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Advancements and Applications in Data Analysis and Statistical Modeling

Exploring Data Science Equips Students for Evidence-Based Decision Making

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Analysis of Large Datasets Using Statistical Models and Machine Learning Algorithms
Analysis of Large Datasets Using Statistical Models and Machine Learning Algorithms

Advancements and Applications in Data Analysis and Statistical Modeling

In the rapidly evolving world of data science, Oberlin College offers a comprehensive Data Science major that equips students with the skills to be evidence-based decision-makers, critical consumers of information, and engaged citizens. This major is not exclusive to Data Science majors, as DATA 113 is recommended or required for ten other majors, two minors, and four other integrative concentrations at Oberlin.

For those considering a Data Science major, potential students are encouraged to start with any combination of DATA 101, DATA 113, and/or CSCI 150. Students with prior programming experience might begin with CSCI 151 instead of CSCI 150. Those with prior statistics studies or strong mathematical maturity may consider starting with DATA 205 instead of DATA 113.

The Data Science major offers three concentrations: Natural Sciences, Social Sciences, or Statistical Theory and Applications. Each concentration tailors the data science curriculum to the specific analytic, theoretical, and applied needs of its domain.

The Natural Sciences Concentration focuses on applications of Data Science in Biology, Biochemistry and Chemistry, Computer Science, Environmental Science, Geosciences, Neuroscience, or Physics. It emphasises modeling, predictive analytics, and machine learning/artificial intelligence techniques relevant for analyzing natural phenomena and scientific data. A strong foundation in computational methods and data-centric computing is also a key component.

The Social Sciences Concentration, on the other hand, explores the interplay between data and society, including social, political, legal, and environmental impacts. It places emphasis on text mining, understanding societal contexts of data and algorithms, and the ethical implications of data use.

The Statistical Theory and Applications Concentration offers a strong grounding in statistical theory and methods, including design of experiments, regression modeling, time series forecasting, survey design, and quality control. It emphasises rigorous data analysis, statistical computing, and application of quantitative methods to real-world data.

Each concentration requires a combination of core competency classes and concentration-specific electives to meet the priorities of their domain. For example, the Natural Sciences Concentration requires courses in Modeling and Predictive Analytics, Machine Learning and AI, and Data-Centric Computing, while the Social Sciences Concentration focuses on courses on Data in Society, Text Mining, Information Presentation, and Social Impact.

The Statistical Theory and Applications Concentration, often housed within a business or statistical science department, combines statistical knowledge with applied analytics. It includes courses such as Quantitative Foundations for Data Science, Data Analysis and Statistical Computing, Survey Design and Sampling, Design of Experiments and Quality Control, Regression and Predictive Analytics, Applied Statistics and Data Science, Time Series and Forecasting Models, and Nonparametric and Categorical Data Analysis.

In essence, each concentration tailors the data science curriculum to the specific analytic, theoretical, and applied needs of its domain: natural sciences focus on computational and predictive methods; social sciences emphasise societal context and impact; and statistical theory concentrates on rigorous statistical methodology and applications.

For more details about major requirements, students should refer to the catalog. Students can also refer to the Data Science Major handout for guidance on satisfying the requirements of the Data Science major across the three concentrations.

Advanced Placement Credit Data Science courses do not have any placement exams. Data Science courses have social, ethical, and legal implications in algorithmic decision-making. Machine learning can be utilised to unlock new knowledge in various fields.

[1] Source: [Graduate Data Science Program](https://www.example.com/graduate-data-science-program) [2] Source: [Data Science in Social Sciences](https://www.example.com/data-science-in-social-sciences) [3] Source: [Statistical Science and Data Analytics](https://www.example.com/statistical-science-and-data-analytics) [4] Source: [Data Science Major Handout](https://www.example.com/data-science-major-handout) [5] Source: [Data in Society](https://www.example.com/data-in-society)

  1. To build a strong foundation in data science, students at Oberlin College can explore technology-driven education and self-development opportunities through the Data Science major, supplementing their knowledge with data-and-cloud-computing courses such as DATA 113, DATA 205, and CSCI 150 or CSCI 151, depending on their background.
  2. For those interested in the Social Sciences Concentration, it offers learning opportunities to understand the interplay between data, society, and the ethical implications of data use. This includes courses on Data in Society, Text Mining, and Information Presentation with a focus on the societal contexts of data and algorithms.

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