INTRODUCTORY DATA SCIENCE FOR THE AGRICULTURAL AND LIFE SCIENCES

Name: INTRODUCTORY DATA SCIENCE FOR THE AGRICULTURAL AND LIFE SCIENCES
Availability: OutOfStock
Rating: 4.4 (137 reviews)

BSE 380

과목 설명

Agricultural and life scientists need to creatively apply physical, chemical, and biological principles to address technical, business, and environmental challenges. Many of these challenges involve numerical analyses, including the interpretation of large datasets. The fundamentals of computer coding using numerical software will be taught, using real-world data science challenges from the agricultural and life sciences.

선수과목

(CHEM 103 , CHEM 109 , or CHEM 115 ) and (MATH 112 , MATH 114 , 217 or placement into MATH 211 or MATH 221 )

충족 요건

BSE 405

학점

미보고

개설 시기

미보고

평점

3.5

0.52% 과거 데이터 대비

수료율

91.67%

-1.12% 과거 데이터 대비

A 비율

62.5%

0.74% 과거 데이터 대비

학급 규모

40.15% 과거 데이터 대비

Cumulative Grade Distribution

강사 (2026 Summr)

다음 사이트의 평점순으로 정렬 Rate My Professors

유사 과목

INTRODUCTION TO PRECISION AGRICULTURE

BSE 305

Introduces managing agricultural production systems using Precision Agriculture (PA), including its modern extensions in smart and digital farming. Provides an overview of the fundamentals of agricultural production systems and the sources of crop variability. Explores geographic positioning and information systems, examining how these tools integrate yield and quality sensors to monitor spatial variability. Covers temporal observation techniques using remote and proximal sensing systems. Culminates in studying the technologies used for implementing and managing variable-rate prescriptions and other management interventions.

DATA VISUALIZATION FOR AGRICULTURAL AND LIFE SCIENCES

AAE 337

Introduction to principles data visualization with applications in agricultural and life sciences. Learn to effectively communicate quantitative data via visual graphics and critically evaluate graphical data in the news and popular media. Develop practical data visualization skills in Microsoft-based programs Excel and Power BI, including tables, maps, dashboards, charts and other graphics that display trends and relationships within and between quantitative variables.

DATA SCIENCE FOR AGRICULTURAL AND APPLIED ECONOMICS

AAE 718

Introduction to data and data processing using both Python and R programming languages. Concepts covered include loading data, data acquisition, cleaning data, visualization/exploring data, and storing data.

ENVIRONMENTAL DATA SCIENCE

F&WECOL 458

Introduces fundamental machine learning techniques for numerical modeling and data analysis and modern computer programming tools used to analyze, prepare, and visualize data from common formats of datasets in the field of Earth and environmental sciences. Emphasizes opportunities to consider real-world applications for concepts in environmental data science.

ARTIFICIAL INTELLIGENCE IN AGRICULTURE

BSE 405

Provides an understanding of how cutting-edge Artificial Intelligence (AI) technologies revolutionize and optimize various aspects of the agricultural sector. Covers topics related to advanced sensors for data acquisition, data processing and visualization, and machine learning models to inform agricultural decision making. Introduces both theoretical concepts and practical insights into real-world AI implementation in agriculture.