Data Science and Engineering (DSE) Certificate
Data science analyzes big and complex data and extracts knowledge and insight for use in a broad range of applications. The goal is to create data centric solutions to scientific, social, or business questions. Data science deals with data collection, storage, integration, analysis, modeling, inference, communication, and ethics. It involves obtaining, wrangling, curating, managing, processing, and exploring data. It defines questions, analyzes data, and communicates the results.
DSE certificate aims at training interested Bilkent University undergraduate students with data science skills and getting them ready for taking data science related jobs. Students taking the required number of courses from three course pools and getting a letter grade B or better from each course are awarded with the DSE certificate.
Students awarded with the certificate will be capable of making good judgments and decisions in problems involving large data sets and use appropriate tools effectively to draw key conclusions. They will become competent in
- collecting and preparing data for analysis,
- setting up, operating, and managing big data systems,
- doing and coordinating the data analysis, statistical modeling, computational modeling, and machine learning,
- solving data-driven problems with appropriate algorithmic approach and software,
- data visualization and outputs of data analysis,
- supporting data driven decision making, uncovering the stories buried in data.
The coursework required for the certificate span mathematical, computational, and statistical foundations of data analytics, data management and curation, data description and visualization, data modeling and assessment, workflow and reproducibility, communication and teamwork, domain specific considerations, and awareness of ethical problems.
Application for the Certificate
The students who satisfy the requirements of DSE Certificate should fill out the following application letter and submit it to his/her department.
DSE Certificate Application Letter (.docx)
DSE Certificate Application Letter in English (.docx)
The Certificate Courses
To get the certificate, a student must complete a total of at least six courses from the lists below with the indicated number of courses from each of three sets, with a grade of B or better from all six courses.
Data Science and Engineering Certificate Sample
Set 1: General Foundations for Data Science(One or two courses) |
| CS 281 Computer and Data Organization
CS 353 Database Systems CS 426 Parallel Computing CS 471 Numerical Methods CS 473 Algorithms I EEE 361 Linear Algebra in Data Analysis and Machine Learning EEE 424 Digital Signal Processing EEE 533 Random Processes IE 411 Introduction to Nonlinear Optimization IE 421 Introduction to Stochastic Processes MATH 260 Introduction to Statistics ME 361 Numerical Methods for Engineers |
Set 1 includes courses that provide mathematical, programming and data systems foundations and computational thinking principles for data science.
Set 2: Statistical, Computational, and Algorithmic Foundations, Models, Tools and Techniques of Data Analysis
|
| GE 461 Introduction to Data Science
CS 433 Information Retrieval Systems CS 461 Artificial Intelligence CS 464 Introduction to Machine Learning CS 478 Computational Geometry EEE 443 Neural Networks EEE 448 Reinforcement Learning and Dynamic Programming EEE 485 Statistical Learning and Data Analytics IE 451 Applied Data Analysis IE 452 Algebraic and Geometric Methods in Data Analysis IE 456 Reinforcement Learning and Dynamic Programming IE 553 Applied Statistical Modeling and Data Analysis IE 586 Computational Optimization MATH 465 Mathematical Foundations of Data Science |
Set 2 includes courses about foundations of data analysis and analytics, statistical and mathematical models, tools, and computational techniques for data science.
Set 3: Applications and Advanced Topics in Data Science
|
| CS 425 Algorithms for Web-scale Data
CS 429 Dynamic and Social Network Analysis CS 443 Cloud Computing CS 477 Biometrics CS 481 Bioinformatics Algorithms CS 483 Natural Language Processing CS 484 Introduction to Computer Vision CS 485 Deep Generative Networks CS 550 Machine Learning CS 551 Pattern Recognition CS 553 Intelligent Data Analysis CS 554 Computer Vision CS 558 Data Mining CS 559 Deep Learning EEE 482 Computational Neuroscience EEE 486 Statistical Foundations of Natural Language Processing EEE 525 Advanced Signal Processing IE 468 Pricing and Revenue Optimization IE 469 Industrial Applications of Operations Research MAN 456 Business Analytics |
Set 3 includes courses about applications, different domains, and more advances topics related with data science.