DS100 Foundations Principles Theories of Data Science
Course Title: Foundations, Principles, and Theories of Data Science
Course Code: DS100
Course Overview:
This course provides a comprehensive exploration of the
foundational concepts, principles, and theoretical underpinnings of data
science. Students will gain a deep understanding of the key components that
drive the field, from the basics of data types to advanced statistical and
machine learning theories.
Course Schedule:
Week 1-2: Introduction to Data Science
- Overview
of Data Science and its Applications
- Importance
of Data in Decision Making
- Historical
Perspective and Evolution of Data Science
Week 3-4: Data Types and Data Representation
- Types
of Data: Nominal, Ordinal, Interval, Ratio
- Data
Encoding and Representation
- Data
Cleaning and Preprocessing Techniques
Week 5-6: Exploratory Data Analysis (EDA)
- Descriptive
Statistics
- Data
Visualization Techniques
- Univariate
and Bivariate Analysis
Week 7-8: Statistical Foundations
- Probability
Distributions
- Hypothesis
Testing
- Statistical
Inference
Week 9-10: Machine Learning Fundamentals
- Introduction
to Machine Learning
- Supervised
and Unsupervised Learning
- Model
Evaluation and Validation
Week 11-12: Advanced Machine Learning Theories
- Ensemble
Learning
- Deep
Learning Fundamentals
- Ethical
Considerations in Machine Learning
Week 13-14: Big Data and Technologies
- Introduction
to Big Data
- Tools
and Technologies for Big Data Processing
- Case
Studies in Big Data Analytics
Week 15: Capstone Project
- Application
of Data Science Principles to a Real-world Problem
- Presentation
and Evaluation of Capstone Projects
Grading:
- Assignments:
30%
- Midterm
Exam: 20%
- Final
Exam: 25%
- Capstone
Project: 20%
- Class
Participation: 5%
Recommended Texts:
- "Data
Science for Beginners" by [Author]
- "Introduction
to Statistical Learning" by [Author]
- "Python
for Data Analysis" by [Author]
Prerequisites:
- Basic
understanding of statistics
- Familiarity
with a programming language (preferably Python)
This syllabus provides a structured outline for covering the
fundamental aspects of data science, from foundational concepts to advanced
theories. Adjustments can be made based on the specific goals and focus areas
of the course.
