4 steps to follow for Data Science threats

Data scientists remain in the quest to find out sources of the data. After getting multiple sources, they accumulate data into data lakes and understand it later on. Through the use of this data, risk analysis, and other predictions are possible. There might be various data science threats to the security of the data. Time might be consumed while scraping and saving the data. The exact steps required to follow can help us depend on our outputs and reach effective solutions.

Data science projects require to explore data from the scratch with authentic sources. It means we can apply safety measures to ensure the authenticity of the data. If our data is accurate, it helps us define values more confidently. So, in this article, we are going to highlight some points to follow for data science projects.

What are the steps to follow?

There are four steps to follow while procuring data from scratch and maintaining it. If you follow these stages, there might not be some sort of difficulties. However, here are the steps.

  • Acquire
  • Clean 
  • Explore
  • Summarize

  1. Acquire :

The first step is to obtain data. You need to determine sources and dig up messy data. Once you have got data, you need to export it into a spreadsheet or some other file format. The format of the file must be a tool friendly in which it can be easily analyzed.Millions of files have to be searched and procured. Accumulating sich a massive data might take more time to transfer into another format. However, according to an estimation, it takes up to 25% time of all four steps.

  1. Clean :

After gathering huge volumes of data, the next step is to go through the data. All the collected data is in complex form and needs to be defined according to your preferences. For this purpose, cleaning is essential converting multiple data formats into readable mediums.So, cleaning all the data especially complex files consumes more time even than the first step. So, about 35% of the total time is added to this step.

  1. Explore :

Exploration is a critical format to convert your data into useful insights for your project. If you are an employee in a business company, to generate effective sales leads, exploration is essential. In this step, you need to explore data, transform it into a visual format, and draw charts to explain it. To increase the ease of your job, you can apply multiple productive techniques for this aim too. About 20% of the time is consumed while doing this job.

  1. Summarize :

Commonly this step connects the explored data to the stakeholders approaching final results. Valuable reports are summarized into the stage and output is produced. This helps the data scientists to finish off the job. Explaining what you achieved and how is it important for the job. However, this step doesn’t include any type of algorithms and other coding skills. You just have to explain what you procured. It looks like an easy job and consumes up to 20% of the total time.

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