Analytics

The Master of Science in Analytics at Northwestern University is a comprehensive, interdisciplinary program designed to equip students with the advanced skills and knowledge necessary to excel in the rapidly evolving field of data analytics. This program combines theoretical foundations with practical applications, ensuring graduates are well-prepared to tackle complex data-driven challenges across diverse industries. Students will engage with a rigorous curriculum that covers core topics such as statistical analysis, machine learning, data mining, and data management, fostering a deep understanding of how to extract insights from large and complex datasets. The program emphasizes both technical proficiency and strategic thinking, enabling students to translate analytical results into actionable business decisions. Through hands-on projects, case studies, and collaborations with industry partners, learners gain valuable experience in deploying analytical solutions in real-world scenarios. The curriculum also includes training in programming languages like Python and R, as well as tools such as SQL, Tableau, and cloud computing platforms, ensuring that graduates are proficient in the latest technologies used in the analytics profession. Northwestern’s strong ties to industry leaders and its location in the Chicago metropolitan area provide students with numerous opportunities for internships, networking, and employment after graduation. The program is suitable for recent graduates with quantitative backgrounds seeking to specialize in analytics and for professionals aiming to enhance their data skills for career advancement. With a flexible schedule that often includes evening and weekend classes, the Master of Science in Analytics at Northwestern University is committed to fostering a diverse and inclusive community of learners passionate about harnessing the power of data to drive innovation and solve complex problems.

Many programs focus on just one aspect of analytics, producing graduates who are trained primarily in either modeling or data mining. The MSiA program provides students with a rigorous course of study in all three areas of analytics: predictive, prescriptive, and descriptive. Coursework in statistics, modeling, operations research, quantitative analysis, decision analysis, databases, and data management form the core of the curriculum. Each course builds upon previous courses and is complemented by case studies, team projects, and guest speakers from industry, ensuring an academic experience that’s grounded in business reality. This comprehensive curriculum provides a broader perspective and teaches the skills necessary to effectively solve business challenges. 

Students leave the program with the ability to lead via our three-pronged approach:

• Business: Students integrate strong business acumen, communication skills, and management ability into their work.
• IT: Students train in Java, Python, and R, preparing them for high-tech roles.
• Data science: Students explore all three areas of data analysis—predictive (forecasting), descriptive (business intelligence and data mining), and prescriptive (optimization and simulation), using industry-leading tools such as SAS, SQL, Hadoop, Pig, Hive, Mahout, Tableau, D3.

Courses

• ENTREP 495: NUVENTION ANALYTICS
• MEM 415: COMPUTER SIMULATION FOR RISK AND OPERATIONS ANALYSIS
• MSIA 400: ANALYTICS FOR COMPETITIVE ADVANTAGE
• MSIA 401: PREDICTIVE ANALYTICS I
• MSIA 410: ANALYTICAL CONSULTING PROJECT LEADERSHIP
• MSIA 411: DATA VISUALIZATION
• MSIA 412: LEADERSHIP FOR ANALYTICAL ORGANIZATIONS AND FUNCTIONS
• MSIA 413: DATABASES RETRIEVAL
• MSIA 420: PREDICTIVE ANALYTICS II
• MSIA 421: DATA MINING
• MSIA 422: INTRO TO JAVA & PYTHON PROGRAMMING
• MSIA 430: BIG DATA AND BUSINESS INTELLIGENCE
• MSIA 431: ANALYTICS FOR BIG DATA
• MSIA 440: OPTIMIZATION AND HEURISTICS
• MSIA 489: INDUSTRY PRACTICUM
• MSIA 490-20: TEXT ANALYTICS
• MSIA 490-21: PREDICTIVE MODELS FOR CREDIT RISK MANAGEMENT
• MSIA 490-23: HEALTH ANALYTICS AND DECISION MAKING
• MSIA 490-27: SOCIAL NETWORKS ANALYSIS
• MSIA 490-29: DEEP LEARNING
• MSIA 499: CAPSTONE DESIGN

Requirements

• A minimum of 3.0 Grade Point Average on a 4.0 scale in undergraduate work
• Transcripts from all undergraduate and graduate education attempted
• $75 application fee payable in US funds
• Three letters of recommendation that attest to the candidate's work performance and management potential
• TOEFL or IELTS scores for international applicants who have not had English as their primary language of instruction
• Unofficial GMAT or GRE scores
• Candidate Video (optional)

Scholarships

The Master of Science in Analytics offers a limited number of fellowships each year covering 50 percent of the tuition expenses. They are awarded during the application process to select students and are based on merit. No additional applications are required for consideration. 

The Master of Science in Analytics at Northwestern University is a highly regarded graduate program designed to prepare students for careers in data analytics, data science, and related fields. This program offers a comprehensive curriculum that combines theoretical foundations with practical applications, equipping students with the skills needed to analyze complex data sets, develop predictive models, and communicate insights effectively. The program is delivered through a blended format, including rigorous coursework, team-based projects, and opportunities for experiential learning. Students gain expertise in statistical analysis, machine learning, data management, programming, and visualization tools such as R, Python, SQL, and Tableau. Northwestern’s program emphasizes real-world problem-solving, with partnerships and collaborations across various industries, including finance, healthcare, technology, and consulting. The curriculum is crafted to be flexible, allowing students to tailor their studies toward specific interests within analytics, such as marketing analytics, operations analytics, or financial analytics. Graduates of the program are well-prepared for roles such as data analyst, data scientist, business analyst, or analytics consultant. The program also provides robust career support through Northwestern's career services, networking events, and connections with top employers. The faculty includes distinguished experts in data analytics, statistics, and computer science, ensuring students receive instruction from leaders in the field. Admission requirements typically include a strong quantitative background, such as prior coursework in mathematics or statistics, along with relevant work experience is beneficial. The program duration is generally one to two years for full-time students. Northwestern University’s analytical program is recognized for its interdisciplinary approach, combining insights from business, technology, and social sciences to address diverse data challenges. This program is ideal for students seeking to develop a deep understanding of analytics techniques and their applications across various domains, ultimately enabling them to solve data-driven problems and make strategic decisions in their organizations.