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.
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.
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
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
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 Multivariate Analysis
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.
1 The above information is drawn from a sampling of degree programs.↑