Advanced Microsoft Excel: Data analysts should have a good handle on excel and understand advanced modeling and analytics techniques.Once they have enough data, they clean and process through programming. Data Mining, Cleaning and Munging: When data isn’t neatly stored in a database, data analysts must use other tools to gather unstructured data.Database Querying Languages: The most common querying language data analysts use is SQL and many variations of this language exist, including PostreSQL, T-SQL, PL/SQL (Procedural Language/SQL).Data is stored in tables and a data analyst pulls information from different tables to perform analysis. SQL Databases: SQL databases are relational databases with structured data.They connect databases from multiple sources to create a data warehouse and use querying languages to find and manage data. Data Warehousing: Some data analysts work on the back-end.A successful data analyst understands what types of graphs to use, how to scale visualizations, and know which charts to use depending on their audience. Data Visualization: Effective data visualization takes trial and error.Strong communication is the key to success. Strong and Effective Communication: Data analysts must clearly convey their findings - whether it’s to an audience of readers or a small team of executives making business decisions.This will help the analyst to generate interesting research questions that will enhance a company’s understanding of the matter at hand. It’s important to have a strong grounding in statistical methods, but even more critical to think through problems with a creative and analytical lens. Creative and Analytical Thinking: Curiosity and creativity are key attributes of a good data analyst.Data analysts use programming languages such as R and SAS for data gathering, data cleaning, statistical analysis, and data visualization.
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