Which Degree Program is Right for You?
Data science, business analytics, predictive analytics, or statistics? If you excel in mathematics, have a high level of quantitative ability, and enjoy working with data sets and solving problems, you can choose from a range of challenging degree and career options.
To help you determine which program best fits your talents and career goals, we have compared some of the key characteristics of our Master of Information and Data Science program with other master’s programs in related fields1.
Information and Data Science
Drawing on insights from the social sciences, computer science, statistics, management, and law, this multidisciplinary curriculum prepares students to use the latest tools and analytical methods to interpret and communicate their findings.
A strong quantitative background, including knowledge of data structures; algorithms and analysis of algorithms; linear algebra; and programming.
R, Python, Hadoop, Map reduce, Apache Spark, NoSQL databases, Cloud Computing, D3, Apache Pig, Tableau, iPython notebooks, github
- Research Design and Application for Data and Analysis
- Statistics for Data Science
- Storing and Retrieving Data
- Applied Machine Learning
- Data Visualization and Communication
- Behind the Data: Humans and Values
- Apply disciplined, creative methods to asking questions and interpreting results
- Retrieve, organize, combine, clean, and store data from multiple sources
- Apply appropriate statistical analysis and machine learning techniques to identify patterns and make predictions
- Design visualizations and effectively communicate findings
- Understand the ethical and legal requirements of data privacy and security
Applying quantitative methods in business modeling and decision-making to improve financial and business performance.
Matrix algebra/linear algebra and a standard 3-course calculus sequence programming.
SQL, software tools (SPSS, SAS), relational database systems
- Modern Statistical Learning Methods
- Statistical Computing and Data Visualization
- Data, Models, and Effective Decisions
- Data Analytics-Driven Dynamic Strategy & Execution
- Data Management
- Analyze large, unstructured datasets
- Translate analyses into decisions that improve business performance
- Present data to decision-makers
Using traditional statistics and machine learning methods to identify patterns and predict future outcomes.
SQL and NoSQL databases, Hadoop, R, Python, and SAS
- Math for Modelers
- Statistical Analysis
- Intro to Predictive Analytics
- Regression and Multi Analysis
- Generalized Linear Models
- Time Series and Forecasting
- Database Systems and Data Prep
- Practical Machine Learning
- Articulate analytics as a core strategy
- Transform data into actionable insights
- Develop statistically sound and robust analytic solutions
- Formulate and manage plans to address business issues
- Evaluate constraints on the use of data
- Assess data structure and data lifecycle
Providing a broad knowledge in a range of statistical application areas.
Matrix algebra/linear algebra and a standard 3-course calculus sequence.
Minitab, R, and SAS
- Applied Statistics
- Applied Multivariate Analysis
- Statistical Consulting
- Theoretical Statistics
- Develop skills in theoretical statistics, data analysis, and statistical computing
- Gain a mathematical foundation in statistical theory, including some probability theory
- Understand the applications to real problems and data
What Sets Data Science and MIDS Apart
As this comparison suggests, data science and the MIDS program take a more comprehensive approach to data analysis. While other analytical degree programs are adapting to the advent of Big Data, the MIDS program is designed from the ground up to focus on the latest tools and approaches to working with data.
As a result, the MIDS program differs from other analytic degree programs through its:
- Focus on working with complex, unstructured, user-generated data sets (i.e., big, messy data)
- Comprehensive, multi-disciplinary curriculum drawn from the social sciences, computer science, statistics, management, and law.
- Coherent integration of the full life cycle of data — from identifying the right questions to retrieving, cleaning, and modeling the data and communicating results.
- Emphasis on the legal and ethical implications of data privacy and security.