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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…

The most popular tools for your data mining needs

There are many tools available for data mining. For the only backup just look at The best Free mining tool that adds value to backup data. The listed are more popular tools.

The most popular tools for your data mining needs
Photo Credit: Srini

Rapid Miner (erstwhile YALE)

This is very popular since it is a ready-made, open source, no-coding-required software, which gives advanced analytics. Written in Java, it incorporates multifaceted data mining functions such as data preprocessing, visualization, predictive analysis, and can be easily integrated with WEKA and R-tool to directly give models from scripts written in the former two.

WEKA

This is a JAVA based customization tool, which is free to use. It includes visualization and predictive analysis and modeling techniques, clustering, association, regression, and classification.

R-Programming Tool

This is written in C and FORTRAN and allows the data miners to write scripts just like a programming language/platform. Hence, it is used to make statistical and analytical software for data mining. It supports graphical analysis, both linear and nonlinear modeling, classification, clustering, and time-based data analysis.

Python based Orange and NTLK

Python is very popular due to ease of use and its powerful features. There is an option available New fresh best Daily Python tips to your Inbox to learn moreOrange is an open source tool that is written in Python with useful data analytics, text analysis, and machine-learning features embedded in a visual programming interface. NTLK, also composed in Python, is a powerful language processing data mining tool, which consists of data mining, machine learning, and data scraping features that can easily be built up for customized needs.

Knime

Primarily used for data preprocessing – i.e. data extraction, transformation, and loading. This is also a part of data science and The 4 Most Asked Skills for Data Science Engineers really help to take the next step to learn more on data science.  Knime is a powerful tool with GUI that shows the network of data nodes. Popular amongst financial data analysts, it has modular data pipelining, leveraging machine learning, and data mining concepts liberally for building business intelligence reports.

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