Course:FRE521D
| Data Analytics in Food, Resource and Economics | |
|---|---|
 | |
| 521D | |
| Section: | |
| Instructor: | Amy Goldlist |
| Email: | |
| Office: | TBA |
| Office Hours: | TBA |
| Class Schedule: | Jan 8 to Feb 16
Lecture: Tue and Thur : 2:30-4 pm Lab: Wed: 5-6:30pm |
| Classroom: | MCML 154 |
| Important Course Pages | |
| Syllabus | |
| Lecture Notes | |
| Assignments | |
| Course Discussion | |
| [[Category:]] | |
COURSE DESCRIPTION
In this course we will explore data and try to find patterns. We will look at the field of data science, using R, SQL, and Python to move through a data pipeline, from database, through some data wrangling, data visualization and inference.
LEARNING OUTCOMES
I. Machine Learning Fundamental
Provides a basic understanding of the technical elements of machine learning through a business lens and with applications in climate, food and the environment.
Learning Outcomes
- Identify and describe machine learning terminology, different branches of machine learning, and steps needed to build a model .
- Demonstrate a theoretical and practical understanding of a number of machine learning approaches including regression, classification, clustering, decision trees and random forest.
- Separate data into training and testing data to prevent overfitting and optimize predictions.
- Describe the best and worst practices of managing machine learning projects.
- Gain practical experience and understand the value of applying machine learning to the climate, food and environmental sector. Example: Assessment of the ESG activities of a firms
II. SQL – Database Basics
Covers SQL’s foundational syntax and essential transformations with practical, hands-on examples. In addition, you will learn importance of structure in databases and why structure is essential plus the significance of SQL in data analysis and the modern job market.
Learning Outcomes
- Describe the structure and design of relational databases.
- Structure data using data models.
- Conduct basic SQL Queries, Functions and Aggregation for retrieving and filtering data from a database, transforming and combining data from multiple tables.
- Identify best practices and common errors when using SQL.
- Gain practical experience and understand the value of applying SQL to the climate, food and environmental sector. Example: Carbon Emissions Calculator or query of data of Europe's rivers, lakes, groundwater bodies and transitional, coastal and marine waters to review pollution and quality.
III. Data Access and Querying on Cloud Based Platforms: API methods.
Covers a review of the different data formats, such as CSV, JSON, or XML and how to access.
Learning Outcomes
- Describe and conduct data wrangling, data cleaning, and preliminary visualization with Python
- Identify the advantages and disadvantages of the major data formats and best fit with analysis objectives.
ASSESSMENT REPORT
- Current Event Connections: Illustrate how your course ties into contemporary issues.
Machine Learning is the tool for analyzing big data and increasingly food and environment application have a big data component. This course will apply the data tools to areas such as ESG assessment, climate impact, and business ratings.
- Career Relevance: Highlight how the course aligns with prospective career paths, emphasizing real-world applicability.
Businesses such as banks (lending assessment), agribusinesses (precision ag) are increasingly reliant on SQL and machine learning for assessments and predictive analytics.
- Course Evaluations:
Major project provides the opportunity to practice and data skills to a real-world data set.
BIG QUESTIONS & REAL-WORLD APPLICATIONS IN CLIMATE, FOOD AND THE ENVIRONMENT COVERED IN THE COURSE
- How can we transition to making data-driven decisions?
- How to use data and numbers to tell a story which will influence decision makers?
ASSESSMENT METHODS
| Activity | % of Grade |
|---|---|
| Assignments / Labs | 20% |
| Project | 30% |
| Quizz | 10% |
| Participation | 10% |
| Midterm | 30% |
| Total | 100% |