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Bachelor of Science in Statistics - Data Science

Winnipeg

 

INTAKE: Jan, May & Sept

Program Overview

The Bachelor of Science in Statistics with a specialization in Data Science program at the University of Winnipeg provides students with a comprehensive education in statistical methods, computational techniques, and data analysis tools essential for succeeding in the rapidly evolving field of data science. Through foundational courses in statistics, mathematical background, data wrangling and visualization, machine learning and predictive modeling, big data analytics, statistical computing and programming, applied data science projects, and exploration of ethical and legal considerations, students develop the knowledge, skills, and expertise needed to extract insights from data, make informed decisions, and drive innovation in diverse industries. Graduates of the program are well-prepared to pursue careers as data scientists, statisticians, data analysts, or research scientists in academia, government, healthcare, finance, technology, and other sectors where data-driven decision-making is critical.

Curriculum:

  1. Foundation in Statistics: The program begins with foundational courses in statistics, covering topics such as probability theory, statistical inference, regression analysis, and multivariate analysis. Students learn the theoretical underpinnings of statistical methods and their application to real-world data analysis problems. This solid foundation in statistics provides students with the necessary skills to understand and interpret data effectively.

  2. Mathematical Background: Students develop a strong mathematical background through courses in calculus, linear algebra, and mathematical statistics. Mathematical proficiency is crucial for understanding advanced statistical concepts and algorithms used in data science. Students learn to manipulate mathematical expressions, solve optimization problems, and apply mathematical techniques to statistical modeling and analysis.

  3. Data Wrangling and Visualization: The program emphasizes practical skills in data wrangling and visualization. Students learn techniques for cleaning, transforming, and preparing data for analysis. They also learn to use data visualization tools and libraries to create informative and visually appealing graphics that communicate insights from data effectively. Data wrangling and visualization skills are essential for understanding data quality, exploring patterns, and communicating findings to stakeholders.

  4. Machine Learning and Predictive Modeling: Students study machine learning algorithms and predictive modeling techniques used to build models and make predictions from data. Topics may include supervised learning algorithms (e.g., linear regression, decision trees, support vector machines) and unsupervised learning algorithms (e.g., clustering, dimensionality reduction). Students learn how to select appropriate algorithms, train models, evaluate performance, and interpret results.

  5. Big Data Analytics: The program covers principles and techniques for analyzing big data, including distributed computing frameworks, parallel processing, and scalable algorithms. Students learn to work with large and high-dimensional datasets using tools and platforms such as Hadoop, Spark, and MapReduce. They explore strategies for data storage, retrieval, and processing in distributed computing environments.

  6. Statistical Computing and Programming: Students gain proficiency in statistical computing and programming languages commonly used in data science, such as R, Python, and SQL. They learn to write code to implement statistical algorithms, manipulate data structures, and automate data analysis tasks. Proficiency in programming enables students to work efficiently with data, develop custom analytical solutions, and collaborate on data science projects.

  7. Applied Data Science Projects: The program incorporates applied data science projects where students work on real-world datasets and projects under the guidance of faculty mentors or industry partners. Students apply statistical methods, machine learning techniques, and data analysis tools to solve practical problems and address research questions in domains such as business, healthcare, social sciences, and environmental science. Applied data science projects allow students to gain hands-on experience, apply classroom knowledge to real-world challenges, and develop critical thinking and problem-solving skills.

  8. Ethical and Legal Considerations: The program explores ethical and legal considerations in data science, including issues related to data privacy, security, bias, and fairness. Students learn about ethical guidelines, regulations, and best practices for responsible data handling and decision-making. They also examine case studies and discuss ethical dilemmas and implications of data science applications in society.

Pollster Education

Location

Winnipeg

Pollster Education

Score

IELTS: 6.5

Pollster Education

Tuition Fee

CAD 19544

Entry Requirements for Undergraduate Programs

Application Fees : CAD$ 120

Academic Qualifications: Students must have completed their secondary education (12th standard) from a recognized Indian educational board with a minimum overall 75%.  

English Language Proficiency: IELTS (International English Language Testing System) overall 6.5 with no band less than 6 is required, TOEFL (Test of English as a Foreign Language) overall score of 86 with no band less than 20 is required. PTE score of 58 and DET score of 120 is required.

The University of Winnipeg offers a range of scholarships and awards to support international students in their academic pursuits. These scholarships recognize academic excellence, leadership potential, and contributions to the community. 

President's Scholarship for World Leaders: This prestigious scholarship is awarded to international students entering the University of Winnipeg's undergraduate programs. It covers full tuition fees for the duration of the program and includes a living allowance.

Entrance Scholarships: The University of Winnipeg offers several entrance scholarships for international students based on their academic achievements. These scholarships range from partial to full tuition fee waivers and are available for both undergraduate and graduate students.

Manitoba Graduate Scholarships (MGS): The MGS program provides scholarships to international students pursuing a master's or doctoral degree at the University of Winnipeg. These scholarships are awarded based on academic excellence and research potential.

In-Course Scholarships: The University of Winnipeg also offers scholarships for international students who are already enrolled in a program. These scholarships recognize academic achievements and are available at various levels of study.

Indigenous Award of Excellence: This scholarship is specifically available for international students of Indigenous descent. It supports their academic journey at the University of Winnipeg and encourages their cultural engagement and leadership.

Donor-Funded Scholarships: The University of Winnipeg receives generous contributions from donors who establish scholarships for specific criteria. These scholarships may target international students based on their country of origin, field of study, or community involvement.

It's important to note that scholarship availability, criteria, and application deadlines may vary each year. It is recommended that international students visit the official website of the University of Winnipeg and the scholarship-specific pages for the most up-to-date and detailed information on eligibility, application procedures, and deadlines.

The Bachelor of Science in Statistics with a specialization in Data Science program at the University of Winnipeg provides graduates with a wide range of career opportunities in industries such as technology, finance, healthcare, government, and academia. With their expertise in statistical analysis, machine learning, programming, and data visualization, graduates are well-equipped to tackle complex challenges, drive innovation, and make data-driven decisions that drive organizational success. Whether pursuing careers as data scientists, statisticians, machine learning engineers, business analysts, or research scientists, graduates of this program play a vital role in transforming data into actionable insights that drive business growth, inform policy decisions, and advance knowledge in their respective fields.

  1. Data Scientist: Graduates can pursue careers as data scientists, leveraging their expertise in statistical analysis, machine learning, and programming to extract insights from complex datasets. Data scientists work across industries such as technology, finance, healthcare, and e-commerce, applying advanced analytics techniques to solve business challenges, optimize processes, and drive innovation.

  2. Statistician: With a strong foundation in statistics, graduates can work as statisticians in industries such as government, market research, and academia. Statisticians design experiments, analyze survey data, and develop statistical models to address research questions and inform decision-making processes. They play a crucial role in designing surveys, collecting data, and ensuring the accuracy and reliability of statistical results.

  3. Machine Learning Engineer: Graduates with expertise in machine learning techniques can pursue careers as machine learning engineers, building and deploying predictive models for applications such as recommendation systems, natural language processing, and image recognition. Machine learning engineers work for tech companies, startups, or research organizations, developing algorithms that learn from data to make predictions and automate tasks.

  4. Business Analyst: Graduates can work as business analysts, helping organizations interpret and analyze data to identify trends, make strategic decisions, and optimize performance. Business analysts use statistical techniques and data visualization tools to generate actionable insights from data, informing business strategies related to marketing, sales, operations, and customer experience.

  5. Quantitative Analyst (Quant): Graduates with strong quantitative skills may pursue careers as quantitative analysts in the finance industry. Quants develop mathematical models and algorithms to analyze financial markets, assess investment risks, and optimize trading strategies. They work for investment banks, hedge funds, or asset management firms, using statistical methods to evaluate financial instruments and portfolios.

  6. Data Engineer: Graduates can work as data engineers, responsible for designing, building, and maintaining data pipelines and infrastructure to support data-driven applications and analytics platforms. Data engineers work with big data technologies such as Hadoop, Spark, and Kafka to ingest, process, and store large volumes of data efficiently and reliably.

  7. Health Data Analyst: Graduates interested in healthcare can pursue careers as health data analysts, working for hospitals, research institutions, or healthcare companies. Health data analysts analyze electronic health records, clinical trial data, and public health datasets to improve patient outcomes, assess healthcare quality, and inform health policy decisions.

  8. Research Scientist: Graduates can work as research scientists in academic institutions or research organizations, conducting interdisciplinary research that integrates statistical methods with domain-specific knowledge in fields such as social sciences, environmental science, or public health. Research scientists contribute to advancements in knowledge and understanding through empirical research, data analysis, and scientific publications.

  9. Consultant: Graduates may work as data science consultants, providing expertise and advisory services to businesses, government agencies, or non-profit organizations. Data science consultants help clients identify business problems, design analytical solutions, and implement data-driven strategies to achieve their goals. They may work independently or as part of consulting firms, offering customized solutions tailored to client needs.

  10. Entrepreneur: Graduates with entrepreneurial aspirations can start their own data-driven ventures or tech startups, leveraging their skills in data science, statistics, and programming to develop innovative products or services. They may launch startups in areas such as predictive analytics, machine learning applications, or data-driven decision support systems, seeking to address market needs and create value for customers.

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