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Data Science

While every project consists of some kind of research and development, the most important part is the collection of data. Data scientist cares up for data acquisition to gather and scrape datafrom multiple sources like SAP servers, logs, databases, APIs and online repositories. It would seem like finding the right data takes both time and effort.

Data Science is a process that is delicate and needs to be done with the utmost care. After the data is gathered, comes data preparation. This step involves data cleaning and data transformation.

In Data Science, data cleaning is the most time-consuming process as it involves handling many complex scenarios. Our Data scientist & Data Analysts sometimes deal with inconsistent data types misspelled attributes, missing values, duplicate values and whatnot.

Usually, the data based on defined mapping rules in a project ETL tools like talent and informatica are used to perform complex transformations that helps the team to understand the data structure better than understanding what you actually can do with your data is very crucial.

Here in Anviam, with the help of exploratory data analysis, our Data Scientists define and refine the selection of feature variables that will be used in moral development. This is quite the important step, as it defines the accuracy of the data, and missing this might end up choosing the wrong variables which will produce an inaccurate model. We proceed to the core activity, i.e. data modeling where we apply type force machine learning techniques like KNL decision tree knife phase to the data to identify the moral that best fits the business requirement.

This was a very brief of how we work for data collection, and converting it to information to then be used by our clients.

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Data Science

 

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