Five Common Courses in a Data Science Degree Program

Last Updated By • December 21, 2022

FIVE COMMON COURSES IN A DATA SCIENCE DEGREE PROGRAM

Data Science Degree Program Courses

  • Applied machine learning
  • Data visualization
  • Statistical analysis
  • Mathematical modeling
  • Data mining

Data science professionals assist with large amounts of data to improve overall operation and boost revenue. Data science courses within data science degree programs provide individuals with the necessary know-how and abilities to succeed in the field. They focus on improving quantitative analysis skills and mathematical abilities to give students a competitive edge for a wide range of careers in data science. The courses in data science degree programs provide the depth and breadth of specific topics in the area and many include practical learning components.

Applied Machine Learning

One of the most popular data science courses is applied machine learning. This course introduces students to the field and focuses on its techniques and methods. It often compares and contrasts machine learning with statistics. It discusses characteristics of data and involves the study of clustering data and evaluating those clusters. Students often participate in task where they create predictive models. They gain an understanding of process issues as they are involved with data generalizability, such as overfitting and cross validation. Students gain an understanding of how to recognize the difference between clustering and classification techniques and which is needed for particular sets of data.

Data Visualization

Data visualization is among the courses in data science programs. The course focuses on the methods used in data visualization for investigating, reporting, and monitoring tools. It introduces computational tools. It helps students gain a solid understanding of clear and effective communication when presenting findings in data collection. The course also commonly focuses on the design and execution of corresponding visual and verbal representation of patterns and evaluations in order to express findings, answer questions, help with decisions, and provide credible evidence supported by data. Students typically engage in hands-on experience with building data visualizations.

Statistical Analysis

Statistical analysis is one of the data science degree program courses that focuses on advanced statistical data science methods. It covers a variety of topics including neural networks, classification, vector machines, unsupervised learning, and tree-based and ensemble methods. Students commonly learn about the fundamentals of statistical reasoning and perform statistical analysis. They explore data and translate and communicate analytical results. Students learn about a variety of statistical methods of gathering and evaluating data. Students learn how to use statistical software to obtain data and communicate their results. Often times this course uses case studies and other hands-on learning activities.

Mathematical Modeling

Mathematical modeling courses focus on the increase of big data and using models and tools to obtain information and evaluate large datasets. It explains how mathematics supports many of the tools that are used to manage and evaluate big data. It shows how different applied problems can have common mathematical objectives and can be solved using similar tools. Students learn about examples of the tools, including graphing for clustering and value decomposition. The course covers important mathematical concepts as they are related to big data analytics, such as principal component analysis, the Laplacian graph, singular value decomposition, and eigenvalues and eigenvectors.

Data Mining

Data mining is a central aspect of the data science field as it combines elements of statistics, artificial intelligence, machine learning, and data processing. Within this course for data science, students study methods for handling, visualizing, and interpreting data. The course focuses on both the methods and the mathematical foundations of the topics. Students learn about traditional methods like Bayes Decision Theory and modern techniques like Support Vector Machines. Students have the ability to see how the data mining algorithms work together by examining case studies and studying more topics more in-depth.

Conclusion

As reported by Forbes, data science is one of the fastest growing fields as businesses and organizations strive to improve their data collection and maintenance processes. Data science courses prepare students to pursue a variety of lucrative careers in this area.

Related Resources: 
Scroll to Top