Data Science Talents Mining from Online Recruitment Market in China Based on Data Mining Technique

As the country implements the big data strategy and accelerates the construction of a digital China, data science has entered a new and dynamic era, and the demand for data science talents in all walks of life is increasing. Many talent training departments have added undergraduates or degrees to data science talents, but it is still unclear whether they can meet social and economic development needs. This article aims to improve the quality and adaptability of data science talent training and conduct an in-depth analysis of the demand for data science talents. The technology used in this article is data mining technology. The data information of data science talents is crawled out of the demand information of data science talents on the recruitment website. The core content of network relationship visualization is proposed and analyzed through machine learning methods and text subject word extraction models. Achieve a comprehensive exploration of the demand for data science talents and provide a reference for talent training units to formulate data science talent training models.

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Analysis of Mobile Service Providers Performance Using Naive Bayes Data Mining Technique

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v8i6.pp5153-5161 ◽

2018 ◽

Vol 8 (6) ◽

pp. 5153

Author(s):

M. A. Burhanuddin ◽

Ronizam Ismail ◽

Nurul Izzaimah ◽

Ali Abdul-Jabbar Mohammed ◽

Norzaimah Zainol

Keyword(s):

Data Mining ◽

Data Science ◽

Service Providers ◽

Telecommunication Service ◽

Mobile Service ◽

Business Growth ◽

Data Mining Technique ◽

R Language ◽

Mining Technique ◽

Industry Standard

Recently, the mobile service providers have been growing rapidly in Malaysia. In this paper, we propose analytical method to find best telecommunication provider by visualizing their performance among telecommunication service providers in Malaysia, i.e. TM Berhad, Celcom, Maxis, U-Mobile, etc. This paperuses data mining technique to evaluate the performanceof telecommunication service providers using their customers feedback from Twitter Inc. It demonstrates on how the system could process and then interpret the big data into a simple graph or visualization format. In addition, build a computerized tool and recommend data analytic model based on the collected result. From prepping the data for pre-processing until conducting analysis, this project is focusing on the process of data science itself where Cross Industry Standard Process for Data Mining (CRISP-DM) methodology will be used as a reference. The analysis was developed by using R language and R Studio packages. From the result, it shows that Telco 4 is the best as it received highest positive scores from the tweet data. In contrast, Telco 3 should improve their performance as having less positive feedback from their customers via tweet data. This project bring insights of how the telecommunication industries can analyze tweet data from their customers. Malaysia telecommunication industry will get the benefit by improving their customer satisfaction and business growth. Besides, it will give the awareness to the telecommunication user of updated review from other users.

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Salary Estimator using Data Science

International Journal for Modern Trends in Science and Technology - RTT2020 ◽

10.46501/ijmtst061259 ◽

2020 ◽

Vol 6 (12) ◽

pp. 319-322

Author(s):

Winner Walecha and Dr. Bhoomi Gupta

Keyword(s):

Data Mining ◽

Random Forest ◽

Data Science ◽

The Internet ◽

Prediction System ◽

Lasso Regression ◽

Data Mining Technique ◽

Mining Technique ◽

Number Of Factors ◽

Using Data

This paper presents a salary prediction system using the job listings from an employment website, in this case Glassdoor.com. A data mining technique is used to generate a model which will scrape number of jobs from the employment website, clean it on the basis of number of factors including the rival companies, revenue and skill required thereby predicting the salary to be expected when applying for a data science job. Techniques like linear regression, lasso regression, random forest regressors are optimised using GridsearchCV to reach the best model. The model can be further extended to build a flask API thus can be deployed on the internet for public usage.

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