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How to Show Data science Project in Resume

In any project, the Data analyst role is to deal with data. The data for data science projects come from multiple sources. This post will explain how to put in data science project in Resume.
Data Science project for Resume The first step for an interview of any project is you need Resume. You need to tell clearly about your resume.

In interviews, you will be asked questions about your project. So the second step is you need to be in a position explain about project.

The third point is you need to explain the roles you performed in your data science project. If you mention the roles correctly, then, you will have 100% chance to shortlist your resume. Based on your experience your resume can be 1 page or 2 pages.
How to show Technologies used in Data science projects In interviews, again they will be asked how you used different tools to complete your data science project.

So, you need to be in a position to explain about how you used different options present in the tools. Sometime…

Hadoop real story to process unstructured data

Hadoop real process
Hadoop Real Process
Hadoop comes into picture to process large volume of Unstructured data. The structured data is already taken care by traditional databases.

Role of traditional databases

Traditional relational databases have been able to store massive data sets for a long time. An Oracle 10g database can store over 8 Petabytes while for many years DB2 databases have been capable of storing well over 500 Petabytes. Of course, this is all theoretical. 

  • No customer has an Oracle or DB2 database that approaches sizes even close to that. Why? Because the speed, or velocity, at which data can be loaded and queries can be executed approaches zero well before then.
  • Similarly, all traditional relational databases can store any variety of data as text or binary large objects. The problem is that large volumes of unstructured data cannot be moved fast enough to enable rapid search and retrieval.

ETL role in the age of Hadoop

Running constant and predictable workloads is what your existing data warehouse has been all about. And as a solution for meeting the demands of structured data—data that can be entered, stored, queried, and analyzed in a simple and straightforward manner—the data warehouse will continue to be a viable solution. Storing, managing and analyzing massive volumes of semi-structured and unstructured data is what Hadoop was purpose-built to do.

  • Unlike structured data, found within the tidy confines of records, spreadsheets and files, semi-structured and unstructured data is raw, complex, and pours in from multiple sources such as emails, text documents, videos, photos, social media posts, Twitter feeds, sensors and clickstreams.
  •  Hadoop and MapReduce enable organizations to distribute the search simultaneously across many machines, reducing the time to find relevant nuggets of information in large volumes of data in a scalable way. That’s why Hadoop is being adopted by bleeding edge enterprises moving into the multi-petabyte club. There are already some environments that break the 100 Petabyte level, and theoretically can continue to scale.
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