Bachelor’s Degree in Applied Statistics
Welcome to our Bachelor of Arts / Bachelor of Science in Applied Statistics program, where we offer a unique dual pathway to cater to diverse student needs and career aspirations. For those inclined towards a more conceptual understanding of statistics and its applications, our BA program provides a comprehensive curriculum without the rigors of calculus. Conversely, our BS program is designed for those who wish to delve deeper into the mathematical underpinnings of statistical analysis, incorporating advanced calculus-based methods.
As a student in our Applied Statistics program, you will gain an abundance of skills essential for the modern data-driven world. These include proficiency in data analysis, statistical modeling, and the ability to interpret and communicate statistical information effectively. Our program is tailored to ensure you develop both the theoretical understanding and practical skills needed to excel in various industries.
Recent graduates from the Computer Science and Mathematics department have a strong track record of success, landing prestigious positions in the pharmaceutical and financial sectors, among others. Furthermore, quite a few have demonstrated exceptional success in gaining admission to graduate schools of high repute, such as Columbia University, Johns Hopkins University, the University of Pennsylvania, and the University of California, Berkeley. Join us to embark on a journey that not only shapes your future career but also contributes to the ever-evolving field of statistics.
Introduction to Data Science with Python
Gain a comprehensive and critical introduction to data science and analytics techniques using Python. Explore the essential concepts of Python programming. Use Python tools to perform data exploration, cleaning, manipulation, and visualization. Learn basic statistics and machine learning techniques to conduct data analysis.
Introduction to Data Science with R
You will study data science and analytics techniques using R in this introductory course. Discover the essential concepts of R programming. Work with tools and various packages in R to perform data cleaning, processing, and visualization. Examine basic statistics and machine learning techniques to conduct data analysis.
Introduction to Data Mining
Embark on a comprehensive and critical introduction to the key concepts, tasks, and techniques of data mining. Acquire the knowledge needed to manipulate and extract new information from large amounts of data. Explore data preprocessing and feature selection, decision trees, cluster analysis, classification, machine learning, evaluation, and validation, as well as scalability. Work through issues and techniques using practical applications and examples taken from various domains, including biology, computer science, sociology, and economics.
Topics in Statistics
Watch for more details coming about this course.