Executive Masters Of Business Administration
The Executive Masters of Business Administration (EMBA) at RIT is desi...
B.S. in Applied Statistics and Data Analytics
Rochester
INTAKE: Jan, May & Aug
Program Overview
The B.S. in Applied Statistics and Data Analytics at Rochester Institute of Technology (RIT) is designed to equip students with the skills and knowledge needed to succeed in the rapidly growing field of data analytics. The program combines a strong foundation in statistical theory, data analysis techniques, and practical application to prepare students for diverse career opportunities in industries such as healthcare, finance, technology, and government. Students gain proficiency in statistical software, programming, and data visualization, ensuring they can analyze and interpret complex data sets effectively.
Curriculum: The curriculum of the B.S. in Applied Statistics and Data Analytics at RIT blends theoretical and practical learning, providing students with a well-rounded education. Core courses include Probability and Statistics, Data Mining, Statistical Computing, Regression Analysis, and Multivariate Statistics. Students also explore specialized topics like machine learning, time series analysis, and big data analytics. Additionally, the program offers a hands-on learning experience with internships and collaborative projects, where students can apply their knowledge in real-world scenarios. This comprehensive curriculum ensures that graduates are ready to tackle the challenges of data analytics across various sectors.
Research Focus: RIT’s B.S. in Applied Statistics and Data Analytics emphasizes research that bridges academic knowledge with practical industry applications. Students engage in cutting-edge research projects that explore innovative ways to use data for solving real-world problems. The program encourages students to participate in research initiatives focusing on areas such as predictive modeling, artificial intelligence, and the development of new data analysis techniques. Faculty members are actively involved in research, offering students the opportunity to work alongside experts in the field and contribute to impactful studies in analytics.
Industry Engagement: RIT has a strong reputation for fostering industry connections, and the B.S. in Applied Statistics and Data Analytics program is no exception. The program includes opportunities for students to collaborate with industry partners through internships, cooperative education programs, and capstone projects. These partnerships with leading organizations provide students with valuable networking opportunities, exposure to industry standards, and hands-on experience working with large datasets. This industry engagement ensures that graduates are not only academically prepared but also have the practical experience sought after by employers.
Global Perspective: RIT’s B.S. in Applied Statistics and Data Analytics provides students with a global perspective on data analytics. The program encourages an understanding of how data analysis is applied across different cultures, industries, and geographical regions. Through international internships, collaborative research, and the diverse backgrounds of students and faculty, the program helps students understand the global challenges and opportunities that come with data analytics. This international focus prepares graduates to work in a globalized economy, where data-driven decision-making is essential to success in any industry.
Location
Rochester
Score
IELTS 6.5
Tuition Fee
USD 53720
Undergraduate Entry Requirements
Academic Qualifications: Applicants for undergraduate programs typically require a minimum academic achievement of 70% or above in their previous academic qualifications.
English Language Proficiency:
- IELTS: Overall band score of 6.0 or 6.5 with a minimum of 5.5 in each component.
- TOEFL: Overall score of 79 or higher.
- PTE: Overall score of 58 or higher.
- DET (Duolingo English Test): Minimum score of 100.
Rochester Institute of Technology (RIT) is supporting international students through a variety of scholarship opportunities designed to ease the financial burden of studying abroad. These scholarships are available for undergraduate and graduate students and can be awarded based on academic merit, need, and specific program requirements.
Merit-Based Scholarships: RIT offers several merit-based scholarships for international students that are awarded based on academic achievement. These scholarships are intended to recognize the hard work and dedication of students who have demonstrated exceptional academic performance in their previous studies. These scholarships can significantly reduce tuition fees, making education at RIT more affordable for international students. Applicants are automatically considered for merit-based scholarships during the admissions process, and no additional application is required.
International Student Scholarships: In addition to merit-based scholarships, RIT offers specific scholarships tailored to international students. These scholarships are designed to make education accessible to students from around the world and help them pursue their studies at a world-class institution. The scholarships vary in amount and eligibility criteria, and they are awarded based on factors such as academic performance, leadership potential, and extracurricular involvement.
Program-Specific Scholarships: Certain academic programs at RIT offer program-specific scholarships for international students. These scholarships are designed to support students in specific fields of study, such as engineering, business, computer science, and the arts. Each program may have its own criteria for eligibility, and students are encouraged to check with their respective departments for details about available scholarships. Program-specific scholarships often offer additional opportunities for networking, research, and professional development.
Graduates with a B.S. in Applied Statistics and Data Analytics from Rochester Institute of Technology (RIT) have a wide array of career options in various industries. The demand for data-driven decision-making in sectors like healthcare, finance, technology, marketing, and government has led to a growing need for professionals skilled in statistics and data analytics.
Data Analyst: Analyzing data to help organizations make informed decisions, identify trends, and improve processes. Data analysts typically work with large datasets, using statistical techniques and software to extract meaningful insights.
Statistical Consultant: Providing expertise in data collection, analysis, and interpretation for organizations across different sectors, including healthcare, research, and business consulting.
Business Intelligence Analyst: Using data to help organizations identify business opportunities and improve operational efficiencies. This role involves gathering and analyzing data from various sources to support strategic decision-making.
Data Scientist: A more advanced role where professionals leverage programming, machine learning, and statistical methods to analyze and model complex datasets. Data scientists often create algorithms and predictive models for businesses.
Quantitative Analyst: Often found in financial institutions, quantitative analysts develop mathematical models to analyze financial data and assess risks, market trends, and investment strategies.
Market Research Analyst: Gathering and analyzing data about consumer preferences, market conditions, and competitors to help businesses design marketing strategies and improve their product offerings.
Healthcare Data Analyst: Specializing in healthcare data to improve patient outcomes, optimize healthcare delivery, and manage public health programs using data-driven insights.
Operations Research Analyst: Applying mathematical and statistical methods to help organizations solve problems related to logistics, production, and resource optimization.
Machine Learning Engineer: Designing and implementing algorithms that allow computers to learn from and make decisions based on data, often in areas like artificial intelligence, robotics, and data-driven applications.
