Education marks a promising data-driven future for industry as well as for professionals with the growth in technology. Analytics and Data Science has spread its branches in course curriculums everywhere. More and more universities and institutes across the world are designing masters, diploma and Ph.D. programs dedicated to analytics, data science and artificial intelligence to offer well-versed knowledge of the cutting-edge technologies. Such colleges and universities are preparing validated professionals with theoretical and practical knowledge of the subject as per the industry demands. The University of Chicago is one of those bodies of knowledge honing analytical skills for budding future.
The University of Chicago is an urban research university that has driven new ways of thinking since 1890. Its commitment to free and open inquiry draws inspired scholars to the global campuses, where ideas are born that challenge and change the world.
The institute empowers individuals to challenge conventional thinking in pursuit of original ideas. Students test their ideas in graduate programs with UChicago scholars and become the next-generation of leaders in academia, industry, non-profits, and government.
Prominent Framework of the Program
The University of Chicago’s program is based on analytics theory that is then applied in advanced analytics classes spanning several analytics disciplines and specialities. Foundation courses support the theoretical, strategic, technical, and practical skills students need to succeed in more advanced courses and specialized electives.
The curriculum centers around a nine-month capstone project that allows students to apply what they are learning in the classroom to solve real-world business problems. Student teams are paired with local companies and industry research partners to consult on key issues the organization needs to solve. Examples of capstone projects include a credit card fraud detection system, a football ticket dynamic pricing model, and a one-step neural network app that analyzes yoga poses and provides feedback to the user.
Realistic Stimulation Providing Edge to Education
The University of Chicago Master of Science in Analytics (MScA) program offers a comprehensive curriculum where students develop an in-depth understanding of a wide range of core as well as emerging analytics methodologies such as automation and artificial intelligence (AI). The MScA program provides students with many opportunities to practice analytics through realistic simulations and by using the most advanced tools, such as Hadoop and Spark, on different cloud-based platforms and University servers. Students learn to apply advanced analytics tools to solve real-life problems in elective courses and through a nine-month capstone project designed to address a business problem at a partnering company. Moreover, the program offers a wide array of opportunities to develop interdisciplinary skills reflecting the inseparable relationship between business, analytics, and multilingual computation. Specializations in coursework address the emerging reality that analytics is evolving differently across industries, for different types of data, and for different kinds of business problems.
Offering Real-World Exposure
Each class presents the opportunity to analyze complex datasets and formulate and solve real-world problems to facilitate data-driven decisions. Throughout the program, students learn how to use common algorithms such as association and sequence rules discovery, memory-based reasoning, clustering, classification and regression decision trees, logistic models, and neural network models. Furthermore, students gain hands-on experience with R, Python, and SQL for data mining, analytics, and data science. Students also learn to use innovative technologies—such as MapReduce, Hadoop, Hive HQL, Spark, Storm, Kafka, TensorFlow, H2O, and others—to ensure they remain at the leading-edge of data science and can address the most challenging questions facing the world today.
Preparing Students for Industry Transformation
The University of Chicago recognizes that the field of analytics is changing so quickly that it can be difficult for programs to transform their curriculum to educate students on the state-of-art technologies and on the ways these technologies could be used to solve practical problems in impactful ways. As a result, companies are having difficulty in finding qualified analytics professionals who can lead analytics initiatives from day one, without a long training process. The Master of Science in Analytics program at the University of Chicago prides itself in being at the forefront of teaching the state-of-art analytical tools with rigor, so that students learn to use analytics creatively and flexibly, with the ability to apply analytics in solving practical problems effectively.
Delivering Insightful Leaders to the Industry
The University of Chicago faculty and alumni contribute to society as scholarly educators, scientists, political leaders, medical professionals, business and community leaders, entrepreneurs, and more. Alumni and faculty, lecturers and postdocs go on to become Nobel laureates, CEOs, university presidents, attorney general, literary giants, and astronauts. The MScA program strives to produce alumni who understand the business, know a broad set of analytical tools, and can manage cross-functional teams.
Specialization is Imperative
The program views analytics a dynamic field that requires analytics professionals to have a strong foundation in data science as well as expertise in specific, functional areas of a business (e.g., marketing, finance, or AI). Professionals with an in-depth knowledge of a particular business will enjoy lucrative career prospects; so, specialization in certain types of analytics will be necessary for many individuals to succeed.
Future Industry Insights
Commenting on the future of Big Data and AI industry, program instructors highlighted the below key trends to drive the growth:
- Use of a framework or platform to deliver production quality machine learning applications, which streamlines the analytics workflow, starting with data ingestion and ending with production deployment.
- Use of methodologies for transparency into analytic models via explainability and interpretability, which are key to the adoption of machine learning applications in business.