Exploring the Effectiveness of Coding Education Methods and Their Impact on the Data Science Job Market Requirements – a Systematic Review

EasyChair Preprint no. 10922

Abstract

The growing demand for data science and coding skills has led to an increase in the number of educational programs in these areas. However, the effectiveness of these programs in terms of career development outcomes remains unclear. This systematic review aims to evaluate the available literature on the impact of coding education on data science career development. The study will follow a rigorous and systematic approach to identify and analyze relevant research on data science and coding education. A comprehensive search of relevant databases will be conducted using keywords and MeSH terms related to data science, coding education, career development, and program evaluation. The purpose of this systematic review is to evaluate the available literature on the effectiveness of coding education for Data science career development. The increasing demand for professionals with data science and coding skills has led to a growth in the number of educational programs in these areas. However, the quality and effectiveness of these programs are not well understood. This systematic review aims to address this gap by synthesizing the existing research on the impact of coding education on data science career development.

Keyphrases: career development, Coding Education, Data Science, systematic review

@Booklet{EasyChair:10922,
  author = {Nipuna Thalpage and Rohit Bansal},
  title = {Exploring the Effectiveness of Coding Education Methods and Their Impact on the Data Science Job Market Requirements – a Systematic Review},
  howpublished = {EasyChair Preprint no. 10922},

  year = {EasyChair, 2023}}