Top of page
Skip to main content
Main content

Tech+Data

Data science students often pursue careers in software, artificial intelligence, machine learning, and data engineering, building and maintaining the systems that power modern technology. Increasingly, technology is relevant across nearly all fields, so there are many ways to pursue your interest in tech while at Emory. 

Here are courses offered at Emory that would be relevant to this career path:

  • Data Science (DATASCI)
    • DATASCI 150 – Introduction to Statistical Computing I: This course is an introduction to the R programming language. It will cover the programming basics of R: data types, controlling flow using loops/conditionals, and writing functions. In addition to these basics, this course will emphasize skills that are relevant for data analysis.
    • DATASCI 151 – Introduction to Statistical Computing II: The purpose of this course is to prepare students for upper-level, data analysis-related courses. This course emphasizes on skills that are relevant for data analysis which include 1) data manipulation such as merging, appending, and reshaping data, and 2) making plots for descriptive analysis.
    • DATASCI 340 – Approaches to Data Science with Text: Teaches common theories & techniques in data science using Python. Focus is text analysis (e.g., text parsing, language models, sequence estimation, vector space models & distributional semantics, cluster analysis, supervised learning). Cloud computing, big data, & data visualization are discussed.
    • DATASCI 347 – Machine Learning I: Introduces students to the field of machine learning, an essential toolset for making sense of the vast and complex data sets that have emerged in the past 20 years. Presents modeling/prediction techniques that are staples in the fields of machine learning, artificial intelligence, and data science.
    • DATASCI 350 – Data Science Computing: This course emphasizes programming for data science, rather than programming for the sake of programming. Students learn essential computer literacy (e.g. shell commands), computing concepts & workflow for reproducible research. Students primarily write Python code and use cloud computing resources.
    • DATASCI 447 – Statistical Machine Learning 2: Classical decision models rely on strong distributional assumptions about uncertain events; these topics are covered in QTM 347. QTM 447 covers advanced machine learning methods for modeling the of interplay between data, personalization, and decision optimization in the face of uncertainty.
  • Computer Science (CS)
    • CS 170 – Introduction to Computer Science I : An introduction to Computer Science for students expecting to utilize serious computing in coursework, research, or employment. Emphasis is on computing concepts, programming principles, algorithm development and basic data structures, using the Java programming language and Unix operating system.
    • CS 171 – Introduction to Computer Science II : A second course in Computer Science, focusing on intermediate programming. Emphasis is on proficiency in the use and implementation of data structures, algorithms for classical programming paradigms, and object oriented design and programming with Java.
    • CS 224 – Foundations of Comp.Science: An introductory course in the theory of Computer Science, focusing on analysis of discrete structures with applications. Emphasis is on developing familiarity with notation, computational acuity and creative problem solving skills.
    • CS 253 – Data Structures and Algorithms: A third course in Computer Science, focusing on advanced programming. Emphasis is on mastery in the use and implementation of data structures and algorithms for classical programming paradigms, using the Java programming language and object oriented design.
    • CS 255 – Comp.Arch./Machine Level Prog.: Introductory systems course in Computer Science, with a focus on high level computer architecture and assembler programming. Emphasis is on comprehension of von Neumann computer architecture, information encoding and data representation, and assembler equivalents of high level programming constructs.
    • CS 312 – Computing, AI, Ethics, and Soc: Understanding ethical and societal concerns introduced by computing and AI into human life, including privacy, online influence and disinformation, information ownership and responsibility, fairness and bias in computer and AI technologies such as facial recognition and robotic systems
    • CS 325 – Artificial Intelligence: Foundations and problems of machine intelligence, application areas, representation of knowledge, constraint processing, AI programming languages, expert systems, design of an intelligent system.
    • CS 334 – Machine Learning: This course will cover the underpinnings, algorithms, and practices that enable a computer to learn. Emphasis will be on fundamental theory and algorithms in statistical machine learning, and approaches to applying machine learning in a variety of domains. MATH 321) or equivalent transfer credit as prerequisite.
    • CS 350 – Systems Programming: System programming topics are illustrated by the POSIX API to the Linux operating system. Topics include: file i/o, the TTY driver, window systems, processes, shared memory, message passing, semaphores, signals, and interrupt handlers.
    • CS 377 – Database Systems: Introduction to storage hierarchies, database models, consistency, reliability, and security issues. Query languages and their implementations, efficiency considerations, and compression and encoding techniques.
    • CS 453 – Computer Security: Understanding offense is key to better cyberdefense. We focus on advanced vulnerabilities, exploits and defense technologies. We teach the hacker mindset, ethics as well as C and assembly.
  • Mathematics (MATH)
    • MATH 170 – Intro.Scientific Computing: The course introduces Python for Scientific Computing for students who will likely use it in upper-level courses. Students will know how to algorithmically formulate a mathematical problem, solve simple scientific computing problems, visualize data, and consider different programming paradigms.
  • Physics (PHYS)
    • PHYS 463 – Quantum Computing&Information: An introduction to qubits, quantum gates, quantum circuits, quantum key distribution, quantum teleportation, quantum dense coding, Grover's search algorithm, Shor's factoring algorithm, quantum entanglement and Bell's theorem, and quantum error correction.
  • Biology (BIOL)
    • BIOL 465 – RNA and Biotechnology: The purpose of this course is to introduce students (upper level undergraduate) to the fundamental concepts of RNA biology and to state-of-the-art biotechnologies that use RNA for medical and industrial applications.
  • Environmental Sciences (ENVS)
    • ENVS 250 – Fundam. of Cartography & GIS: Explores the study and design of maps and geographic information systems (GIS) as a problem-solving tool for geographic analysis with focus on applications of GIS, data collection and processing, cartographic design, and trends in geospatial technology.
  • Music (MUS)
    • MUS 347 – Elec Music/Midi Technology: Techniques and principles of electronic music and computer applications in music.

TBA

  • Akamai Technologies
  • Analog Devices
  • Amazon
  • Amazon Web Services
  • AT&T
  • Cox Automotive
  • Databricks
  • Deloitte
  • Epic
  • EY
  • Flexport
  • Google
  • IBM
  • LG Electronics North America
  • Meta
  • Microsoft
  • Oak Ridge National Laboratory
  • Palantir Technologies
  • Palo Alto Networks
  • PwC
  • Raytheon Technologies
  • Salesforce
  • Sandia National Laboratories
  • SAP SuccessFactors
  • Tesla
  • Verint

  • Ruoxuan Xiong
  • Lauren Klein
  • Weihua An
  • Peter Sentz

  • Neal Pawar
  • Rubina Ohanian
  • Richard Pocklington
  • Savneet Singh