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 its global campuses, where ideas are born that challenge and change the world.
Graham School under the aegis of the University of Chicago empowers individuals to challenge conventional thinking in pursuit of original ideas. Its Master of Science in Analytics empowers students with advanced analytical training to develop the ability to draw insights from big data and build automated artificial intelligence systems.
About the Distinguished Program
Graham School’s Master of Science in Analytics Program builds a basis in analytics theory that is then applied in advanced analytics classes spanning several analytics disciplines and specialities. The curriculum provides a common set of foundation courses that gives students the essential theoretical, strategic, technical, and practical skills they need to succeed in building impactful professionals.
The Master of Science in Analytics course curriculum is centred on a yearlong capstone project that allows students to apply what they are learning in the classroom to solve real-world business problems. The Capstone Project is a degree requirement for students and is designed in a way that both students and capstone partners gain experience working on real-life data- and analytics- related problems. During the tenure of the project, 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, an NFL ticket dynamic pricing model, and a one-step neural network app that analyses yoga poses and provides feedback to the user.
Learning through Practical Exposure
In addition to the capstone project, each class of the Master of Science in Analytics course gives students an opportunity to analyse complex datasets and formulate and solve real-world problems to facilitate data-driven decisions. The 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, throughout the program. In addition, students gain hands-on experience with the four primary programing languages for data mining, analytics, and data science: R, Python, SAS, and SQL.
Additionally, Graham School’s Master of Science in Analytics Program gives the students an opportunity to learn to use innovative technologies such as MapReduce, Hadoop, Hive HQL, Spark, Storm, Kafka, TensorFlow, H2O, and others. The pedagogy ensures that the students remain at the leading edge of data science and are empowered to address the most challenging questions faced by the world today.
A Passionate Leader
Sema Barlas is the Senior Academic Director and the Founder of the Master of Science in Analytics Program at Graham School. She holds a PhD in research methodology and quantitative psychology from the University of Chicago. Sema’s expertise lie in the areas of applied marketing models, statistics, research methodology, and consumer behaviour. She has published papers in peer-reviewed journals with over 500 citations. Prior to joining the Analytics program at the University of Chicago, Sema taught at Argosy University in Chicago, McGill University in Canada, and the University of Groningen in the Netherlands. She has also worked in the industry for several years as an analytics professional at companies that include Sears, Roebuck, and Co. and Experian.
Delivering Industry Ready Analytics Courses
Graham School’s Master of Science in Analytics (MScA) differentiates itself from other similar programs as it offers a comprehensive curriculum where students develop an in-depth understanding of a wide range of core as well as emerging analytics methodologies like automation and artificial intelligence (AI). The 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 a variety of different cloud-based platforms as well as University servers. Students studying at Master of Science in Analytics (MScA) at Graham School are provided with a wide array of opportunities to develop interdisciplinary skills reflecting the inseparable relationship between business, analytics, and multilingual computations.
The Master of Science in Analytics (MScA) at Graham School offers a rich set of specializations, reflecting the emerging reality that analytics is evolving differently in different industries, for different types of data, and for different kinds of business problems.
Transforming Data Science Education
Sema believes that the field of analytics is changing so quickly that it is difficult for programs to transform their curriculum to educate students on the state-of-art technologies and the ways these technologies can be used to solve practical problems in impactful ways. As a result, companies face a difficulty to find qualified analytics professionals who can lead analytics initiatives from day one, without a long training process. The Master of Science in Analytics program at Graham School prides itself in being at the forefront of teaching the state-of-art analytical tools with rigor. The school equips students with the necessary skills to use analytics creatively and flexibly and apply analytical applications to solve practical problems effectively.
The MScA program at Graham School attracts a large number of students with excellent academic and professional credentials. The program started with only 30 students at the inception on January 2014 and today, serves to 300 highly diverse groups of students and 258 alumni. Over 98% of the students who start the MScA program successfully complete it. A recent survey revealed that nearly 100% of MScA alumni are employed, generally by reputable companies, and in many cases, at senior levels. Also, it should be noted that students start enjoying better career outcomes right after they start the program; thus far, the MScA involvement has contributed significantly to the 33% increase in annual salaries generally and to the more than 50% increase in salaries of the graduates.
Overcoming Barriers in the Big Data Industry
Graham School ascertains that there are at least five major challenges faced by industries attempting to compete using analytics, including:
• Insufficient number of qualified analytics professionals.
• Lack of data quality that pertains among different units in many companies who compile data from a variety of different sources and do not always have an enterprise-level database with common standards applied across these different units.
• Data security issues which is becoming increasingly more challenging for organizations who aim to secure their data and protect the privacy of data sources. The risk factors for data breaches are damaging the reputation of companies and causing harm to customers. It is widely expected that companies will soon face potentially costly compliance requirements for emerging new regulations.
• Many organizations still lack an analytics culture that is necessary for them to enjoy the benefits of the analytics fully. The return-on-investment in analytics will remain limited until everyone in an organization understands analytics at some level, believes in its value, and is able to think in analytical terms.
• Ethics of algorithms is emerging as a new field of concern. Many companies may suffer from reputation or other kinds of risks until guidelines for ethical use of analytics are clearly stated.
Sema asserts that different set of advanced skills will be required of analytics professionals in different functional areas of business (e.g., marketing, finance, or AI) or professionals working with different types of data (internet of things, text, video). Professionals with in-depth knowledge of a particular business and related analytics will enjoy lucrative career prospects; so, specialization in certain types of analytics will be necessary for many individuals to succeed. For instance, experts who focus on addressing data quality issues by using automation and AI will be most sought after in the employment circles. At the same time, there will be increasing demand for professionals who understand the business, know a broad set of analytical tools, and thus, can manage cross-functional teams.