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The Importance of data formats for Data Scientist

Posted by Doctor Dan on Jul 29, 2016 6:37:15 AM

Data sorting and organizing is crucial for any data scientist. They can save the labor of spending most of their sorting and cleaning data prior to working on it. Before the data analysis begins, 80% of the time is spent in collecting and organizing data.

Since, the size and format of the data is in variable length and structure, working on motley of data sets becomes difficult. Patterns are important for data scientists. Data mining takes away yet another 10% of time before finally get to working on the sorted data. More than 60% of time is spent on data preparation and sampling.


Let us understand the process to know why data formats play a vital role.

Refining and Tweaking

Once the data is in place, data scientists spend time in refining and tweaking the algorithms and patterns. An organization that deals with data analysis and presentation spends more time and money in collecting data. Resources and money get wasted keeping the productivity low.

Choosing a Data Format

Checking is required for technologies and tools used for analyzing and processing the data. Choosing tools that are compatible with the type of data you analyze is vital. Choosing tools that have too many parsers or converters adds to the load and processing time.

Checking the list and the format of queries sent is another important task. The tools used must support the format of queries. One needs to choose the data formats that do not compress the storage capacity or occupy high amount of space in the internal memory.

Data formats should never halt the background processes. The raw data must be able to split easily. Readable format is difficult to split as the system has to integrate many parameters before splitting this data.


Data formats are crucial for any data scientist. They deal with massive amounts of raw data for analysis every day. Data stored, data modified, and the data that is to be analyzed are a few parameters to look at when selecting data formats and tools that analyze them.

Topics: Data Science