Data Analyst

7 days ago

Melbourne, Victoria, Australia AI - Innovation hub Full time

Role Description: Data Analyst

A
Data Analyst
is responsible for collecting, processing, and performing statistical analyses on large datasets to help organizations make data-driven decisions. The role involves transforming raw data into meaningful insights that can inform business strategy, improve operational efficiency, and drive growth. Data Analysts work across various industries such as finance, marketing, healthcare, and technology, collaborating with different departments to understand data needs and deliver actionable insights.

The Data Analyst plays a critical role in identifying trends, patterns, and relationships within data, often using tools like
Excel
,
SQL
, and specialized software to perform their work. Strong communication skills are necessary to present findings to non-technical stakeholders and translate complex data into simple, actionable recommendations.

Qualifications

  • Education:
  • Bachelor's degree
    in
    Data Science
    ,
    Statistics
    ,
    Mathematics
    ,
    Computer Science
    ,
    Economics
    , or a related field.
  • Master's degree
    in a data-related field may be preferred, but it's not always required, especially for entry-level roles.
  • Certifications such as
    Google Data Analytics Certificate
    ,
    Microsoft Certified: Data Analyst Associate
    , or
    Tableau Desktop Specialist
    can be beneficial.
  • Skills:
  • Data Analysis & Interpretation:
    Strong ability to analyze large datasets and identify meaningful trends, correlations, and patterns.
  • Problem-Solving:
    Ability to approach complex business challenges analytically and use data to propose solutions.
  • Statistical Knowledge:
    Understanding of statistical techniques (e.g., hypothesis testing, regression analysis, sampling methods) to interpret data accurately.
  • Attention to Detail:
    Accuracy is critical, as small errors in data analysis can lead to incorrect insights or conclusions.
  • Business Acumen:
    Understanding of how data can be applied to solve real-world business problems and add value to organizational processes.
  • Communication Skills:
    Ability to present data findings clearly and concisely to non-technical stakeholders, often through reports, visualizations, and presentations.
  • Collaboration:
    Work closely with teams across the organization to understand their data needs and deliver insights that support their goals.
  • Technical Skills:
  • Data Analysis Tools:
    Proficiency in tools like
    Microsoft Excel
    ,
    Google Sheets
    , or other spreadsheet applications for data manipulation, analysis, and reporting.
  • SQL (Structured Query Language):
    Knowledge of
    SQL
    for querying relational databases and extracting relevant data.
  • Data Visualization Tools:
    Familiarity with data visualization platforms like
    Tableau
    ,
    Power BI
    , or
    Google Data Studio
    for creating charts, graphs, and dashboards that communicate data insights effectively.
  • Programming Languages:
    Basic to intermediate knowledge of programming languages like
    Python
    or
    R
    for data manipulation, statistical analysis, and automating tasks.
  • Data Cleaning & Preprocessing:
    Experience with data cleaning and preprocessing to ensure datasets are accurate and usable, using tools or libraries (e.g.,
    Pandas
    in Python).
  • Statistical Software:
    Familiarity with software like
    SPSS
    ,
    SAS
    , or
    Stata
    for more advanced statistical analysis (depending on the industry).

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