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Major in Data Science & Economics Assignment Example, Singapore

The Data Science and Economics (DSE) cross-disciplinary program aims to produce students who are able to understand how data is used for an individual’s benefit, as well as society at large. The aim of this course would be that these graduates will have strong foundation knowledge in both economics with an emphasis on empirical analysis alongside their understanding of statistics or programming languages like RowsamLisp etc.. They’ll learn what makes up modern-day economies which can then help them apply those insights into organizations where they work so it has a greater impact than just being theoretical knowledge only.

The course would be for students who are looking to make use of data science in economics/financial markets. This is differentiated from the Data Science (DS) program that has an emphasis on being able to formulate models and mathematics, which is more focused on quantitative finance.

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This course is looking to train students who are able to understand how data is used for an individual’s benefit, as well as society at large.

Learning Outcomes in Major in Data Science & Economics Assignment 

 By completing this course, Singaporean students will be able to take their first steps towards achieving career success in Data Science & Economics. They’ll do so by solving the following learning outcomes

Assignment Activity 1: To comprehend the conceptual and methodological foundations of analytical techniques for data science and the fundamentals of theoretical and empirical economic analysis

Understanding the conceptual and methodological foundations of analytical techniques is the prerequisite to studying data science. That is because knowledge about raw data, machine learning algorithms, etc. becomes a lot clearer when one knows how data can be measured and what options exist for measurements thereof. When it comes to the fundamentals of theoretical and empirical economic analysis, both disciplines depend on each other in that without concepts from economics, statistics renders meaningless.

Furthermore, economic assumptions help interpret statistical relationships in a different way than purely statistically-driven methodology would. For instance the control variable selection could be based on an understanding of how markets depend upon each other or which goods could easily substitute for others during periods where demand changes drastically so as not to create unnecessary frustration regarding statistical methods we employ.

To comprehend the computational and more detailed theoretical methods of data science from a practical angle, for instance understanding how to process large datasets, use machine-learning algorithms on structured or less structured information, and retrieve patterns from these datasets. Understandingly identify metrics that can be used across various fields of research. Furthermore, learn about developments in empirical economic analysis through exposure to topics such as cost-benefit analysis and welfare economics basics.  

Assignment Activity 2: To appreciate and understand current data-scientific problems in economics and be able to identify and formulate practically relevant questions and issues in various aspects of economics, for example, in macroeconomic and financial modeling, or health and labor markets

To appreciate the current data-scientific problems in economics, one could identify and formulate practically relevant questions and issues in various aspects.

To understand how data analysis can be used to analyze consumption data of households, one might have explored consumption patterns for food items during past four months or so. To extend it to other areas, one may analyze consumption patterns for construction materials just before monsoon season. Such consumption analyses are becoming increasingly important for Indian industry given increased volatility in prices these days due to policy actions on either side of the spectrum – interest rates being lowered to promote consumption while fuel prices being increased because global oil supplies are slackening.

A number of firms out there offer data analytics services (especially solutions related to predictive modeling).  These services are in-demand from a number of sectors, including but not limited to marketing, finance and banking.

In the past few years, Big Data analytics have been growing at an extremely fast pace in different industry verticals. In this sector, R is one programming language that firms use extensively for data analysis purposes. For instance, many companies in this sector use R with Hadoop to work on huge datasets.

One of the most widely used software packages in the market is SAS, developed by SAS Institute Inc.  Using SAS for data analysis requires one to learn its programming language called SAS-Lang (the ‘Lang’ in the name stands for ‘Language’).  Though SAS is a commercial software product, its cost is not prohibitive for most small firms interested in learning data analytics (or even medium-scale enterprises).

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Assignment Activity 3: To apply appropriate analytic tools and techniques to resolve complex data-scientific problems in various aspects of economics using appropriately curated data, and be able to clearly communicate findings and insights gained using appropriate visualization tools

The data scientist has to be knowledgeable in all facets of economics, which is a very complicated field. He has to remember that the ways these analytics will be applied to each aspect may vary and take general analytical skills into account.

Intelligent data analysis requires deep analytical thinking and an understanding of how all these effects will come together in order to get a better overall view on any subject being analyzed for information.

Appropriate tools and techniques can be applied to problems in any data-science field. Here are a few examples: metrics and benchmarks in the knowledge discovery process, wavelet thresholding for hierarchical clustering whilst evaluating contrast enhancement, sorting methods such as a k-nearest neighbor or shell sort for univariate analysis.

A business could do this by collecting pertinent information on their customers relevant to them and computing predictive features based on inherent characteristics such as customer identifiers, aggregated transactional history (e.g., total spend), data derived from web activity (e.g., behavior graph) or other forms of specific behavioral evaluation (e.g., survey responses).

The appropriate analytical toolkit will depend on the problem you’re trying to solve, but there are several important general principles to keep in mind:

  • Need for accuracy. Given that there is uncertainty in all systems, it is critical that the data analyst knows which methods provide precision and reliability; these methods include Monte Carlo simulations.
  • Descriptive vs predictive. This distinction has implications for data collection, modeling, and interpretation. For example, when sampling from a population, predictive methods would try to minimize the margin of error (which is measured by the variance), whereas descriptive methods seek to reduce bias in sample data.
  • Modeling vs machine learning. Modeling implies finding which variables are relevant given the limited set of features available. These features are established via machine learning by analyzing the data.
  • Reliability of results. As with all models, it is critical to ensure that the final analysis being conducted is both reliable and consistent with theory. This can be ensured through Bayesian analyses, sensitivity testing, cross-validation and others.
  • Evaluation. This requires understanding the data limitations and how they affect the analysis. A good example of this is having some clear insight on whether any statistical models used are linear or nonlinear based on some theory about the underlying process, etc.

The analytical research approach should be formalized toward answering questions that address specific business problems. This type of approach requires the support of domain experts, who are aware of the business context, to ensure that analytical research is framed with relevant questions.

Assignment Activity 4: To cultivate in the students the practice of independent and peer learning so as to prepare them to function effectively in diverse careers as data science professionals and economists

The shift to data science knowledge workers is redefining the workplace landscape. Employers are seeking well-rounded, dynamic individuals because that’s exactly how data science needs to be done.

Graduates with data science qualifications will have incredible opportunities for both direct hire jobs and contract work. Data scientists are needed in every industry – not just technology or mathematics! And they need employees across all skill levels. New developers may be tasked with monitoring software or creating prototypes, while more experienced developers may go into developing new products or improving existing ones.

Teachers should prepare their students by teaching them skills that are necessary for success in today’s economy – critical thinking, creativity, collaboration, and communication-plus math and problem-solving skills that students will need to solve the data problems.

And more and more businesses are catching on to the opportunities available by hiring data science talent, ushering in a new era of modern retail.

Data science is opening up multiple career paths for students – both in tech and beyond. The flexibility, creativity, and problem-solving skills needed to enter into this area of study make data science a field that fits many different interests. And the employment opportunities are growing rapidly.

Businesses today are increasingly reliant on drawing insights from their data to make better decisions.

As the demand for data scientists pushes past supply, companies are making it a priority to hire accomplished graduates with degrees in advanced math and computer science and there’s a lot of money at stake.

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