Data Science Courses

Developed by faculty from Cornell University’s SC Johnson College of Business, these certificate programs are a must for anyone seeking to make sense of organizational data, develop processes for managing data and use data to inform key business decisions. The courses are rich with opportunities to practice techniques using both your own company’s existing data or a sample data set.

  • Individual Ethics (CIS591)

  • Understanding Data Analytics (CEEM581)

  • Finding Patterns in Data Using Association Rules, PCA, and Factor Analysis (CEEM582)

  • Finding Patterns in Data Using Cluster and Hotspot Analysis (CEEM583)

  • Regression Analysis and Discrete Choice Models (CEEM584)

  • Supervised Learning Techniques (CEEM585)

  • Neural Networks and Machine Learning (CEEM586)

  • Making Data-Driven Recommendations Using Optimization (CEEM587)

  • Making Predictions Using Simulation (CEEM588)

  • Ethics and the Data Lifecycle (CIS592)

  • Integrating Virtue Ethics and Data Science Practice (CIS593)

  • Creating an Ethical Data Science Practice and Workplace (CIS594)

  • Classifying Data With Logistic Regression (CIS449)

  • Stakeholder Presentations (CIS450)

  • Exploring Data (JCB561)

  • Integrating Data From Multiple Tables (JCB562)

  • Extracting Insights from Data (JCB563)

  • Fundamentals of Database Design (JCB564)

  • Introduction to Scalability and Automation (JCB565)

  • Understanding and Visualizing Data (SHA571)

  • Implementing Scientific Decision Making (SHA572)

  • Using Predictive Data Analysis (SHA573)

  • Modeling Uncertainty and Risk (SHA574)

  • Optimization and Modeling Simultaneous Decisions (SHA575)

  • Getting Started with Spreadsheet Modeling and Business Analytics (DYS541)

  • Harvesting Spreadsheet Data (DYS542)

  • Visualizing and Communicating Insights Using Excel (DYS543)

  • Making Predictions and Forecasts with Data (DYS544)

  • Using Prescriptive Analytics in Excel (DYS545)

  • Creating and Sharing Interactive Data Models (DYS546)

  • Presenting Quantitative Data (CORE521)

  • Descriptive Statistics for Business (CORE522)

  • Making Predictions Using Statistical Probability (CORE523)

  • Inferential Statistics (CORE524)

  • Multivariable Comparisons (CORE525)

  • Statistical Forecasting (CORE526)

  • Practical Applications of Statistics (CORE527)

  • Predictive Analytics in R (SHA576)

  • Clustering, Classification and Machine Learning in R (SHA577)

  • Prescriptive Analytics in R (SHA578)

  • Creating Data Visualizations with Tableau (CAC113)

  • Enhancing Data Visualizations with Tableau (CAC114)

  • Telling a Data-Driven Story with Tableau (CAC115)

  • Planning and Delivering Effective Presentations (LSM588)

  • Problem Solving Using Evidence and Critical Thinking (LSM401)

  • Strategic Decision Making (LSM582)

  • Building Compelling Slide Decks and Reports (LSM701)

  • Constructing Expressions in Python (CAC101)

  • Writing Custom Python Functions, Classes and Workflows (CAC102)

  • Developing Data Science Applications (CAC103)

  • Creating Data Arrays and Tables in Python (CAC104)

  • Organizing Data with Python (CAC105)

  • Analyzing and Visualizing Data with Python (CAC106)

  • Building Predictive Machine Learning Models (CAC107)

  • Interpreting and Communicating Data (ILR511)

  • Using Statistical Tests to Make Decisions (ILR512)

  • Applying Statistical Tests (ILR513)

  • Making Predictions With Data Models (ILR514)

  • Querying Relational Databases (CAC111)

  • Working with Data Using SQL (CAC112)

  • Exploring Data Sets With R (CIS445)

  • Summarizing and Visualizing Data (CIS446)

  • Measuring Relationships and Uncertainty (CIS447)

  • Data Cleaning With the Tidyverse (CIS448)