WGU MSDA DE vs MSDA DS

[pluggingalong]

Does anyone have any experience with these two tracts? Perfer one over the other?

[bluebooger]

out of 11 courses the first 7 are the same for both

https://www.wgu.edu/online-it-degrees/da...ience.html

https://www.wgu.edu/online-it-degrees/da...ering.html

1: The Data Analytics Journey
2: Data Management
3: Analytics Programming
4: Data Preparation and Exploration
5: Statistical Data Mining
6: Data Storytelling for Varied Audiences
7: Deployment

look at the remaining 4 courses in each and see which you're interested in

for me its no contest
Data Science Specialization all the way

Advanced Analytics

Advanced Analytics extends analytics techniques from machine learning to artificial intelligence more broadly, including topics in neural networks, deep learning, and natural language processing. The course discusses approaches to developing these models including PyTorch and TensorFlow. Students learn to apply a combination of techniques to solve complex business challenges including computer vision and sentiment analysis. There is no prerequisite for this course.

Optimization

Optimization is a large class of business problems requiring the iterative algorithmic maximization or minimization or one or more variables. Students in this course will select and use a variety of optimization approaches to address various business needs. The course covers classes of optimization problems at a foundational level (continuous/discrete, linear/nonlinear, and bounded/unbounded) and the solving of linear optimization problems in both Python and R through the use of gradient and non-gradient-based algorithms. Analytics Programming is a prerequisite. 

Machine Learning

Machine Learning comprises the broad discipline of developing algorithms and statistical models to predict, classify, or cluster data and iteratively improve over time. Machine Learning focuses on building, training, running, and testing supervised and unsupervised models and quantifying the accuracy and precision of those models to determine which may best be used in a particular business situation. Supervised methods discussed include k-nearest neighbors, decision trees, and support vector machines. Unsupervised models discussed include k-means clustering, hierarchical clustering, and t-distributed stochastic neighbor embedding (t-SNE). Ensemble methods are also presented. The following courses are prerequisites: Analytics Programming and Statistical Data Mining.

Data Science Capstone

Data Science Capstone integrates the key concepts from the MSDA core with the knowledge gained in the three courses within the Data Science specialization. In this course, students will evaluate various needs and opportunities in an organization or marketplace. In addition, students will identify business requirements and translate those business requirements into technical requirements. Finally, students will create a comprehensive project plan to solve a problem in a way that satisfies customer or business needs. Projects within this specialization may include the design and construction of machine learning approaches, optimization, and the use of advanced analytics techniques as the project requires. There is no prerequisite for this course.

as opposed to

Cloud Databases

Cloud Databases covers the application of cloud architectures to large-scale data systems. The course discusses the differences between cloud-native approaches to data architectures and smaller-scale systems. Students in this course apply cloud computing concepts to address specific business scenarios. There is no prerequisite for this course.

Data Engineering Capstone

The Data Engineering Capstone has learners utilize the skills learned throughout the MDSA core courses and the data engineering courses to examine a problem where data engineering is a solution and to build a cloud-native infrastructure that allows for data processing. Learners are asked to implement their solutions and tell a story using the data. Course material introduces the project and reminds learners of relevant learning resources from previous courses that will prove helpful in completing the performance assessment.

Data Analytics at Scale

Data Analytics at Scale builds on previous data engineering courses and discusses approaches for analyzing large data sets. The course discusses map/reduce approaches, Apache Spark, and cloud–native solutions for developing, automating, and scaling data analytics. Also discussed are methods for integrating data processing pipelines and data stores to create comprehensive data analytics architectures.

Data Processing

Data Processing includes the practice of automating data flow into and out of components of an analytics system. Data processing comprises a major part of the analytics life cycle in modern organizations. This course covers concepts in extract, transform, and load (ETL) pipeline operations on data at scale and variations of ETL as a function of data repositories, including data warehouses and data lakes. Streaming and batch data operations and their differences are discussed, and students implement pipeline solutions in cloud-native environments. There is no prerequisite for this course.

[Mint Berry Crunch]

out of 11 courses the first 7 are the same for both

https://www.wgu.edu/online-it-degrees/da...ience.html

https://www.wgu.edu/online-it-degrees/da...ering.html

1: The Data Analytics Journey
2: Data Management
3: Analytics Programming
4: Data Preparation and Exploration
5: Statistical Data Mining
6: Data Storytelling for Varied Audiences
7: Deployment

look at the remaining 4 courses in each and see which you're interested in

for me its no contest
Data Science Specialization all the way  

Advanced Analytics

Advanced Analytics extends analytics techniques from machine learning to artificial intelligence more broadly, including topics in neural networks, deep learning, and natural language processing. The course discusses approaches to developing these models including PyTorch and TensorFlow. Students learn to apply a combination of techniques to solve complex business challenges including computer vision and sentiment analysis. There is no prerequisite for this course.

Optimization

Optimization is a large class of business problems requiring the iterative algorithmic maximization or minimization or one or more variables. Students in this course will select and use a variety of optimization approaches to address various business needs. The course covers classes of optimization problems at a foundational level (continuous/discrete, linear/nonlinear, and bounded/unbounded) and the solving of linear optimization problems in both Python and R through the use of gradient and non-gradient-based algorithms. Analytics Programming is a prerequisite. 

Machine Learning

Machine Learning comprises the broad discipline of developing algorithms and statistical models to predict, classify, or cluster data and iteratively improve over time. Machine Learning focuses on building, training, running, and testing supervised and unsupervised models and quantifying the accuracy and precision of those models to determine which may best be used in a particular business situation. Supervised methods discussed include k-nearest neighbors, decision trees, and support vector machines. Unsupervised models discussed include k-means clustering, hierarchical clustering, and t-distributed stochastic neighbor embedding (t-SNE). Ensemble methods are also presented. The following courses are prerequisites: Analytics Programming and Statistical Data Mining.

Data Science Capstone

Data Science Capstone integrates the key concepts from the MSDA core with the knowledge gained in the three courses within the Data Science specialization. In this course, students will evaluate various needs and opportunities in an organization or marketplace. In addition, students will identify business requirements and translate those business requirements into technical requirements. Finally, students will create a comprehensive project plan to solve a problem in a way that satisfies customer or business needs. Projects within this specialization may include the design and construction of machine learning approaches, optimization, and the use of advanced analytics techniques as the project requires. There is no prerequisite for this course.

as opposed to

Cloud Databases

Cloud Databases covers the application of cloud architectures to large-scale data systems. The course discusses the differences between cloud-native approaches to data architectures and smaller-scale systems. Students in this course apply cloud computing concepts to address specific business scenarios. There is no prerequisite for this course.

Data Engineering Capstone

The Data Engineering Capstone has learners utilize the skills learned throughout the MDSA core courses and the data engineering courses to examine a problem where data engineering is a solution and to build a cloud-native infrastructure that allows for data processing. Learners are asked to implement their solutions and tell a story using the data. Course material introduces the project and reminds learners of relevant learning resources from previous courses that will prove helpful in completing the performance assessment.

Data Analytics at Scale

Data Analytics at Scale builds on previous data engineering courses and discusses approaches for analyzing large data sets. The course discusses map/reduce approaches, Apache Spark, and cloud–native solutions for developing, automating, and scaling data analytics. Also discussed are methods for integrating data processing pipelines and data stores to create comprehensive data analytics architectures.

Data Processing

Data Processing includes the practice of automating data flow into and out of components of an analytics system. Data processing comprises a major part of the analytics life cycle in modern organizations. This course covers concepts in extract, transform, and load (ETL) pipeline operations on data at scale and variations of ETL as a function of data repositories, including data warehouses and data lakes. Streaming and batch data operations and their differences are discussed, and students implement pipeline solutions in cloud-native environments. There is no prerequisite for this course.

Interesting. Before, I would have no idea what any of this stuff was. I'm glad that part of the DBA program introduced me to BI as one of its courses so now I'm only slightly familiar with these concepts. Data Lakehouses have become a particularly engrossing topic of mine to explore. If given the chance, yes, I agree, Data Science specialization would be a great path. Although, the engineering one seems interesting too.

[bjcheung77]

For the Engineering vs Science tracks... It really depends what you're looking to take for the electives, as those four classes determine the track... at the end of the day, both of them are MSDA degrees.