At Montclair State University (MSU), I completed my Master's in Data Science. This university, established in 1908, offers more than 300 doctoral, master’s and bachelor-level programs, I was part of the College of Science and Mathematics (CSAM) at MSU, this college provided me with a stimulating academic environment and access to resources that facilitated my learning and growth.

Coursework Highlights

My coursework covered essential topics like statistics, exploratory data analysis, database systems, data mining, computer algorithms, machine learning, big data analytics, and Python for data science.

Machine Learning

Dr. Jing Peng’s course offered a balanced introduction to modern machine learning, covering algorithms like Bayesian classifiers, decision trees, SVMs, neural networks, and reinforcement learning. Dr. Peng’s expertise in reinforcement learning and multi-agent systems added depth to the curriculum. Practical applications and experimental evaluations of algorithms equipped me with robust skills to develop and assess machine learning models.

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Big Data Analytics & Python for Data Science​

Both courses, taught by  Dr. Boxiang Dong, focused on big data management and Python for data analytics and visualization. Dr. Dong’s research in big data analytics informed the curriculum, covering Hadoop, Spark, data streaming, and machine learning with Spark MLlib. I learned to design and implement big data processing workflows, enhancing my ability to handle and analyze large datasets efficiently.

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Exploratory Data Analysis and Visualization

Led by Dr. Jiayin Wang, an expert in big data, cloud computing, and wireless networks, this course provided an in-depth introduction to the data science lifecycle. Through Python and Jupyter Notebook, I mastered data manipulation, processing, and visualization using key Python libraries. The course emphasized practical applications, enabling me to design and critique visualizations and solve real-world data problems. By the end, I was proficient in demonstrating data science skills and creating effective visual representations for data analysis.

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Database Systems

Under Dr. Angela Pena's guidance, the Database Systems course focused on the theory, construction, and application of databases. We developed ER and EER diagrams and created an E-Commerce website, translating theoretical designs into practical databases. This hands-on approach enhanced my ability to design, implement, and manage databases, critical skills for data scientists dealing with vast amounts of data. The course emphasized real-world applications, encouraging us to solve practical problems, thus equipping me with practical skills in database design and application. This knowledge is indispensable for effectively managing and querying data in a computerized world.

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Data Mining

Dr. Yan Kong’s course on Data Mining covered a wide array of methods for discovering patterns and trends in large datasets. Topics included knowledge discovery, data preprocessing, clustering, classification, and advanced topics like web mining and big data. Using tools like WEKA and Apache Mahout, I developed practical data mining skills applicable to various industries, making me adept at extracting meaningful insights from complex data sets.

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Computer Algorithms and Analysis

Led by Dr. Maggie Zhang, whose research spans big data, cloud computing, this course focused on fundamental algorithms and their applications. I explored sorting techniques, greedy algorithms, dynamic programming, and graph-related algorithms. This deep understanding of algorithm design and analysis is essential for optimizing data processing tasks in data science.

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Statistics for Data Sciences

Taught by Dr. Andrada Ivanescu, the statistics course at Montclair State University provided a comprehensive foundation in key statistical concepts and tools. Dr. Ivanescu’s expertise in functional data analysis enriched the curriculum, covering data visualization, relationships between variables, probability, and statistical inference techniques. Advanced topics included multiple regression, model selection, cross-validation, and bootstrap methods, all implemented using R software. 

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Research Methods in Computing & Master's Project

Dr. Jiayin Wang supervised these projects, focusing on developing predictive models for stock price movements and identifying diseases in potato crops using CNNs. These projects provided hands-on experience in applying machine learning techniques and deep learning models to real-world problems, refining my research and analytical skills crucial for a data scientist.

I pursued my Bachelor's in Mechanical Engineering at the National Institute of Technology Silchar, India, a premier institution recognized as one of national importance under the Ministry of Education, India. This institution provided me with a strong foundation in engineering principles, instilling a research-driven and entrepreneurial mindset.  

My coursework included subjects such as Applied Thermodynamics, Theory of Machines, Fluid Mechanics, Manufacturing Process, Material Science, Energy Science and Technology, Turbomachinery, Automobile Engineering, Power Plant Engineering, and Solar Architecture. These subjects formed a comprehensive background in both traditional and modern mechanical engineering, preparing me to excel in the field and pursue impactful research projects.

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Undergraduate Research Project

During my undergraduate studies, I had the opportunity to work under the mentorship of Dr. K.K. Sharma, a distinguished figure in Mechanical Engineering known for his work on wind turbine performance. His research includes innovative studies on modified asymmetric blade H-Darrieus VAWT rotors and blade thickness effects, with publications in renowned journals like the Journal of Energy Research and Environmental Technology, International Journal of Engineering Research in Electronics and Communication Engineering.

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Project on Vertical Axis Wind Turbines (VAWTs)

Our project focused on designing and testing six VAWTs with varying blade sizes and tower heights. The turbines were arranged in two rows, and we experimented with different patterns to study their effects on power output. Our findings showed that VAWTs with alternating tower heights generated more power than those with uniform heights. Additionally, positioning turbines at inclined angles relative to the wind direction further enhanced their efficiency. This setup demonstrated potential applications in electricity generation and water pumping, particularly for areas with low wind speeds. Collaborating with Dr. Sharma not only developed my technical skills but also deepened my understanding of innovative technologies.

Our findings were published in The Journal of Energy Research and Environmental Technology (JERET), Volume 4, Issue 1, January-March 2017, pages 13-14.link

Master's Other Projects:

ADQ Solar Tracking System (RTC/FCT) in ARM7 with Isis link

With the growing importance of renewable energy, our solar tracking project aimed to improve solar power efficiency using fuzzy logic and an advanced RTC system in ARM7 for virtual control. The system incorporated meteorological data to calculate optimal altitude and azimuthal angles, achieving an 18.57% efficiency improvement over standard setups. Featuring DC motors with light-dependent resistors (LDRs), mirrors, a 3D accelerometer, and RS232 communication, the project integrated circuit control in ISIS PROTEUS with code management in Keil4IDE.