These include SQL, Python, Git and data management tools such as R, SAS and SPSS. There are a few key tools that are used in data analysis, however. Data analysis tools can also be used to automate tedious tasks, providing data analysts with more time for actual analysis. Some data analysis tools are used for tasks such as executing algorithms, visualising data and results, while others may be designed for preparing data for analysis, assessing results or for developing hypotheses or predictions.
Different companies will have different requirements and may work with an internal or external set of tools. Industry standards based syntax - Oracle's features conform to ANSI SQL standard and are supported by a large number of independent software vendors.The best tools to learn as a data analyst depend partly on the role you are in and the needs of the business.Minimized Learning Effort - SQL analytic functions minimize the need to learn new keywords because the syntax leverages existing well-understood keywords.Improved Manageability - ability to access a consolidated view of all data types and sources is simplified when applications share a common relational environment rather than a mix of calculation engines with incompatible data structures.Actions which before required self-joins or complex procedural processing may now be performed in native SQL. Improved Query Speed - processing optimizations supported by in-database analytics enable significantly better query performance.Complex tasks can now be expressed using single SQL statement which is quicker to formulate and maintain, resulting in greater productivity. Enhanced Developer Productivity - perform complex analyses with much clearer and more concise SQL code.The key benefits provided by Oracle's in-database analytical functions and features are:
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