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Häufig gestellte Fragen zu Datenanalyse
What is Data Analysis?
Data Analysis consists of transforming a great amount of data into information and knowledge through the application of statistical concepts and scientific methodologies to help companies decision making.\r \r Using technologies such as R, Python, Excel, SQL, Big Data, Data Mining and Dataviz, it is possible to work with data and generate relevant information to answer questions within companies.
What is Data Analysis for?
Great part of the well structured companies make decisions based on Data Analysis. In the current scenario, it has become a competitive need to operate a business based on data.\r \r The analysis, therefore, serves to generate intelligence from the information gathered inside the companies that can serve to support the decision making and validate hypotheses with security. \r \r In other words, Data Analysis serves to benchmark information, define goals, objectives, monitor and measure results.
How long does it take to learn Data Analysis?
The study process of Data Analysis has a series of variables that can influence the learning time. The area that the professional intends to exercise - such as analyst or data scientist - and the technologies involved in this analysis will also influence the time it takes. \r \r Additionaly, prior knowledge such as programming or Excel must be taken into consideration. However, starting from scratch, it is possible that a professional can enjoy the first analyses in Excel and SQL in 8 weeks of learning the most basic concepts of statistics, database and graph visualization.
Where should I start learning Data Analysis?\r
To start as a data analyst it is important to learn some basic programming languages such as SQL, which is widely used in creating and consulting companies’ databases.\r \r The second step would be to learn some languages focused on Data Analysis and statistics such as R, Python and VBA Excel.\r \r To start viewing and organizing data in reports, it is necessary to go deeper into Excel and Power Point.\r \r For those who still want to specialize and become a future data scientist, it is also necessary to develop knowledge in machine learning technologies and artificial intelligence.