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Data science is a science that is currently very popular and can be applied in various industries. Of course, the need for proficient people in this field is growing in demand by many businesses, including large corporations. Data science is a science that combines expertise in a particular field of science with programming, mathematics, and statistics skills. The goal is to extract knowledge or information from data. The problem that usually occurs in the data science process is at the data modeling stage, namely the accuracy of the mathematical model that will be used to obtain the estimated parameter values. To overcome this problem, this research was conducted to test the correctness of analytic derivatives in mathematical models and conduct data modeling using analytic derivatives that have been tested to obtain the estimated value of each parameter. The validation process is carried out using an analytic and numeric gradient comparison. If the comparison of the two values is close to zero, then the analytical derivatives used are correct. The parameter estimation method used is the Maximum Likelihood Estimation. In this research, it was concluded that the value of the analytic gradient is the same or close to the numeric gradient value for each parameter so that the analytic derivatives are correct and can be used for the next stage. With the results of this research, the expected implication is to get more accurate data modeling so that the conclusions obtained are more precise. [ABSTRACT FROM AUTHOR]

This article, as an example of the integration of disciplines, is focused on the issues of combining knowledge, skills and practical experience at all levels of training of software engineering specialists and synthesizing knowledge directed to specific field goals are discussed. At the same time, modern information and communication technologies are considered as a means of integration that connects mathematics and computer sciences with each other. [ABSTRACT FROM AUTHOR]

Embedding technology in teaching and learning (T&L) becomes necessary, especially during open distance learning (ODL). Mobius is a math-based online platform that provides interactive T&L applications to meet the ODL requirements. This platform is built on Maple software with intelligent grading and response, where students can attempt several random questions within the same subtopic and get an immediate response. However, students' response to this platform is the primary concern. Thus, this study analyzed the students' feedback on using Mobius for Calculus I. The content designs, implementation, and students' experience using this platform are discussed. The respondents in this study were 20 non-mathematical students in the second semester of Bachelor of Information Technology, Faculty of Computer and Mathematical Sciences, UiTM Shah Alam. Students were given access to Mobius and required to complete all the exercises on selected topics in Calculus I - techniques of differentiation, chain rule, techniques of integration and application of integration. Finally, their feedback is recorded and analyzed. The result shows that most students showed positive feedback on using Mobius to improve their Calculus I understanding and recommended using Mobius in T&L sessions. [ABSTRACT FROM AUTHOR]