As the field of data science continues to evolve and expand in the twentieth century , the demand for professionals with a comprehensive understanding of the latest tools, techniques, and best practices has never been higher. Pursuing a master's degree in applied data science at Syracuse University was a perfect tool to help me develop the expertise I needed to succeed in this exciting field, and prepare me for a range of career opportunities in industries such as finance, healthcare, technology, and more.

My expectation from this this program were for it to provide me with the expertise and hands-on experience necessary to succeed in this dynamic field, and prepare me for the range of career opportunities in various industries.

During my studies, I developed a solid foundation in programming and software engineering principles. Specifically, I gained expertise in programming languages such as Python and R, as well as learn about software engineering practices that are essential for building reliable and scalable data-driven systems. This was coupled with access to advanced tools like Hadoop for Big Data, various cloud technologies.

I also gained a strong understanding of statistical methods and machine learning algorithms. Topics such as probability theory, regression analysis, clustering, and decision trees were covered in depth, and I the course work allowed me the opportunity to apply these methods to real-world data problems.

In addition, learnt how to collect, clean, store, and analyze data. This aspect of data science is essential for success in the field, and the experience involved working with a variety of data types and sources, including structured and unstructured data. Throughout the course work I also became proficient in using data management tools such as SQL and Hadoop.

Another important aspect of data science is the ability to communicate findings and insights to a variety of stakeholders. This is at the core of this master's program, what I call putting the “applied” in data science. It is of paramount importance to be able to convey the results of analysis to a variety of people from a diverse background. It is a true art form and one that is often only mastered by the true data scientist. This was by far my biggest motivation behind choosing to pursue this program.

I greatly improved my communication and visualization skills, through constant deliverables expected in the form of actionable reports of analysis than files of code. To better improve communication, a myriad of toolsets were introduced, this included the use of tools such as Tableau and Power BI. These skills will be essential for success in the field and will help me effectively communicate complex data insights to a broad audience.

The opportunity to explore domain-specific applications of data science was also provided within the program . While the principles of data science are generally applicable across many different industries and domains, each industry has its unique challenges and opportunities.

Throughout my masters degree program, we were focused on six key learning outcomes. Each of these goals was essential to my development as a data scientist, and I had to work hard to achieve them.

The first goal was to identify and leverage applicable technologies to collect, store, and access data. I learned about the latest technologies, including traditional RDBMS and big data systems like distributed architecture. I understand how to store unstructured data in a meaningful way for faster and reliable retrieval. Additionally, I learned different strategies for reading and writing data and can make informed decisions about which technology to use for a given problem.

The second goal was to create actionable insights across a range of contexts, including societal, business, and political settings, using data and the full data science life cycle. I learned the entire data science lifecycle, from data gathering to preparation, analysis, and visualization. I also gained domain knowledge in the context in which I operate to recognize pertinent problems and evaluate which data sources and procedures are best suited for analysis.

The third goal was to apply visualization and predictive models to help generate actionable insights. I learned that a picture can speak a thousand words, and it's crucial to use appropriate visual tools to interpret complex sets of information. Additionally, after exploring data, I gained the ability to create prediction models that can help predict outcomes with a certain level of accuracy, if the business goal demands it
The fourth goal was to use programming languages such as R and Python to support the generation of actionable insights. I learned to draw insights from data and present them in a useful and actionable manner by utilizing the features and capabilities of programming languages like R and Python. I also learned how to manage and analyze data, establish models, and produce visualizations or other types of output that can support decision-making processes. Additionally, I learned how to explain data analysis findings to non-technical stakeholders using short reports or visualizations.

The fifth goal was to communicate insights gained via visualization and analytics to a broad range of audiences, including project sponsors and technical team leads. I learned to draw insights from data and present them in a useful and actionable manner by utilizing programming languages, dashboarding and reporting tools. I gained the ability to manage and analyze data, establish models, and produce visualizations or other types of output that can support decision-making processes. I also learned the best ways to explain data analysis findings to non-technical stakeholders, such as through short reports or visualizations.

The sixth and final goal was to apply ethics in the development, use, and evaluation of data and predictive models, such as fairness, bias, transparency, and privacy. I learned that data-driven decision-making must be fair, transparent, and responsible. Therefore, it is crucial to apply ethics in the data science lifecycle. I gained awareness of the potential effects of data gathering, analysis, and modeling on society at large. I also learned how to recognize and respond to ethical issues related to privacy, fairness, prejudice, and openness.

My summer internship at SiriusXM as a cloud software developer was a great opportunity that helped me enhance my skills and knowledge in applied data science. During my internship, I was tasked with leveraging big data technologies to build an infrastructure to migrate data systems to the cloud. This experience was particularly valuable to me, as it enabled me to put into practice the concepts and theories that I had learned in my master’s degree program.

One of the most important skills that I developed during my internship was the ability to work with cloud-based big data technologies. I learned how to use a variety of cloud services and tools, such as Amazon Web Services (AWS) and Apache Hadoop, to store, process, and analyze large data sets. This was particularly useful in my master's program, as it enabled me to analyze large data sets and derive valuable insights using the latest tools and techniques.

Another skill that I developed during my internship was the ability to work in a team environment. I worked with a team of software developers, data engineers, and data scientists, which helped me to learn how to collaborate with others and communicate effectively. This skill has proven to be particularly useful in my master's program, as it has enabled me to work effectively with my classmates on group projects and to communicate complex ideas and concepts to a diverse range of stakeholders.

It was an invaluable experience that helped me to bridge the gap between academic learning and real-world application. The skills and knowledge that I gained during my internship have proved to be invaluable in my master's program enabling me to apply cutting-edge techniques and technologies to solve complex data science problems.

My best class of the program would be a hearty tie between MBC 638 and CIS 667, Data analysis and decision making, and Introduction to Artificial intelligence respectively. The former was a secondary core as part of the program that is a part of the Whitman School of Management and is taught by Prof. Anna Chernobai. The course covers the entire data pipeline using Microsoft Excel. My former experience with Microsoft Excel being fairly advacned than my peers, it would have been a no-brainer to skip it, yet based on reviews from my peers I decided to take it. My motivation to take the class was not the content, but the clarity with which the professor taught crucial concepts of statistics important for data science. Personally, being acquainted with the tool allowed to not focus on learning and mastering the tool but to solely focus on the concepts being taught. The course work was not the most difficult but thanks to great teaching experience of the professor the weekly quizzes structured to test our conceptual understanding kept everybody on the toes. The final exams being more tricky that hard to score well in was a welcomed challenge. This allowed a harsh review of my personal understanding of various concepts which I am proud to say after this course are crystal clear.

The elective course that I found to be immensely captivating was Introduction to Artificial Intelligence, which was not a part of my degree curriculum. Despite the fact that my program concentrated on improving my analytical and communication skills, I was not being challenged on the programming front, which I have always enjoyed. This course, on the other hand, targeted a different area of my brain, stimulating my programming and problem-solving abilities. The weekly assignments were a formidable combination of mathematics, problem solving, and programming, constituting a mental marathon that I found both stimulating and refreshing. The course delved into the tools of automation and algorithms that power artificial intelligence machines, which was a unique and intriguing experience for me. It was refreshing to shift my focus away from data and toward the capabilities of artificial intelligence machines, which are steadily moving from a condition-based paradigm to one that is characterized by the ability to adapt and evolve to changing circumstances using fuzzy logic.It allowed me to apply my programming skills in a new and exciting field, one that holds immense potential for the future of technology.

I had high expectations for my master's degree in applied data science. In hindsight I can confidently confer that the program provided me with the knowledge and skills necessary to succeed in the field, and I am excited to embark on the journey ahead of discovery and growth in my professional career.