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A data analyst is a professional who retrieves, organizes and analyzes information from various sources to help an organization achieve business goals. Data analysts use logic, statistical techniques and computer programming to turn numbers into information that an organization can use to improve workflow and business processes.
An important goal of the analysis is to distinguish between what data is important and what data should be given less weight. In many organizations, data analysts are also responsible for data quality and preparing reports for internal and external stakeholders.
Developing and implementing databases and data collection systems: Data analysts create and manage databases and data collection systems that efficiently gather relevant information.
Identifying critical KPIs and setting priorities: Collaborating with management, data analysts help determine the most crucial Key Performance Indicators (KPIs) and establish priorities for the company.
Gathering information from original and secondary sources: Data analysts acquire comprehensive information for analysis by gathering data from various sources, including primary and secondary sources.
Sorting and sanitizing data: Prior to analysis, data needs to be organized and cleansed to ensure accuracy and quality.
Identifying trends and patterns in large datasets: Data analysts utilize advanced analytical techniques to uncover meaningful trends and patterns hidden within extensive datasets.
Creating visual representations of data: Data analysts employ data visualization tools to present insights in a visually appealing and understandable format, enabling stakeholders to easily comprehend complex information.
Generating and modifying reports: Data analysts develop reports that summarize their findings, providing actionable insights and recommendations for decision-making purposes. These reports are continually refined and adjusted based on evolving data analysis requirements.
Building and maintaining dashboards: Data analysts design and maintain interactive dashboards that offer real-time visualizations of key metrics, allowing stakeholders to monitor performance and make informed decisions.
Documenting data models, metrics, and infrastructure: Data analysts document the data models, metrics, and supporting infrastructure they create and maintain to ensure data integrity and facilitate collaboration.
Scripting: SQL, Python, and R
Data blending: Informatica, Alteryx, and SAS
Data visualization: Excel, Tableau, Power BI, D3.js, and ElasticSearch
Machine Learning: TensorFlow, Keras, PyTorch, and Pandas
Practical database querying
Data management
Data cleaning
Google Sheets
SQL
Microsoft Excel
SAS
Tableau
R or Python
Jupyter Notebooks
Microsoft Power BI
