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Data Science plays a vital role in sciences and engineering disciplines to discover meaningful information and predict the outcome of real-world problems. Despite the significance of this field and high demand, knowledge of how to effectively provide data science research experience to STEM students is scarce. This paper focuses on the role of data science and analytics education to improve the students' computing and analytical skills across a range of domain-specific problems. The paper studies four examples of data-intensive STEM projects for supervised undergraduate research experiences (SURE) in Mechanical Engineering, Biomedical science, Quantum Physics, and Cybersecurity. The developed projects include the applications of data science for improving additive manufacturing, automating microscopy image analysis, identifying the quantum optical modes, and detecting network intrusion. The paper aims to provide some guidelines to effectively educate the next generation of STEM undergraduate and graduate students and prepare STEM professionals with interdisciplinary knowledge, skills, and competencies in data science. The paper includes a summary of activities and outcomes from our research and education in the field of data science and machine learning. We will evaluate the student learning outcomes in solving big data interdisciplinary projects to confront the new challenges in a computationally-driven world. 

Data Science plays a vital role in sciences and engineering disciplines to discover meaningful information and predict the outcome of real-world problems. Despite the significance of this field and high demand, knowledge of how to effectively provide data science research experience to STEM students is scarce. This paper focuses on the role of data science and analytics education to improve the students' computing and analytical skills across a range of domain-specific problems. The paper studies four examples of data-intensive STEM projects for supervised undergraduate research experiences (SURE) in Mechanical Engineering, Biomedical science, Quantum Physics, and Cybersecurity. The developed projects include the applications of data science for improving additive manufacturing, automating microscopy images analysis, identifying the quantum optical modes, and detecting network intrusion. The paper aims to provide some guidelines to effectively educate the next generation of STEM undergraduate and graduate students and prepare STEM professionals with interdisciplinary knowledge, skills, and competencies in data science. The paper includes a summary of activities and outcomes from our research and education in the field of data science and machine learning. We will evaluate the student learning outcomes in solving big data interdisciplinary projects to confront the new challenges in a computationally-driven world. 

Quantum computation is gaining popularity as a practical application of quantum physics based on quantum superposition, entanglements, and the no-cloning theorem. Because the security of electronic transactions is vital, various cryptographic protocols based on distributed keys between the intended participants have been created. Complex mathematical models and lengthy keys determine the security of these protocols. These keys, on the other hand, are readily broken. The security of information has undergone a paradigm shift as a result of quantum technologies. The quantum circuits in this thesis were constructed utilizing the IBM quantum experience platform with the goal of realizing safe quantum key distribution (BB84 algorithm). With increasing the number of runs, the possibility of these circuits being realized on a practical quantum computer accessed through the IBM QX online platform increased. Furthermore, there is a significant likelihood of identifying the presence of a third party. The probability of identifying a third-party stealing information increases as the number of qubits increases. 

Additive Manufacturing (AM) is a crucial component of the smart manufacturing industry. In this paper, we propose an automated quality grading system for the fused deposition modeling (FDM) process as one of the major AM processes using a developed real-time deep convolutional neural network (CNN) model. The CNN model is trained offline using the images of the internal and surface defects in the layer-by-layer deposition of materials and tested online by studying the performance of detecting and grading the failure in AM process at different extruder speeds and temperatures. The model demonstrates an accuracy of 94% and specificity of 96%, as well as above 75% in measures of the F-score, the sensitivity, and the precision for classifying the quality of the AM process in five grades in real-time. The high-performance of the model could not be achieved with the values usually used for printing temperature and printing speed, only in addition with much higher values. The proposed online model adds an automated, consistent, and non-contact quality control signal to the AM process. The quality monitoring signal can also be used by the AM machine to stop the AM process and eliminate the sophisticated inspection of the printed parts for internal defects. The proposed quality control model ensures reliable parts with fewer quality hiccups while improving performance in time and material consumption. 

Network intrusion detection systems (NIDS) for Internet-of-Things (IoT) infrastructure are among the most critical tools to ensure the protection and security of networks against malicious cyberattacks. This paper employs four machine learning algorithms and evaluates their performance in NIDS considering the accuracy, precision, recall, and F-score. The comparative analysis conducted using the CICIDS2017 dataset reveals that the Boosted machine learning techniques perform better than the other algorithms reaching the predicted accuracy of above 99% in detecting cyberattacks. Such ML-based attack detectors also have the largest weighted metrics of F1-score, precision, and recall. The results assist the network engineers in choosing the most effective machine learning-based NIDS to ensure network security for today’s growing IoT network traffic.