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Course Details

Data Science Pathways Overview:

  • This course is comprised of 10 blocks, with a pathway to credit at the end of each block.
  • Even if your goal is not earning a certification, this course is a great way to brush up on your existing data science skills. Try Block 1 and see what you think! 
  • Blocks can be taken in any order, although sequential order is highly recommend.
  • All 10 blocks must be completed to earn the Texas State University System Professional Certificate.
  • Upon completion, working age Texans with at least 60 hours of college credit, or professionals with on-the-job experience, can take a comprehensive DASCA exam to earn an additional certification from DASCA. It is the Professional ‘ABDA™’ Certification (Associate Big Data Analyst Certification™) (not required)
  • There are potential pathways to credits in many academic programs at participating universities.    
  • For additional questions, check out our FAQs.

 

 

Course Structure

A diagram detailing the course structure of the Data Science Pathways course

 

Curriculum Breakdown By Block

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  • Language:
    Excel & R

    Faculty Members:
    Dr. Tahir Ekin, CIS & QM  (Initial Cohort, Sept. 5)
    Dr. Barbara Hewitt, HIM  (Initial Cohort, Nov. 7)
    Dr. Li-Jen Lester, Computer Science  (Initial Cohort, Jan. 9)
    Dr. Amar Rasheed, Computer Science

    Course Description:
    This course provides an explanation of the big picture goals of the certificate and explains how the modules prepare one to achieve those objectives. It identifies the roles, functions, applications and importance of data science. It defines data types and describes data analytics frameworks. It covers big data analytics with a discussion of the curse of dimensionality and cloud-based systems. It concludes with an overview of societal and ethical implications of big data analytics, and the role of privacy and transparency in analytics. 

    Recommended:
    This intro block has no prerequisites. 

  • Language:
    Excel & R

    Faculty Members:
    Dr. Francis Méndez, CIS & QM  (Initial Cohort, Nov. 7)
    Dr. Alex White, Mathematics  (Initial Cohort, Jan. 9)
    Dr. Melinda Holt, Statistics

    Course Description:
    For higher-level courses in data science, it is convenient to review basic mathematical concepts. The objective of this learning block is to build an intuitive understanding of the underlying mathematics of data science. The zero module covers a basic introduction to R language. The first module shows the mathematics of optimization. It introduces calculus techniques and the use of matrices. Matrices are discussed in detail in the linear algebra modules. The second module introduces linear algebra and how it relates to data science. The mathematics of vectors and matrices are discussed. The third module introduces the mathematics of probability theory and how it relates to data science. The fundamental concept of probability distribution is discussed. The fourth module demonstrates how calculus, matrix algebra and probability are used in typical data science applications. 

    Recommended:
    Functional knowledge of all previous blocks.

  • Language:
    Excel & R

    Faculty Members:
    Dr. Fereshteh Zihagh, Marketing  (Initial Cohort, Jan. 9)
    Dr. Diane Dolezel, HIM
    Dr. Vung Pham, Computer Science

    Course Description:
    This course prepares individuals to organize and derive meaning from data by using visual presentation tools and techniques. This course includes topics related to data visualization theory, visual designs, evaluations of visual designs, and visualization application programming. 

    Recommended:
    Functional knowledge of all previous blocks.

  • Language:
    R

    Faculty Members:
    Dr. Emily Zhu, CIS & QM
    Dr. Vera Loudina, Mathematics

    Dr. Di Gao, Mathematics
    Dr. DooYoung Kim, Statistics

    Course Description:

    This course covers basic concepts and methods of descriptive and inferential statistics for data science. Topics include measures of central tendency and dispersion, probability distributions, sampling distributions, confidence intervals, hypothesis testing. The fundamental concepts of correlation, simple and multiple regression are discussed. The optional module provides basic ideas of Bayesian statistics with the real-world examples. All modules are designed to teach students how to perform statistical analysis using an open-source statistical language R within RStudio.

    Recommended:
    Functional knowledge of all previous blocks.

  • Language:
    R

    Faculty Members:
    Dr. Valles-Molina, Electrical Engineering
    Dr. Dinçer Konur, CIS & QM
    Dr. Min Kyung An, Computer Science

    Course Description:  

    This predictive analysis block presents the key concepts, techniques, and code approaches for predictive modeling and visualization analysis. Students will be tasked to discuss, employ, apply, assess, and explain predictive-related topics, such as linear regression, time-series, ETC, ARIMA, and HTC modeling. The block includes an optional forecasting project module to help students understand the differences between forecasting and prediction analytics.

    Recommended:
    Functional knowledge of all previous blocks.

  • Language:
    Python

    Faculty Members:
    Dr. Tanzima Islam, Computer Science
    Dr. Karpoor Shashidhar, Computer Science
    Dr. Hyuk Cho, Computer Science

    Course Description:
    Machine learning is the art and science of designing algorithms and developing programs that can accomplish specific goals and objectives without explicit instructions. This course is a broad overview of machine learning and will encompass supervised and unsupervised learning including topics from regression, ensemble learning, kNN, kMeans, support vector machines and principal component analysis.

    Recommended:
    Functional knowledge of all previous blocks.

  • Language:
    Python

    Faculty Members:
    Dr. Chul-Ho Lee, Computer Science
    Dr. Xiaoxi Shen, Mathematics
    Dr. Fan Liang, Computer Science

    Course Description:  

    This block covers the fundamental topics of neural networks. Topics include different types of neural networks such as multilayer perceptrons, convolutional neural networks and recurrent neural networks, optimization algorithms and techniques for neural networks, and autoencoders.

    Recommended:
    Functional knowledge of all previous blocks.

  • Language:
    Python

    Faculty Members:
    Dr. Vangelis Metsis, Computer Science
    Dr. Masoud Moradi, Marketing
    Dr. Qingzhong Liu, Computer Science
    Dr. ABM Islam, Computer Science

    Course Description:
    This course covers fundamental topics of computer vision. Topics include elementary image operations and transformations, feature extraction, model fitting, object recognition, classification and tracking, deep learning, camera models and stereo vision.

    Recommended:
    Functional knowledge of all previous blocks.

  • Language:
    Python

    Faculty Members:
    Dr. Apan Qasem, Computer Science
    Dr. Emmanuel Alanis, Finance & Economics
    Dr. Cihan Varol, Computer Science

    Course Description:
    This course covers fundamental topics of big data and natural language processing. Topics include Hadoop, MapReduce, pre-processing techniques, recurrent neural networks, n-gram, and Naïve Bayes.

    Recommended:
    Functional knowledge of all previous blocks.

  • Language:
    Python

    Faculty Members:
    Dr. Byron Gao, Computer Science
    Dr. Damián Valles Molina, Electrical Engineering
    Dr. Bing Zhou, Computer Science

    Course Description:
    This block prepares students with necessary knowledge and skills to conduct industry capstone projects in data analytics. Students will be guided and tasked with solving real-world big data analytical problems, employing practical skills in classification, clustering, frequent pattern mining, and advanced machine learning

    Recommended:
    Functional knowledge of all previous blocks.

The Data Science Pathways course launches in September of 2022!

Data Science Pathways on the Zeus Platform

Initial course launches in September of 2022!

datasciencepathways@txstate.edu