It’s been a while since I last updated my blog, and a lot has happened in my life over the past few months. Life has thrown some unexpected curveballs my way, but I’ve come to realize that it’s all part of the journey.
Earlier in the year 2024, I managed to secure a place for the Associateship of The Malaysian Insurance Institute (AMII) Level 2, with the scholarship.
However, after giving the first week of class a try, I knew in my heart that I wasn’t ready to continue in the insurance field.
In addition, I received the news that I had failed my 309 Commercial Property and Business Interruption Insurance subject—meaning I would need to resit it soon.
I couldn’t ignore how burnt out I had become from studying insurance. It wasn’t fulfilling me the way it used to, and I knew I needed a change—something fresh, challenging, and aligned with my passion for data and technology.
That’s when I decided to take the leap and pursue something entirely new: a Master’s in Data Science.
To my surprise, shortly after I made that decision, I received an acceptance offer from the University of Malaya (UM) to study Data Science.
Excited about the possibilities ahead, and with enough savings and belief in myself, I made the bold choice to leave my job at Liberty General Insurance and embark on this new chapter as a full-time student.
I knew it wouldn’t be easy, but I felt deeply that this was the right path to grow and upskill in an entirely different domain.
The Curriculum: Courses, Content, and Learning Challenges
Embarking on my Master’s in Data Science at UM was an exciting yet daunting journey, filled with rigorous coursework and new challenges. My schedule was packed with diverse modules, each offering a different facet of the data science world.
Here’s a breakdown of my courses, the content I tackled, and the learning challenges I faced:
1. Research Methodology (WOX7001)
This course, led by Assoc. Prof. Dr. Norjihan Abdul Ghani, focused on developing research skills necessary for academic inquiry.
We explored different types of research (quantitative, qualitative, mixed methods) and dived into topics like literature reviews and systematic reviews.
The biggest challenge here was mastering research design and developing a comprehensive research proposal by mid-semester, which contributed 30% to our grade.
2. Principles of Data Science (WQD7001)
Taught by AP Dr. Maizatul Akmar Ismail, this course introduced the foundations of data science. It covered an overview of data science, the importance of reproducible research, and data preparation.
The practical application of techniques like exploratory data analysis (EDA) and cleaning data in real-world scenarios was tested in various assignments.
Mid-term exams and group projects pushed me to apply these concepts practically.
3. Data Analytics (WQD7003)
Under the guidance of Professor Dr. Loo Chu Kiong, this module emphasized the practical use of data analytics tools and methods.
I was already familiar with Python, but this course challenged me to deepen my understanding of Python’s capabilities.
The lab sessions and continuous assignments required a strong grasp of data preprocessing, data visualization, and statistical modeling techniques.
4. Programming for Data Science (WQD7004)
Taught by Assoc. Prof. Dr. Ang Tan Fong, this course focused on the programming languages and tools used in data science, particularly R.
We worked through labs and assignments, learning essential programming skills such as flow control, functions, and file handling.
Programming challenges were demanding, as we tackled complex problems like data structure manipulation and handling unstructured data.
5. Big Data Applications and Analytics (WQD7009)
Dr. Riyaz Ahamed led this course on big data, where we explored cloud computing, NoSQL platforms, and technologies like Hadoop, Apache Spark, and MongoDB.
It was an exciting area of study, and I enjoyed learning about big data storage, management, and analysis, but I also found it to be a steep learning curve.
Applying these tools to real-world data challenges was one of the more difficult aspects of the program.
Hands-on Projects and Assignments: Bridging Theory with Practice
Throughout the course, I’ve had the opportunity to work on several group projects and assignments that allowed me to bridge the gap between theory and practice.
These projects challenged me to apply the concepts I learned in class to real-world problems, and I was able to use modern data science tools and methodologies to solve them.
Here are the key projects I’ve worked on:
1. Cryptocurrency Trends Prediction using Sentiment Analysis
- Course: Research Methodology
- Objective: I developed a research proposal focused on predicting cryptocurrency trends using sentiment analysis from social media and news platforms.
- Skills I Gained:
- Writing a research proposal and designing a research methodology.
- Identifying research questions and analyzing existing literature.
- Understanding how sentiment analysis can be used to predict market trends.
- Outcome: I created a comprehensive research proposal that laid the foundation for studying how sentiment analysis can influence cryptocurrency market movements.
2. Customer Churn Prediction using Bank Data
- Course: Principles of Data Science
- Objective: This Group Project involved using Python to clean, explore, and model customer data from a bank to predict customer churn.
- Skills I Gained:
- Data cleaning and preprocessing techniques.
- Conducting exploratory data analysis (EDA) to identify trends and relationships.
- Building predictive models with machine learning algorithms like logistic regression and decision trees.
- Outcome: We developed a predictive model that helps banks identify customers at risk of leaving, enabling them to improve customer retention strategies.
3. Predictive Modelling of Skin Disease Types Using Clinical Indicators
- Course: Data Analytics
- Objective: This Group Project involved Python to clean and prepare clinical data, perform EDA, and build a predictive model to classify skin disease types based on clinical indicators.
- Skills I Gained:
- Data cleaning and transformation for healthcare datasets.
- Feature engineering to create useful inputs for the model.
- Building classification models such as Random Forest and Support Vector Machines (SVM).
- Outcome: We created a predictive model that can assist in diagnosing skin diseases, showcasing the power of data analytics in the healthcare industry.
4. State-Level Population Trends and Resource Allocation Needs in Malaysia
- Course: Programming for Data Science
- Objective: Using R, this Group Project involved cleaning and exploring population data and built both classification and regression models to analyze population trends and forecast resource allocation needs across Malaysia’s states.
- Skills I Gained:
- Data cleaning and preprocessing in R.
- Conducting EDA to analyze trends and outliers.
- Building classification and regression models to forecast future needs.
- Outcome: We provided a detailed analysis and predictive model that helps in optimizing resource allocation based on population trends in Malaysia.
5. A Hybrid Cloud Architecture for Analyzing Electric Vehicle (EV) Charging Patterns
- Course: Big Data Applications & Analytics
- Objective: This Group Project involved building a hybrid cloud architecture to analyze EV charging data, using Google Cloud for data pipelines, pulling data from Kaggle’s EV datasets, and visualizing it using Tableau.
- Skills I Gained:
- Using cloud platforms like Google Cloud to manage large datasets.
- Creating data pipelines to automate data collection and preprocessing.
- Visualizing data with Tableau to analyze charging patterns and demand forecasting.
- Outcome: We developed a cloud-based solution to analyze and visualize electric vehicle charging patterns, which provides valuable insights into the future of the EV market.
Conclusion
Looking back, my First Semester’s Master’s journey in Data Science at UM was both challenging and rewarding.
Each course pushed me to expand my technical skills, think critically, and apply data science concepts to real-world problems.
While the rigorous coursework and demanding assignments tested my perseverance, they also strengthened my analytical abilities and problem-solving mindset.
The experience not only deepened my expertise in data science but also prepared me to navigate complex data challenges with confidence.
Though the journey had its difficulties, it was ultimately a transformative learning experience that shaped my growth as a data professional.
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