Data Analyst

Skill: Data Analyst

Must Have Technical/Functional Skills:
Data Conditioning - identification of appropriate test data, conditioning of test data as necessary to meet non-functional testing requirements.
Statistical Analysis: Proficiency in statistical methods and techniques.
SQL: Knowledge of Structured Query Language for database management.
Data Visualization Tools: Experience with tools like Tableau, Power BI.
Programming Languages: Familiarity with languages like Python or R.
Data Cleaning and Preparation: Skills in data cleaning, transformation, and quality assurance.
Data Modeling: Ability to design and implement data models.
Communication and Presentation: Strong communication and presentation skills to effectively convey findings to stakeholders.
Critical Thinking: Ability to analyze data, identify trends, and draw conclusions.
Attention to Detail: Accuracy and precision in data analysis and reporting.
Roles & Responsibilities:
Data Conditioning - identification of appropriate test data, conditioning of test data as necessary to meet non-functional testing requirements.
Data Collection and Preparation: Gathering data from various sources, cleaning and preparing it for analysis, and ensuring data quality.
Statistical Analysis: Performing statistical analyses to identify trends, patterns, and anomalies in the data.
Exploratory Data Analysis (EDA): Engaging in exploratory data analysis to gain a deeper understanding of the data and identify potential areas for further investigation.
Data Visualization: Creating clear and concise visualizations (charts, graphs, dashboards) to communicate data insights effectively.
Reporting and Presentation: Preparing reports and presentations to communicate findings to stakeholders, influencing decision-making.
Data Governance and Quality: Ensuring data quality, process documentation, and defining Key Performance Indicators (KPIs).
Database Management: Managing databases, troubleshooting data-related issues, and optimizing database performance.
Data Modeling: Building data models to represent and analyze data effectively.
Data Mining: Mining data from various sources and transforming it into usable formats.