Tableau, providing real-time insights on KPIs to C-suite leadership team
• Collaborate with product managers and engineering teams to optimise digital banking features, increasing user engagement by 32%
• Conduct A/B testing for marketing campaigns, improving conversion rates by 24% and generating $1.8M additional revenue
• Mentor junior analysts and data scientists, contributing to 90% team retention rate and accelerated skill development
Data Analyst
Woolworths Group, Melbourne VIC
January 2019 – February 2021
• Analysed sales and inventory data across 1,000+ retail locations to optimise supply chain efficiency and reduce waste
• Developed customer segmentation models using clustering algorithms, enabling targeted marketing strategies that increased customer lifetime value by 28%
• Built automated reporting systems reducing manual analysis time by 60% and improving data accuracy to 99.5%
• Performed statistical analysis on pricing strategies, identifying optimal price points that maximised profit margins
• Created interactive Power BI dashboards for store managers, providing insights on local market trends and performance metrics
• Collaborated with merchandising teams to forecast demand patterns, reducing overstock by 15% and stockouts by 22%
• Presented findings to senior leadership, influencing strategic decisions on market expansion and product positioning
Junior Data Analyst
Deloitte Australia, Melbourne VIC
June 2017 – December 2018
• Supported consulting engagements by conducting quantitative analysis for clients across healthcare, government, and financial services
• Performed data cleaning and preparation on large datasets (100K+ records) ensuring data quality and integrity
• Developed financial models and scenario analyses helping clients evaluate investment opportunities worth $50M+
• Created data visualisations and reports for client presentations, contributing to successful project deliveries
• Utilised SQL for data extraction and manipulation from various client database systems
• Assisted senior consultants with market research and competitive analysis across multiple industry sectors
• Gained exposure to diverse analytical methodologies and business intelligence tools across consulting projects
Research Assistant (Data Analytics)
University of Melbourne – Department of Economics, Melbourne VIC
February 2016 – May 2017
• Conducted econometric analysis for academic research projects examining labour market dynamics and economic policy impacts
• Collected, cleaned, and analysed complex datasets from Australian Bureau of Statistics and international economic databases
• Performed regression analysis and statistical testing using R and Stata for peer-reviewed economic research
• Co-authored 3 research papers published in Australian economic journals with focus on quantitative methodology
• Presented research findings at 2 national economics conferences, receiving recognition for analytical rigor
• Collaborated with faculty members on government-funded research projects totalling $200K in grants
EDUCATION
Master of Data Science
University of Melbourne, Melbourne VIC
2017
High Distinction | GPA: 3.9/4.0
Capstone Project: “Machine Learning Applications in Fraud Detection for Financial Services”
Relevant Coursework: Statistical Learning, Database Systems, Data Mining, Business Analytics
Bachelor of Commerce (Econometrics & Business Statistics)
Monash University, Melbourne VIC
2015
First Class Honours | Dean’s List 2013-2015
Honours Thesis: “Predictive Modelling of Australian Housing Market Trends”
Major: Econometrics | Minor: Computer Science
KEY PROJECTS & ACHIEVEMENTS
Customer Churn Prediction Model – CommBank (2023)
• Developed machine learning model predicting customer churn with 89% accuracy using Python and scikit-learn
• Implemented early intervention strategies based on model insights, reducing churn rate by 12%
• Model now used across all retail banking channels, impacting retention strategies for 5M+ customers
Dynamic Pricing Algorithm – Woolworths (2020)
• Created automated pricing algorithm analysing competitor prices, demand elasticity, and inventory levels
• Deployed solution across 500+ stores resulting in 8% increase in gross margins
• Algorithm processes 50,000+ price points daily and adjusts pricing in real-time
Healthcare Cost Analysis – Deloitte Client (2018)
• Conducted comprehensive analysis of healthcare expenditure patterns for state government client
• Identified $15M in potential cost savings through data-driven service delivery optimisation
• Delivered actionable recommendations leading to policy changes affecting 2M+ residents
CERTIFICATIONS & PROFESSIONAL DEVELOPMENT
• Google Cloud Professional Data Engineer – 2023
• Tableau Desktop Certified Associate – 2022
• AWS Certified Data Analytics – Specialty – 2022
• Microsoft Power BI Data Analyst Associate – 2021
• SAS Certified Statistical Business Analyst – 2020
• Python for Data Science Certificate – Coursera/University of Michigan – 2019
PUBLICATIONS & PRESENTATIONS
• “Machine Learning Applications in Banking: A Risk Management Perspective” – Australian Fintech Conference, 2023
• “Retail Analytics: Transforming Customer Experience Through Data” – Data Science Melbourne Meetup, 2022
• Co-author: “Economic Impact of Digital Transformation in Australian Retail” – Economic Papers Journal, 2021
AWARDS & RECOGNITION
• Data Innovation Award – CommBank Excellence Awards, 2023
• Outstanding Consultant Recognition – Deloitte Australia, 2018
• Best Capstone Project – University of Melbourne Data Science Programme, 2017
• Academic Excellence Scholarship – Monash University, 2013-2015
PROFESSIONAL ASSOCIATIONS
• Member, Australian Computer Society (ACS)
• Member, Statistical Society of Australia (SSA)
• Member, Institute of Analytics Professionals of Australia (IAPA)
• Active Participant, Data Science Melbourne Meetup Group
What is The Best Format for a Data Analyst Resume?
The reverse chronological format is ideal for data analyst positions as it clearly demonstrates your progression in analytical complexity, technical skills development, and increasing impact on business outcomes. This format allows hiring managers to quickly assess your experience with data analysis tools, project outcomes, and professional growth in the analytics field.
Font and Formatting Guidelines:
Choose clean, modern fonts such as Arial, Calibri, or Helvetica that reflect technical professionalism and attention to detail. Use 14-16pt for your name and contact information, 12pt for section headings, and 10-11pt for body text. Maintain consistent 2.5cm margins and ensure adequate white space for readability. Save as PDF to preserve formatting when submitting to tech companies and data-driven organisations.
Essential Resume Sections:
Header: Include your full name, professional title, mobile number, professional email address, city and state, LinkedIn profile URL, and importantly, your GitHub profile or portfolio website to showcase your technical work.
Professional Summary: Create a compelling 3-4 line overview highlighting your analytical experience, technical expertise, key achievements, and ability to drive business value through data insights.
Technical Skills: Include a dedicated section showcasing your proficiency with programming languages, analytics tools, databases, and platforms, organised by category for easy scanning.
Experience: Present your analytical roles in reverse chronological order, emphasising quantifiable business impact, technical implementations, and data-driven solutions delivered.
Additional Sections: Highlight education, key projects, certifications, publications, and professional associations that demonstrate your commitment to analytics excellence and continuous learning.
What Experience Should Be on Your Data Analyst Resume?
Your experience section should demonstrate your ability to extract meaningful insights from complex data and translate them into business value. Focus on specific projects delivered, analytical methodologies used, business problems solved, and quantifiable outcomes achieved that show your impact as a data professional.
Highlight experiences that demonstrate your proficiency with data analysis tools, statistical methods, data visualisation, and your ability to communicate findings effectively to both technical and non-technical stakeholders in business environments.
Data Analyst
Telstra Corporation, Melbourne VIC
2020 – Present
• Analyse customer usage patterns from 18M+ mobile and broadband subscribers using SQL and Python to identify retention opportunities
• Develop predictive models for network capacity planning, reducing infrastructure costs by $3.2M through optimised resource allocation
• Create automated Tableau dashboards tracking KPIs across 5 business units, reducing reporting time from 40 hours to 2 hours weekly
• Conduct A/B testing for digital product features, improving user engagement by 28% and reducing customer service calls by 15%
• Collaborate with product and marketing teams to design customer segmentation strategies, increasing targeted campaign conversion rates by 35%
• Present monthly analytics insights to executive leadership, directly influencing strategic decisions on product development and market positioning
• Lead data quality initiatives resulting in 99.2% data accuracy improvement across core customer databases
Data Analyst
Tech Company
2020 – Present
• Worked with data and databases
• Created reports and charts
• Used Excel and other software
• Helped with business decisions
• Attended meetings with team members
• Analysed company information
Entry-Level Data Analyst Resume Samples [Experience]
For entry-level positions, emphasise internships, academic projects, personal analytics projects, and any relevant experience with data analysis tools. Focus on demonstrating your analytical thinking, technical aptitude, and eagerness to apply quantitative skills to business problems.
Data Analyst Intern
ANZ Bank, Sydney NSW
November 2023 – February 2024
• Supported credit risk analytics team by cleaning and analysing loan application data from 50,000+ customers using Python and SQL
• Built interactive Power BI dashboard tracking key risk metrics, enabling senior analysts to identify trends 60% faster
• Performed statistical analysis on default patterns, contributing insights that improved risk assessment model accuracy by 12%
• Automated data validation processes using Python scripts, reducing manual verification time from 8 hours to 30 minutes daily
• Collaborated with cross-functional teams to understand business requirements and translate them into analytical solutions
• Presented findings on customer behaviour patterns to senior management, receiving commendation for clear data storytelling
• Completed advanced Excel and Tableau training, achieving certification in both platforms during internship period
Data Intern
2023 – 2024
• Learned about data analysis
• Worked with spreadsheets
• Attended training sessions
• Helped senior analysts
• Did data entry tasks
• Gained experience with software
How to Write the Education Section for Your Data Analyst Resume
Education is crucial for data analyst positions as it demonstrates your quantitative foundation, analytical training, and technical competency. Include degrees in data science, statistics, mathematics, economics, or related fields that show your ability to work with complex data and apply statistical methodologies.
Highlight academic achievements, relevant coursework, capstone projects, and research experience that demonstrate your analytical capabilities and readiness to tackle complex business problems through data analysis.
Data Analyst Resume Example [Education]
EDUCATION
Master of Analytics
University of New South Wales, Sydney NSW
2022
High Distinction | GPA: 3.8/4.0
Capstone Project: “Real-time Fraud Detection System Using Machine Learning for E-commerce Platforms”
Relevant Coursework: Advanced Statistics, Data Mining, Machine Learning, Business Intelligence, Database Design
Bachelor of Science (Applied Mathematics & Statistics)
Australian National University, Canberra ACT
2020
First Class Honours | University Medal
Honours Thesis: “Time Series Analysis of Australian Stock Market Volatility”
Relevant Coursework: Regression Analysis, Probability Theory, Mathematical Modelling, Econometrics
ADDITIONAL QUALIFICATIONS
• Graduate Certificate in Data Science – Coursera/Johns Hopkins University, 2021
• Professional Certificate in Google Data Analytics – Google Career Certificates, 2021
• Advanced SQL for Data Scientists – edX/University of California San Diego, 2020
How to Write the Skills Section for Your Data Analyst Resume
The skills section is critical for data analyst resumes as it immediately demonstrates your technical proficiency and analytical capabilities to both human recruiters and ATS systems. Include 12-15 skills organised by category, showcasing your programming abilities, analytics tools expertise, and business acumen essential for modern data analysis roles.
Balance technical skills with analytical and communication abilities to present yourself as a well-rounded data professional capable of both executing complex analysis and communicating insights effectively to diverse stakeholders.
Data Analyst Resume Skills (Hard Skills)
• SQL Database Querying & Data Manipulation
• Python Programming (Pandas, NumPy, Matplotlib)
• Statistical Analysis & Hypothesis Testing
• Data Visualisation (Tableau, Power BI, D3.js)
• Machine Learning & Predictive Modelling
• Excel Advanced Functions & VBA
• R Programming & Statistical Computing
• ETL Processes & Data Pipeline Development
• A/B Testing & Experimental Design
• Cloud Platforms (AWS, Azure, Google Cloud)
Data Analyst Resume Skills (Soft Skills)
• Business Intelligence & Strategic Thinking
• Data Storytelling & Presentation Skills
• Cross-Functional Collaboration
• Problem-Solving & Critical Analysis
• Project Management & Stakeholder Communication
• Attention to Detail & Quality Assurance
• Time Management & Priority Setting
• Adaptability & Continuous Learning
• Client Relations & Consulting Skills
• Leadership & Mentoring Abilities
How to Pick the Best Data Analyst Skills:
1. **Analyse data analyst job descriptions** to identify the most commonly requested technical skills, programming languages, and analytics platforms
2. **Emphasise SQL proficiency** as it’s fundamental to most data analyst roles and essential for database querying and data manipulation
3. **Include visualisation tools** such as Tableau, Power BI, or Python libraries as data storytelling is crucial for business impact
4. **Balance programming languages** with business tools to show you can both perform technical analysis and communicate insights effectively
5. **Consider industry-specific skills** based on your target sectors such as financial modelling for banking or marketing analytics for retail
6. **Highlight cloud platform experience** as organisations increasingly move data operations to cloud environments
Data Analyst Resume Examples [Skills]
• **Python Analytics Mastery:** Developed automated data analysis pipelines using pandas and scikit-learn, reducing analysis time from weeks to hours
• **Data Visualisation Excellence:** Created 50+ interactive Tableau dashboards providing real-time business insights to C-level executives and department heads
• **Statistical Analysis Proficiency:** Conducted hypothesis testing and regression analysis identifying key drivers of customer behaviour, informing $5M marketing spend decisions
• **Machine Learning Implementation:** Built predictive models with 92% accuracy for demand forecasting, enabling inventory optimisation saving $1.2M annually
Should I Add Bonus Sections to My Data Analyst Resume?
Additional sections can significantly enhance your data analyst resume by demonstrating your analytical impact, technical expertise, and professional engagement beyond basic job requirements. These sections are particularly valuable for data analysts where project portfolios, certifications, and continuous learning are key differentiators.
Valuable bonus sections include:
Key Projects & Portfolio: Specific analytical projects you’ve delivered including scope, methodologies used, and business impact achieved that demonstrate your problem-solving capabilities and technical expertise.
Technical Certifications: Industry certifications in analytics tools, cloud platforms, or programming languages that validate your technical competency and commitment to professional development.
Publications & Presentations: Data science articles, conference presentations, or blog posts that demonstrate thought leadership and ability to communicate complex analytical concepts.
GitHub Portfolio: Link to your GitHub profile showcasing code samples, data analysis projects, and technical documentation that provides evidence of your programming abilities.
Data Analyst Resume Examples [Other Sections]
FEATURED PROJECTS
• E-commerce Recommendation Engine – Built collaborative filtering system increasing cross-sell revenue by 23% using Python and AWS
• Customer Lifetime Value Model – Developed predictive model identifying high-value customers, enabling targeted retention strategies
• Real-time Sales Dashboard – Created automated Tableau dashboard providing live sales insights to 200+ retail managersTECHNICAL CERTIFICATIONS
• Google Cloud Professional Data Engineer – 2023
• Microsoft Azure Data Scientist Associate – 2022
• Tableau Desktop Certified Professional – 2022
• AWS Certified Big Data – Specialty – 2021
THOUGHT LEADERSHIP
• “Democratising Data: Self-Service Analytics in Enterprise” – Medium, 5,000+ views
• Speaker: “Python for Business Analytics” – Melbourne Data Science Meetup, 2023
• Technical Reviewer: “Applied Data Science with Python” – O’Reilly Media, 2022
HOBBIES & INTERESTS
• Playing with data in spare time
• Following tech blogs
• Watching data science videos
• Building spreadsheets for funPERSONAL INFORMATION
• Love working with numbers
• Detail-oriented person
• Good with computers
• Quick learner
Tip: Focus on quantifiable achievements and professional recognition rather than generic personal interests. Include technical hobbies only if they demonstrate relevant skills or contribute to your professional development as a data analyst.
How to Write a Data Analyst Resume Objective or Resume Summary
A compelling professional summary positions you as a skilled data analyst capable of transforming raw data into strategic business insights. Focus on your analytical experience, technical expertise, quantifiable achievements, and ability to drive business value through data-driven solutions and recommendations.
For data analyst roles, emphasise your proficiency with analytics tools, statistical methodologies, data visualisation capabilities, and track record of solving complex business problems through analytical approaches and data-driven insights.
Key elements to include:
• Years of data analysis experience and industries worked in
• Technical proficiency with key analytics tools and programming languages
• Quantifiable business impact and analytical achievements
• Specialisation areas such as machine learning, business intelligence, or statistical analysis
• Communication abilities and stakeholder engagement experience
• Commitment to data-driven decision making and continuous learning
Data Analyst Resume Summary Examples
Analytical Data Analyst with 7+ years of experience delivering actionable insights across finance, retail, and healthcare sectors. Expert in Python, SQL, and advanced analytics with proven track record of building predictive models that improved business outcomes by 30+ million dollars. Proficient in Tableau and Power BI with demonstrated ability to translate complex statistical findings into compelling visualisations for C-level executives. Strong background in machine learning, A/B testing, and database optimisation with passion for solving complex business challenges through data-driven approaches.
Data analyst with experience working with numbers and spreadsheets. Good at using Excel and other computer programs to analyse information. Have worked for several companies doing data tasks and creating reports. Looking for opportunities to continue working with data and help companies make better decisions using analytical skills.
For entry-level candidates, a resume objective focusing on educational foundation, technical skills, and career aspirations may be more appropriate than an experience-focused summary.
Entry-Level Data Analyst Resume Summary Examples
Recent Data Science graduate with Master’s degree and comprehensive training in Python, SQL, and machine learning methodologies. Demonstrated analytical capabilities through capstone project predicting customer churn with 91% accuracy using advanced statistical techniques. Proficient in Tableau, R, and cloud analytics platforms with strong foundation in statistical analysis and data visualisation. Seeking to apply quantitative skills and passion for data-driven insights to solve complex business challenges in dynamic analytical environment.
New graduate with data science degree looking for first data analyst job. I learned about statistics and programming in school and want to start working with real business data. I am eager to apply my education and learn from experienced data professionals while contributing to company success through analytical work.
How to Update Your LinkedIn Profile When Updating Your Data Analyst Resume
Your LinkedIn profile is essential for data analyst career development in Australia, with tech companies, consulting firms, and data-driven organisations increasingly using the platform to identify qualified analytics professionals. When updating your data analyst resume, ensure your LinkedIn profile reflects consistent professional branding while leveraging LinkedIn’s features for analytics networking and thought leadership.
LinkedIn provides opportunities to demonstrate your analytical expertise through posts, share insights about data trends, and connect with data professionals, hiring managers, and industry leaders across Australia’s growing analytics and technology sectors.
LinkedIn Headline Optimisation for Data Analysts
Your LinkedIn headline should communicate your analytical expertise, technical specialisations, and professional value proposition beyond just your job title. Include relevant programming languages, analytics tools, and key achievements that distinguish you in the competitive data analyst market.
• “Data Analyst | Python & SQL Expert | Machine Learning | Turning Complex Data into Strategic Business Insights | 5+ Years Experience”
• “Senior Data Analyst | Tableau Specialist | Predictive Modelling | Driving $10M+ Revenue Through Analytics | AWS Certified”
• “Data Analyst & Visualization Expert | R & Python | Statistical Analysis | Transforming Data into Actionable Intelligence”
• “Data Analyst”
• “Analyst at Tech Company”
• “Looking for data analyst opportunities”
LinkedIn Summary vs Resume Summary: Key Differences
While your resume summary is concise and achievement-focused, your LinkedIn summary allows for more storytelling about your analytical journey and data philosophy. Write in first person, explain your passion for data analysis, discuss your approach to problem-solving, and share your vision for using analytics to drive business value. For the Australian context, mention your understanding of local market dynamics or experience with Australian business challenges.
Your LinkedIn summary can span 3-4 paragraphs, providing space to discuss your analytical evolution, key project achievements, technical interests, and career aspirations in Australia’s data-driven economy. Include relevant analytics keywords naturally to improve discoverability by tech recruiters and hiring managers.
Showcasing Data Analyst Experience on LinkedIn
LinkedIn’s experience section allows for more detailed descriptions of your analytical projects, methodologies used, and business impact achieved. Expand beyond resume bullet points to explain your problem-solving approach, technical innovations, and the strategic value of your analytical contributions.
Consider adding media to your experience entries such as data visualisations, project screenshots, or case study summaries (ensuring data confidentiality). Visual content helps demonstrate your analytical capabilities and storytelling skills to profile visitors.
LinkedIn Skills and Endorsements for Data Analysts
Select up to 50 skills relevant to data analysis, prioritising those most valued by employers and analytics professionals. Include technical skills (Python, SQL, Tableau), analytical capabilities (statistical analysis, machine learning), and business skills (data storytelling, stakeholder communication). Actively seek endorsements from colleagues, managers, and clients you’ve worked with.
Take LinkedIn skill assessments for relevant areas like Excel, Python, and Google Analytics. Displayed skill badges enhance your profile credibility and can improve visibility in searches by recruiters looking for specific technical competencies.
LinkedIn Profile Tips for Australian Data Analysts
Optimise your profile for the Australian analytics market by mentioning experience with local business contexts, understanding of Australian market dynamics, and familiarity with local data privacy regulations. Share insights about Australian data trends, comment on analytics industry posts, and engage with content from Australian data science leaders.
Join relevant LinkedIn groups such as “Data Science Australia,” “Australian Analytics Professionals,” or “Melbourne Data Science,” and participate in discussions about analytics trends, career development, or technical innovations. Connect strategically with Australian data professionals, hiring managers, and potential collaborators to build your professional network within Australia’s growing analytics community.
Creating an outstanding data analyst resume requires demonstrating both technical expertise and business acumen that resonates with data-driven organisations across Australia. Your resume should position you as a analytical professional capable of transforming complex data into strategic insights while communicating findings effectively to diverse stakeholders.
Complement your polished resume with a targeted data analyst cover letter that explains your analytical approach and demonstrates your understanding of the specific organisation’s data challenges and opportunities. Stay informed about Australian analytics developments through Institute of Analytics Professionals of Australia resources and continue your professional development through platforms like Kaggle to maintain excellence throughout your analytics career.
With a compelling resume that showcases your analytical expertise, technical proficiency, and commitment to data-driven excellence, you’ll be well-positioned to secure positions and build a successful career in Australia’s thriving data analytics industry.
