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Analyzing data can be done by anyone so long as you know how to type this shouldn't be very hard for you to do. The work of  a data entry analyst is to check out documents and see where there is a problem and rectify it. say when you read a document, when you are analyzing it, you will have to check the spellings, the punctuation marks, let's just say the language. then you will also need to read through and make sure whatever is being written is in line with what is needed, if you cannot rectify it yourself, you can have another person rectify it to what is needed. I don't think you need to actually go for a training in this if it is simple data entries. but for complex documents like something to do with numbers then that will need some training like in quick books. because if it has something to do with balancing of sheets, that is complex you cannot take it lightly. I have seen people taking up online jobs that require you to do this kinds of jobs, and some people have never gone for technical training but they do them, because they have the basic knowledge in computers, the rest will come along as you try out new programs.
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There is no one-size-fits-all answer to this question, as the best way to become a data analyst without a technical background may vary depending on what you are interested in doing as a career. However, some tips on how to become a data analyst without a technical background include Studies (free or fee-based), study different software programming languages, and interning in a local software company.
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The best way to become a data analyst without a technical background may vary depending on what you are seeking in a career. However, some tips on how to become a data analyst without a technical background include studying and learning as much as possible about data analysis and management. Additionally, it is important to be able to work with a data team and be able to manage data projects.
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Step 1: Identify your dream job.

Step 2: Learn new skills through a data science course.

Step 3: Business Problems and how badly you need to automate decision making.

Step 4: Explore what is Data Science vs AI vs ML vs DL vs Statistics vs Analytics vs BI vs BigData.
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