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MICROCREDS: COMPUTER SCIENCE & ENGINEERING

SCHOOL OF COMPUTER SCIENCE & ENGINEERING

With the global shift towards flexible workplaces and a digital economy, professionals who are skilled in fields of study such as artificial intelligence, data science, and cybersecurity are well-positioned to benefit from fast-track career growth and exciting job opportunities.

Facilitated by the online tutorials, activities, and interactions of Taylor’s MicroCreds, develop in-demand tech skills to become a problem-solver for your organisation using data processing tools, database management systems, algorithms, and machine learning.

COURSES

FUNDAMENTALS OF DATA ANALYTICS

SYNOPSIS

Data analytics is about analysing data using appropriate techniques to gain insights into a problem to make informed decisions. In this MicroCreds course the descriptive statistics namely population versus sample, types of variables such as quantitative and qualitative and the level of the measurements namely nominal, ordinal, interval and ratio are discussed. Measures of central tendency, measures of dispersion, and measures of position besides the counting principles such as multiplication rule, combination and permutation are also covered. At the end of this course, learners will be able to apply the appropriate data analytics techniques to seek insights into a given problem.

LEARNING OUTCOMES

  • Apply the appropriate techniques in performing data analytics to seek insights for a given problem

COURSE LECTURER

Dr Thulasyammal Ramiah Pillai is a senior lecturer in Taylor’s University. She has over 25 years as an educator and researcher. She has built her professional network by collaborating with various research groups namely Environmental and Natural Resource Science Group (UKM), Institute Medical Research(IMR), National Heart Institute Malaysia (IJN), Universiti Teknologi Mara Shah Alam , University West England and Swiss Tropical and Public Health Institute.

DATA PRE-PROCESSING TECHNIQUES

SYNOPSIS

Data pre-processing helps to fix errors quickly and produce quality data to make better decisions. Data pre-processing steps such as data cleaning, data transformation, feature reduction, feature extraction and Exploratory Data Analysis (EDA) will be covered in this MicroCreds course. Topics covered include fundamentals of data pre-processing, organisational data preparation, exploratory data analysis, data warehouse, simple linear regression, multiple linear regression, support vector regression, decision tree regression and evaluation of regression models performance using datasets. At the end this course learners should be able to evaluate and propose appropriate data pre-processing techniques solution for a given case.

LEARNING OUTCOMES

  • Apply appropriate methods, tools, and techniques to perform data pre-processing for a given data set. 

COURSE LECTURER

Dr Thulasyammal Ramiah Pillai is a senior lecturer in Taylor’s University. She has over 25 years as an educator and researcher. She has built her professional network by collaborating with various research groups namely Environmental and Natural Resource Science Group (UKM), Institute Medical Research(IMR), National Heart Institute Malaysia (IJN), Universiti Teknologi Mara Shah Alam , University West England and Swiss Tropical and Public Health Institute.

SUPERVISED MACHINE LEARNING

SYNOPSIS

Supervised machine learning involves observing several examples of a random value x and an associated value y and learning to predict y from x, usually by estimating p(y/x). Supervised learning means, from the view of the target y being provided by an instructor or teacher who shows the machine learning system what to do. Topics covered include the Machine, Decision tree and Random Forest classification. At the end of this MicroCreds course, learners should be able to propose the most suitable supervised machine learning algorithm to solve a business problem.

LEARNING OUTCOMES

  • Propose the most appropriate supervised machine learning algorithm to solve a business problem.

COURSE LECTURER

Dr Thulasyammal Ramiah Pillai is a senior lecturer in Taylor’s University. She has over 25 years as an educator and researcher. She has built her professional network by collaborating with various research groups namely Environmental and Natural Resource Science Group (UKM), Institute Medical Research(IMR), National Heart Institute Malaysia (IJN), Universiti Teknologi Mara Shah Alam , University West England and Swiss Tropical and Public Health Institute.

UNSUPERVISED MACHINE LEARNING

SYNOPSIS

Unsupervised machine learning algorithms experience a dataset containing many features that are neither classified nor labelled, then learn useful properties of the structure of this dataset and cluster them. These algorithms can be used to uncover hidden patterns in data without human intervention. Topics covered include the fundamentals of unsupervised machine learning, K-means clustering, Hierarchical clustering, Association Rule Mining, Reinforcement Learning and Deep Learning. At the end of this MicroCreds course, learners should be able to identify the most suitable unsupervised machine learning algorithm to solve a specific problem.

LEARNING OUTCOMES

  • Identify the most appropriate unsupervised machine learning algorithm to solve a specific problem

COURSE LECTURER

Dr Thulasyammal Ramiah Pillai is a senior lecturer in Taylor’s University. She has over 25 years as an educator and researcher. She has built her professional network by collaborating with various research groups namely Environmental and Natural Resource Science Group (UKM), Institute Medical Research(IMR), National Heart Institute Malaysia (IJN), Universiti Teknologi Mara Shah Alam , University West England and Swiss Tropical and Public Health Institute.

DATA SCIENCE TOOLKITS

SYNOPSIS

  • Data science has become a rewarding career choice as many companies today need data scientists to help them empower their analytics processes, build a numbers-based strategy that will boost their bottom line, and ensure that enormous amounts of data are translated into actionable insights.
  • This MicroCreds course introduces a range of data science toolkits like python, r programming, IDE, Colab, version control, and other relevant tools as well as fundamental of data science skillsets.
  • There are hands-on practicals and project-based learning. Learners will be assessed on practical skills, originality,and creativity in providing analysis on tool selection alongside critical thinking skills.

LEARNING OUTCOMES

  • Identify the relevant tools and approaches to gain insight out of the data for a given application 

COURSE LECTURER

Dr. Riyaz Ahamed is a well-established senior lecturer and trainer in the field of Data Science and Big Data Technologies, with 13 years of experience in training, research, software development, and academic teaching. He has conducted hands-on workshops in data science, machine learning, data analytics, Python, Hadoop, Spark, and R programming to researchers, industrialists, academicians, as well as Ph.D. and Masters’ students from various departments.

DATA COLLECTION AND CLEANING TECHNIQUES

SYNOPSIS

  • Data cleaning is one of the most important tasks to do if you are a data science professional. Working with inaccurate or corrupted data can lead to many difficulties and can be detrimental to data processing and analysis.
  • This MicroCreds course covers a variety of data that is used in a data science application and its various stages in the data collection process alongside the importance of datasets and how to identify and reduce the problem associated with data cleaning using R programming.
  • Learners will be introduced to various data cleaning techniques to handle missing data through its case study-based project.

 

LEARNING OUTCOMES

  • Identify the relevant data collection tools, and cleaning techniques to gain insight out of the data for a given application

COURSE LECTURER

Dr. Riyaz Ahamed is a well-established senior lecturer and trainer in the field of Data Science and Big Data Technologies, with 13 years of experience in training, research, software development, and academic teaching. He has conducted hands-on workshops in data science, machine learning, data analytics, Python, Hadoop, Spark, and R programming to researchers, industrialists, academicians, as well as Ph.D. and Masters’ students from various departments.

DATA VISUALISATION IN DATA SCIENCE

SYNOPSIS

  • Data visualisation helps us to understand data through visuals, such as maps or graphs to easily see trends, patterns, and outliers for vast data sets. This MicroCreds course covers the basics of data visualisation and exploratory data analysis.
  • Learners are introduced to various data visualisation packages used for statistical analysis, the relationship between data analytics, and data visualisation and the numerous visualisation techniques using R programming. There will be lecture notes, quizzes, and hands-on practical’s and guided case study-based project.
  • These assessments are designed to assess learners’ practical skills, originality, creative solutions, and effective communication skills with various stakeholders to solve real-world problems.

LEARNING OUTCOMES

  • Design various types of data visualisations to communicate to the audience

COURSE LECTURER

Dr. Riyaz Ahamed is a well-established senior lecturer and trainer in the field of Data Science and Big Data Technologies, with 13 years of experience in training, research, software development, and academic teaching. He has conducted hands-on workshops in data science, machine learning, data analytics, Python, Hadoop, Spark, and R programming to researchers, industrialists, academicians, as well as Ph.D. and Masters’ students from various departments.

APPLIED DATA SCIENCE

SYNOPSIS

LEARNING OUTCOMES

  • Apply the fundamental concepts and techniques in data science in scientific problem solving.

COURSE LECTURER

Dr. Riyaz Ahamed is a well-established senior lecturer and trainer in the field of Data Science and Big Data Technologies, with 13 years of experience in training, research, software development, and academic teaching. He has conducted hands-on workshops in data science, machine learning, data analytics, Python, Hadoop, Spark, and R programming to researchers, industrialists, academicians, as well as Ph.D. and Masters’ students from various departments.

  • This MicroCreds course introduces data science processes which cover the fundamentals of data collection, data processing, analysis, visualisation and the knowledge of various topics such as big data technologies and cloud computing.
  • It also covers the importance of supervised learning and unsupervised learning algorithms besides numerous real-world applications related to data science.
  • Learners will be assessed on practical skills, originality, creativity, and critical thinking in providing in-depth analysis for the given case study by applying the machine learning algorithm to understand various stages of data science.
FUNDAMENTALS OF BIG DATA

SYNOPSIS

This MicroCreds course includes the basics of Big Data management, associated architectures, and available toolsand technologies. It is taught using guided online learning and problem-based learning through a combination of online lectures, online resources, and assessment activities. Online tutorials and pre-recorded teaching materials are available for students to watch and do the required online engagement activities. The in-course assessments involve a quiz, an individual assignment, and a final assessment in which students are required to identify big data characteristics, existing relevant database management systems, and available big data solutions.

LEARNING OUTCOMES

  • Identify big data characteristics, existing relevant database management systems, and available big data solutions that can be used in managing big data.

COURSE LECTURER

Dr Mohsen Marjani is a lecturer at the Department of Computing and IT, Taylor’s University. He has extensive experience in Mathematics and IT-based subjects since mid-1999 in public, and private institutions. He contributed his expert knowledge by conducting IT courses for teachers and collaborated with a number of IT companies in various consultative roles related to IT. His area of interest includes Big Data, Data Analytics, Machine Learning, IoT, and Distributed Computing.

BIG DATA TECHNOLOGIES

SYNOPSIS

This MicroCreds course offers a comprehensive overview of Big Data technologies and computational techniques that are useful for big data management tasks and activities. The participants learn how to install, configure, and maintain big data technologies that are used in real-world applications. The in-course assessments involve a quiz, an individual assignment, and a final assessment in which students’ achieved knowledge and skills in using proper tools and technologies that can address requirements and challenges of a given big data case study will be assessed and evaluated.

LEARNING OUTCOMES

  • Design big data solution with a suitable data model using proper big data technologies to address requirements of a given big data scenario.

COURSE LECTURER

Dr Mohsen Marjani is a lecturer at the Department of Computing and IT, Taylor’s University. He has extensive experience in Mathematics and IT-based subjects since mid-1999 in public, and private institutions. He contributed his expert knowledge by conducting IT courses for teachers and collaborated with a number of IT companies in various consultative roles related to IT. His area of interest includes Big Data, Data Analytics, Machine Learning, IoT, and Distributed Computing.

BIG DATA MANAGEMENT

SYNOPSIS

In this MicroCreds course, participants learn the required knowledge and skills to propose appropriate big data management solutions based on requirements and limitations of different real-world big data problems. The learning is facilitated by online tutorials, lecture notes, and online engagements and activities. The students’ knowledge and skills in deploying proper technologies and managing big data challenges will be examined via a quiz, an individual assignment, and a final assessment in the form of a mini-project.

LEARNING OUTCOMES

  • Propose a big data management solution using structured and unstructured model for a given case study related to industrial or social problems

COURSE LECTURER

Dr Mohsen Marjani is a lecturer at the Department of Computing and IT, Taylor’s University. He has extensive experience in Mathematics and IT-based subjects since mid-1999 in public, and private institutions. He contributed his expert knowledge by conducting IT courses for teachers and collaborated with a number of IT companies in various consultative roles related to IT. His area of interest includes Big Data, Data Analytics, Machine Learning, IoT, and Distributed Computing.

APPLIED BIG DATA MANAGEMENT

SYNOPSIS

This MicroCreds course introduces big data modelling, big data management strategies, data ethics, privacy and security based on different case studies. Also, participants learn the required knowledge and skills to use big data technologies to address big data management challenges. This course is taught using guided online learning and problem-based learning through a combination of online lecture notes, pre-recorded teaching sources, online engagements, and assessment activities. Students’ achieved knowledge and skills in this course will be assessed and evaluated via a quiz, an individual assignment, and a final assessment in the form of a mini-project.

LEARNING OUTCOMES

  • Analyse the impact of using various big data tools in terms of big data management strategies, data ethics, privacy and security for a given case study.

COURSE LECTURER

Dr Mohsen Marjani is a lecturer at the Department of Computing and IT, Taylor’s University. He has extensive experience in Mathematics and IT-based subjects since mid-1999 in public, and private institutions. He contributed his expert knowledge by conducting IT courses for teachers and collaborated with a number of IT companies in various consultative roles related to IT. His area of interest includes Big Data, Data Analytics, Machine Learning, IoT, and Distributed Computing.

FUNDAMENTALS OF WIRELESS NETWORK

SYNOPSIS

The current era network is completely transforming from conventional networks to wireless networks due to the ease of access, cheap cost, and highly flexible approach. Mobile devices continue to evolve and penetrate our lives, leading to the increased importance of wireless communication, mobile computing, and computer security. This MicroCreds course covers wireless transmissions, signals and system principles, analog signals versus digital signals, mobile communication, and signal propagation. Furthermore, it looks into wireless networks, wireless network topology, the equipment involved in the setup, wireless standards, protocols, and security technology involved in its deployment.

LEARNING OUTCOMES

  • Explore the fundamental principles of wireless networks to secure a given network.

COURSE LECTURER

Dr Noor Zaman Jhanjhi is currently working as Associate Professor and Director Center for Smart Society 5.0 [CSS5] and Cybersecurity cluster head at Taylor's University School of Computer Science and Engineering. With 21 years of vast experience teaching internationally, he is involved in teaching specialisation courses such as Wireless Networks and Security, Secured Software Systems, Cloud Computing, Mobile Application Development, and Internet of Things (IoT).

He is also the Associate Editor and Editorial Assistant Board for several reputable journals and awarded globally as a top 1% reviewer by Publons (Web of Science). He has high indexed publications in WoS/ISI/SCI/Scopus and international Patents on his account, having edited/authored more than 29 research books published by world-class publishers. To date, Dr Zaman has completed more than 22 international funded research grants successfully.

Dr. Mike Kok is currently attached to Taylor’s School of Computer Science and Engineering. He is a well-established Senior Lecturer and his involvement in the academic field includes journal publication, presenting papers in conference and examiner for Thesis Completion Seminar. Mike is actively involved in scholarly activity in the field of Cyber Security; specialising in malware detection and machine learning.

WIRELESS SECURITY

SYNOPSIS

Mobile devices continue to evolve and penetrate our everyday lives, leading to the increased importance of wireless communication, mobile computing, and computer security. In the fast deployment and expanded usage of wireless infrastructure, there is a need to ensure its deployment safety and security. Wireless networks are easy to approach, and at the same time, they are easy to target for hackers. This MicroCreds course covers the possible growing threats to wireless devices, networks, services delivered over the mobile infrastructure, wireless security, attack vectors, mobile security building blocks, cryptography, and best possible mitigation ways to secure wireless networks.

LEARNING OUTCOMES

  • Apply appropriate security measures to secure a wireless network from intruder attacks. 

COURSE LECTURER

Dr Noor Zaman Jhanjhi is currently working as Associate Professor and Director Center for Smart Society 5.0 [CSS5] and Cybersecurity cluster head at Taylor's University School of Computer Science and Engineering. With 21 years of vast experience teaching internationally, he is involved in teaching specialisation courses such as Wireless Networks and Security, Secured Software Systems, Cloud Computing, Mobile Application Development, and Internet of Things (IoT).

He is also the Associate Editor and Editorial Assistant Board for several reputable journals and awarded globally as a top 1% reviewer by Publons (Web of Science). He has high indexed publications in WoS/ISI/SCI/Scopus and international Patents on his account, having edited/authored more than 29 research books published by world-class publishers. To date, Dr Zaman has completed more than 22 international funded research grants successfully.

Dr. Mike Kok is currently attached to Taylor’s School of Computer Science and Engineering. He is a well-established Senior Lecturer and his involvement in the academic field includes journal publication, presenting papers in conference and examiner for Thesis Completion Seminar. Mike is actively involved in scholarly activity in the field of Cyber Security; specialising in malware detection and machine learning.

CYBERSECURITY THREATS

SYNOPSIS

Mobile and wireless devices today have outnumbered computers worldwide. This ease of access with wireless gadgets is attractive for the users. At the same time, it is easy for hackers to attack systems with malicious intent by utilising the vulnerabilities in mobile third-party applications, of which the users are not aware. This MicroCreds course covers the latest cybersecurity threats, such as phishing, advanced persistent threats (APT), compromised Wi-Fi hotspots, hacked applications, top 80 security issues faced by the world of wireless interconnectivity, and privacy leakage.

LEARNING OUTCOMES

  • Apply skills to solve practical problems in cybersecurity

COURSE LECTURER

Dr Noor Zaman Jhanjhi is currently working as Associate Professor and Director Center for Smart Society 5.0 [CSS5] and Cybersecurity cluster head at Taylor's University School of Computer Science and Engineering. With 21 years of vast experience teaching internationally, he is involved in teaching specialisation courses such as Wireless Networks and Security, Secured Software Systems, Cloud Computing, Mobile Application Development, and Internet of Things (IoT).

He is also the Associate Editor and Editorial Assistant Board for several reputable journals and awarded globally as a top 1% reviewer by Publons (Web of Science). He has high indexed publications in WoS/ISI/SCI/Scopus and international Patents on his account, having edited/authored more than 29 research books published by world-class publishers. To date, Dr Zaman has completed more than 22 international funded research grants successfully.

Dr. Mike Kok is currently attached to Taylor’s School of Computer Science and Engineering. He is a well-established Senior Lecturer and his involvement in the academic field includes journal publication, presenting papers in conference and examiner for Thesis Completion Seminar. Mike is actively involved in scholarly activity in the field of Cyber Security; specialising in malware detection and machinelearning.

CYBERSECURITY FOR MOBILE DEVICES

SYNOPSIS

Mobile devices continue to evolve and penetrate our everyday lives, leading to the increased importance of wireless communication, mobile computing, and cybersecurity. This MicroCreds course covers mobile device security risks that discuss the current cybersecurity state, types of threats, vulnerabilities, and risk. Furthermore, it looks at available security solutions such as good governance and safeguards policy, mainly focused on the cybersecurity issues for the mobile/smartphone domain. It discusses the Android app and processes such as permission, sandbox, and attack path.

LEARNING OUTCOMES

  • Evaluate different security mechanism for mobile devices to mitigate cybersecurity issues. 

COURSE LECTURER

Dr Noor Zaman Jhanjhi is currently working as Associate Professor and Director Center for Smart Society 5.0 [CSS5] and Cybersecurity cluster head at Taylor's University School of Computer Science and Engineering. With 21 years of vast experience teaching internationally, he is involved in teaching specialisation courses such as Wireless Networks and Security, Secured Software Systems, Cloud Computing, Mobile Application Development, and Internet of Things (IoT).

He is also the Associate Editor and Editorial Assistant Board for several reputable journals and awarded globally as a top 1% reviewer by Publons (Web of Science). He has high indexed publications in WoS/ISI/SCI/Scopus and international Patents on his account, having edited/authored more than 29 research books published by world-class publishers. To date, Dr Zaman has completed more than 22 international funded research grants successfully.

Dr. Mike Kok is currently attached to Taylor’s School of Computer Science and Engineering. He is a well-established Senior Lecturer and his involvement in the academic field includes journal publication, presenting papers in conference and examiner for Thesis Completion Seminar. Mike is actively involved in scholarly activity in the field of Cyber Security; specialising in malware detection and machine learning.

*All information is subject to change. Readers are responsible for verifying information that pertains to them by contacting the university.

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