3. Benefits of Having a Data Scientist

Data Scientist Daily Tasks

These are the daily tasks that data scientist perform:

1. Data Cleaning

In the business world, incorrect data can be costly. Many companies use customer information databases that record data related to contact information, addresses, and preferences.

For instance, if the addresses are inconsistent, the company will suffer the cost of resending emails or even end up losing customers.

For these reasons, it is very important to have step-by-step guidelines that walk through the quality checks to be applied. There’s a lot of data out there, but much of it is not in an easy to use format.

This part of a data scientist’s job involves making sure that data is nicely formatted, all the errors and duplicates are eliminated, and the irrelevant information is discarded.

2. Data Analysis

This is the sort of work most people think of using Excel for and frankly speaking, it is true but for small businesses only! A data scientist will typically work with large data sets that are unable to open in a typical spreadsheet program even on a single computer.

Data analysis is the stage where you make plots of the data in order to understand it. This enables the data scientist to explain the data in a way that will be easy to communicate and easy to make decisions on.

For example – Data scientists at Facebook figured out that having a minimum of ten friends in a profile helps guarantee that a user will stay active on the site. This is the reason why there are suggestions to find/add more friends to the site.

3. Predictive Modeling/Statistics

Depending on the background of a data scientist, they call themselves modeler or statistician. Those who studied statistics, consider themselves statistician and everyone else claims to be a modeler.

After you are done with cleaning and analyzing the data, you would want to make predictions from the data. A data scientist actually spends a lot of time evaluating and twisting the models. If that doesn’t work, he turns back to the data to bring new details that can help make better models.

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