Master of Science (Data Science and Analytics)

Program Overview

What is the aim of the Master of Science (Data Science and Analytics)?
Master of Science (Data Science and Analytics) programme will equip graduates with the core Data Science and Analytics knowledge required to work with large and complex data sets in multidisciplinary fields. The core modules of the programme include Data Mining, Data Analytical Programming, Machine Learning for Data Science, Big Data Storage and Management and Data Visualisation and Visual Analytics. Graduates will have strong capabilities to integrate the specialised filed requirements with Data Science and Analytics to improve and transform the Data Science process within an organisation.
For successful completion of Master’s degree each candidate should publish minimum of two research articles in Scopus indexed journals with Lincoln University College affiliation.

What is the duration of the MCS DS?
Full Time: 1 Years 6 Months

What are the intakes for this program?
Intakes: March, July, November

What specializations are available?
At Lincoln University College, the Master of Science in Data Science and Analytics program offers several specializations that allow students to focus on specific areas within the field. While the program primarily covers core topics such as Data Mining, Machine Learning, and Big Data Management, students can tailor their studies according to their interests and career goals. Some common areas of specialization include:
1. Machine Learning – Focusing on algorithms and statistical models that enable computers to perform tasks without explicit instructions.
2. Big Data Management – Concentrating on handling and processing large volumes of data using various technologies and frameworks.
3. Data Visualization – Learning techniques to represent data graphically for clearer understanding and insights.
The program aims to equip graduates with essential knowledge and skills to manage complex data sets across various disciplines, and students are also encouraged to engage in research activities, including publishing in Scopus-indexed journals

What practical experience is included in the MCS DS?
The Master of Science in Data Science and Analytics (MCS DS) program at Lincoln University College includes several practical components designed to equip students with hands-on experience in the field. Key aspects of practical experience in this program involve:
1. Capstone Project: Students engage in a capstone project, where they apply the knowledge and skills gained throughout the program to solve real-world data science problems. This project typically requires students to analyze complex datasets, apply various data analytical methods, and propose actionable insights.
2. Research Articles: To fulfill graduation requirements, students must publish a minimum of two research articles in Scopus-indexed journals. This aspect not only reinforces practical application through research but also prepares students for academic contributions and enhances their professional profiles.
3. Hands-on Modules: Core modules such as Data Mining, Data Analytical Programming, Machine Learning, and Data Visualization include practical assignments that require students to work with real data sets and analytics tools.
4. Internships or Industry Collaborations: While not explicitly mentioned, many programs in data science encourage internships or industry partnerships that provide practical experience, allowing students to apply their theoretical knowledge in professional settings

Do you have Alumni?
Yes, we do have.
https://alumni.lincoln.edu.my/

Deferment / Withdrawal

What happens if I change my mind after enrolling and want to withdraw?
Refund policies vary; it’s best to check with the admissions or finance department.
https://www.lincoln.edu.my/refund-policy/

May I differ a semester?
Yes, you can defer a semester at Lincoln University College (LUC), but you must follow the university’s deferral process:
1. Submit a Formal Request: You need to submit a deferral request to the university, stating the reasons for deferring.
2. Approval Required: The deferral must be approved by the university administration, and valid reasons such as medical issues, financial difficulties, or personal circumstances may be required.
3. Duration: The deferral is usually granted for one semester but can vary based on your situation and university policies.
4. Impact on Visa: For international students, deferring a semester may impact your student visa, so you must check with the Visa and Immigration Office.
Ensure you communicate early with the Student Affairs Office or your academic advisor for guidance on the deferral process.

How many semesters can I differ in a raw or during the entire programme?
At Lincoln University College (LUC), you are allowed to defer up to two consecutive semesters in a row, but this may vary depending on your circumstances and program requirements. However, no more than two or three semesters deferment throughout the duration of the program will be allowed.
Extended deferrals may require additional justification and approval from the university administration.
It’s crucial to check with the Student Affairs Office about the impact of deferment on your study progress and visa (if applicable).

Career Prospects

What career opportunities are available after graduation?
The Master of Science (Data Science and Analytics) programme blends scientific knowledge with practical learning to prepare you to further your career in today’s data-driven environment. Their program-acquired abilities will prepare them for graduate-level positions in this industry. The most prominent positions held by someone having this degree are listed in below:
• Data Scientist
• Big Data Analyst
• Machine Learning Engineer
• Mining Analyst
• Data Modeler
• Data Architect/Engineer
• Qualitative Analyst

Does the program prepare students for licensure?
The Master of Science in Data Science and Analytics program at Lincoln University College does not specifically prepare students for licensure, as this field typically does not have standardized licensure requirements like professions such as engineering or nursing. However, the program is designed to equip graduates with essential skills and knowledge in handling large and complex data sets across various disciplines, including core topics like Data Mining, Machine Learning, and Data Visualization.
Additionally, a significant requirement for graduation is that each student must publish a minimum of two research articles in Scopus-indexed journals, which emphasizes the program’s focus on research and practical application of data science.

Are there leadership roles available for graduates?
Yes, graduates can find various leadership roles, especially in fields like Data Science and Analytics, Computer Science, and Engineering. Many companies actively seek graduates who not only possess technical skills but also demonstrate strong leadership abilities.
For example, graduates from the Master of Science in Data Science and Analytics program are expected to exhibit good leadership qualities, effective communication skills, and teamwork capabilities. The curriculum is designed to prepare them for roles where they can creatively and ethically solve IT-related problems, thereby enhancing their leadership potential in organizations

Are there career support services after graduation?
Yes, LUC provides career support services for graduates, including:
1. Job Placement Help: Assisting with finding teaching jobs and other roles.
2. Career Counselling: Offering guidance on career options, resume writing, and interviews.
3. Workshops: Hosting sessions on job market trends and professional development.
4. Alumni Network: Connecting graduates with a network for job opportunities and mentorship

Entry Requirements

What are the eligibility criteria for admission to the program?
i. A Bachelor’s degree (Level 6, MQF) in Computing or related fields with a minimum CGPA of 2.50, as accepted by the HEP Senate; OR

ii. A Bachelor’s degree (Level 6, MQF) in Computing or related fields with a minimum CGPA of 2.00 and not meeting a CGPA of 2.50 can be accepted subject to a thorough rigorous assessment as determined by the HEP; OR

iii. A Bachelor’s degree (Level 6, MQF) in Non-Computing field with a minimum CGPA of 2.00 can be accepted subject to a thorough rigorous assessment as determined by the HEP to identify the appropriate prerequisite courses that equivalent to their working experience in the Computing or related fields; OR

iv. A Bachelor’s degree (Level 6, MQF) in Non-Computing field with a minimum CGPA of 2.00 can be accepted subject to appropriate prerequisite courses; OR

v. Other qualifications equivalent to a Bachelor’s degree (Level 6, MQF) in Computing or related fields recognised by the Government of Malaysia must fulfil the requirement on item i or ii.

Entry Requirements:
Achieve a minimum of Band 4 in MUET or equivalent to CEFR (Low B2).
If a student does not meet this requirement, the HEP must offer English proficiency courses to ensure that the student’s proficiency is sufficient to meet the needs of the programme.

Can I apply with equivalent qualifications from another country?
Yes, qualifications recognized as equivalent by the Malaysian government are accepted.

Can I do credit transfer?
Yes, credit transfer is generally possible for the Master of Computer Science program at Lincoln University College. Students with relevant prior qualifications or coursework may be eligible to transfer credits, subject to the university’s policies and the approval of the program coordinator.

How many credits can I transfer?
You may transfer up to 30% of the credits, depending on how closely your diploma courses align with the Master of Computer Science subjects.

What are the documents needed for doing this credit transfer?
The documents typically required for a credit transfer application include:

1. Official Academic Transcripts
2. Course Syllabi/Descriptions
3. Diploma Certificate
4. Identification Documents (A valid ID or passport to verify your identity).

How do I apply for a credit transfer?

Before Arrival/ Before Registration:
– Submit your application through the Lincoln University College (LUC) website.
– Upload necessary documents, including your transcript, course outlines, and official certificate.

After Arrival:
– Once registered, apply for credit transfer through the Lincoln Learning System (My LLS).

Follow-Up:
– Contact the relevant faculty to check the status of your application.

What is the English language eligibility criteria?
Achieve a minimum of Band 4 in MUET or equivalent to CEFR (Low B2).
If a student does not meet this requirement, the HEP must offer English proficiency courses to ensure that the student’s proficiency is sufficient to meet the needs of the programme.

Can I study part-time?
No

If I don’t have the required academic qualifications but have experience in the relevant field, am I eligible?
Yes, you are eligible through the APEL (Accreditation of Prior Experiential Learning) pathway, which has been approved by the Malaysian Qualifications Agency (MQA) for Lincoln University College. Your relevant work experience can be assessed and recognized for entry into a bachelor’s program.

How does this APEL works?
You have to take APEL assessment, and we will check your working experience if it fits the entry qualification you can join.

Teaching /Learning /Assessment

What subjects are covered in the program?
The Master of Science in Data Science and Analytics program at Lincoln University College covers a range of essential subjects designed to equip graduates with the necessary skills to manage and analyze large and complex datasets. The core modules of the program include:
1. Data Mining
2. Data Analytical Programming
3. Machine Learning
4. Big Data Management
5. Data Visualization

What skills will graduates develop?
Graduates of the Master of Science in Data Science and Analytics program will develop a comprehensive set of skills essential for effectively managing and analyzing large and complex data sets across various disciplines. Key skills include:
1. Data Management and Mining: Proficiency in handling and extracting meaningful insights from extensive data collections.
2. Machine Learning and Statistical Analysis: Understanding advanced techniques for predictive modeling and data interpretation.
3. Big Data Technologies: Familiarity with tools and frameworks used in managing and analyzing big data.
4. Data Visualization: Skills in presenting data findings in clear, engaging formats that aid decision-making.
5. Research and Publication: The program emphasizes research capabilities, requiring students to publish articles in reputable journals

What technology-related subjects are taught?
Lincoln University College offers a variety of technology-related subjects across different programs, particularly in the field of computer science and data analytics. Here are some of the key subjects and areas of study available:
1. Data Science and Analytics: This master’s program includes subjects focusing on statistical methods, data mining, machine learning, and big data analytics, providing students with a strong foundation in data analytical techniques and their applications in various industries.
2. Computer Science and Software Engineering: The curriculum covers software development life cycles, agile methodologies, and object-oriented programming. It also emphasizes the importance of quality assurance in software products and includes project management documentation.
3. Artificial Intelligence: Students can explore advanced concepts in AI, including machine learning algorithms, natural language processing, and the ethical implications of AI technologies.
4. Cloud Infrastructure: This subject focuses on cloud computing principles, deployment models, and the technologies that support cloud infrastructure, which is crucial for modern IT environments.
5. Telecommunication Systems: This subject dives into the principles and technologies underpinning telecommunications, covering both theoretical and practical aspects.
These subjects are designed to equip students with both theoretical knowledge and practical skills, preparing them for careers in various technology-related fields.

How are students assessed?
Students in the Master of Science (Data Science and Analytics) program at Lincoln University College are assessed through a combination of coursework, examinations, and practical projects. The assessment methods are designed to evaluate students’ understanding of key concepts, their ability to apply analytical techniques, and their proficiency in using data science tools and methodologies.
Key aspects of the assessment include:
1. Coursework and Projects: Students engage in assignments that require them to apply their knowledge to real-world data problems, often collaborating in teams. This includes practical projects that may involve data mining, machine learning, and data visualization.
2. Examinations: Formal exams are part of the evaluation process, assessing theoretical knowledge and problem-solving skills.
3. Research Contribution: An important requirement for graduation is the publication of a minimum of two research articles in Scopus-indexed journals under the university’s affiliation. This emphasizes the importance of research and the application of theoretical knowledge in practical scenarios

What is the Practical structure like?
The practical structure of the Master of Science in Data Science and Analytics program at Lincoln University College includes several key components designed to equip students with hands-on experience and industry-relevant skills.
1. Hands-On Projects: Students engage in practical projects throughout the course, allowing them to apply theoretical knowledge to real-world problems. This may include data analysis, machine learning applications, and data visualization tasks.
2. Research Component: A significant requirement is for students to publish a minimum of two research articles in Scopus-indexed journals. This aspect emphasizes the importance of research skills and contributes to students’ professional profiles.
3. Core Modules: The program includes core modules such as Data Mining, Data Analytical Programming, Machine Learning, Big Data Management, and Data Visualization, each typically containing practical assignments that reinforce theoretical concepts.
4. Capstone Project: As part of the curriculum, students often undertake a capstone project, which integrates their learning and demonstrates their ability to manage and analyze complex data sets.

How do I prepare for the Practical?
Preparing for your practical in Data Analytics requires a well-structured approach to ensure you cover all necessary aspects effectively. Here are some key steps to consider:
1. Review Course Material: Go through your lecture notes, textbooks, and any supplementary materials provided during the course. Focus on key concepts, methodologies, and techniques that you’ve learned.
2. Understand Tools and Technologies: Familiarize yourself with the specific software and programming languages you’ll be using during the practical. This could include tools like Python, R, SQL, or data visualization software. Make sure you know how to navigate these tools and apply them to your datasets.
3. Hands-on Practice: Engage in practical exercises using real datasets. If possible, find past practical assignments or sample datasets to work with. This will help reinforce your understanding of data manipulation, analysis, and interpretation.
4. Focus on Key Competencies: Brush up on competencies such as statistical analysis, regression techniques, data visualization, and data cleaning processes. Understanding these concepts will help you apply the right techniques during your practical.
5. Prepare for Common Questions: Anticipate questions or scenarios that may be presented during the practical. Think through the methodologies you would use to analyze data, draw conclusions, and present your findings.
6. Study Past Practical Assessments: If available, review past practical exams or assessments to understand the format and types of questions typically asked. This will give you insight into what to expect and how to structure your answers.
7. Group Study: Collaborate with classmates to discuss challenging topics or share insights. Group study can help solidify your understanding and uncover different perspectives on the material.
8. Stay Organized: Create a checklist of topics to cover and resources to review. Staying organized can help manage your time effectively leading up to the practical.
9. Reach Out for Clarification: If you’re unclear about any concepts or tools, don’t hesitate to ask your instructor or peers for clarification.

How can I volunteer for community services?
Lincoln encourages participation in community service programs organized by the student affairs office or academic departments.

Is there a focus on contemporary education issues?
Yes, courses like Philosophy and Current Issues Course International Student address current challenges.

Can I teach globally with the MCS certification?
Yes, but additional qualifications may be required depending on the country’s regulations.

When can I start the course, and when do I receive the materials?
The course starts in March, July, November, and materials are provided at the start of the academic term.

Attendance

Why is regular attendance important for this course?
Regular attendance ensures you stay engaged with the course material, participate in discussions, and better understand the subject. It also helps you keep up with assignments, group work, and any changes in deadlines or course requirements.

Will my attendance impact my final grade?
Yes, attendance is often factored into participation marks. Missing too many classes can also affect your understanding of key concepts, leading to lower performance on exams and assignments, which could ultimately impact your final grade.