| Foundation |
ADS 500A ![]() |
3 |
Prerequisite - Probability and Statistics for Data Science |
- Statistical Concepts
- Probability Theory, Random and Multivariate Variables
- Data and Sampling Distributions
- Descriptive Statistics
- Hypothesis Testing
|
|
ADS 500B |
3 |
Prerequisite - Data Science Programming |
- Data Structures and Algorithms
- Introduction to Python and R Programming
- Advanced Python and R Data Analytics Programming
|
|
ADS 501 |
3 |
Foundations of Data Science and Data Ethics |
- Data Science Methods Introductory and Visualization
- Analytics Problem Solving
- Big Data Concepts
- Data Ethics
|
|
ADS 502 |
3 |
Applied Data Mining |
- Data Types and Preprocessing
- Classification Concepts and Techniques
- Cluster Analysis Concepts and Algorithms
- Association Analysis Concepts and Algorithms
- Anomaly Detection
|
| Core |
ADS 503 |
3 |
Applied Predictive Modeling |
- Practical Data Preprocessing with Linear, Nonlinear,
- Tree-based and Rule-Based Models for Regression and
- Classification Problems in Python and R
|
|
ADS 504 |
3 |
Machine Learning and Deep Learning for Data Science |
- Supervised, Unsupervised, Reinforcement Learning
- Neural Networks
- Fundamentals of Deep Learning
|
|
ADS 505 |
3 |
Applied Data Science for Business |
- Applications of Data Science in Marketing and Finance
- SWOT Analysis
- Research Course Topic
- Post-processing Modeling
|
|
ADS 506 |
3 |
Applied Time Series Analysis |
- Time Series Regression and Exploratory Data Analysis
- Seasonality/Stationarity, MARS, ARMA, ARIMA, VAR Models
|
|
ADS 507 |
3 |
Practical Data Engineering |
- SQL
- Spark
- Relational Algebra
- Reading and Writing Tables Database Modeling
|
|
ADS 508 |
3 |
Data Science with Cloud Computing |
- Cloud Computing Foundations
- Amazon Web Services (AWS)
- Distributed Computing
- Working with Cloud Services and Platforms
|
|
ADS 509 |
3 |
Applied Text Mining |
- Parsing and Preprocessing Text
- Topic Modeling and Clustering
- Text Classification
- Sentiment Analysis
|
| Capstone |
ADS 599 |
3 |
Capstone Project |
- Data Acquisition
- Data Cleaning
- Preprocessing
- Feature Engineering
- Data Modeling
- Model Deployment and Interpretation Report
|
