Data Science Fundamentals

This course covers the foundational aspects of data science, including data collection, cleaning, analysis, and visualization. Students will learn practical skills for working with real-world datasets.

Instructor: Prof. Data

Term: Spring

Location: Science Building, Room 202

Time: Mondays and Wednesdays, 2:00-3:30 PM

Course Overview

This course provides a comprehensive introduction to data science principles and practices. Students will:

  • Learn the end-to-end data science workflow
  • Gain practical experience with data manipulation tools
  • Develop skills in data visualization and communication
  • Apply statistical methods to derive insights from data

Prerequisites

  • Basic programming knowledge (preferably in Python)
  • Introductory statistics
  • Comfort with basic algebra

Textbooks

  • “Python for Data Analysis” by Wes McKinney
  • “Data Science from Scratch” by Joel Grus

Grading

  • Assignments: 50%
  • Project: 40%
  • Participation: 10%

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layout: course title: Introduction to Machine Learning description: This course provides an introduction to machine learning concepts, algorithms, and applications. Students will learn about supervised and unsupervised learning, model evaluation, and practical implementations. instructor: Prof. Example year: 2023 term: Fall location: Main Campus, Room 301 time: Tuesdays and Thursdays, 10:00-11:30 AM course_id: intro-machine-learning schedule:

  • week: 1 date: Sept 5 topic: Course Introduction description: Overview of machine learning, course structure, and expectations. materials:
    • name: Syllabus url: /assets/pdf/example_pdf.pdf
    • name: Slides url: /assets/pdf/example_pdf.pdf
  • week: 2 date: Sept 12 topic: Linear Regression description: Introduction to linear regression, gradient descent, and model evaluation. materials:
    • name: Lecture Notes url: /assets/pdf/example_pdf.pdf
    • name: Assignment 1 url: /assets/pdf/example_pdf.pdf
  • week: 3 date: Sept 19 topic: Classification description: Logistic regression, decision boundaries, and multi-class classification. materials:
    • name: Lecture Notes url: /assets/pdf/example_pdf.pdf
    • name: Coding Lab url: https://github.com/
  • week: 4 date: Sept 26 topic: Decision Trees and Random Forests description: Tree-based methods, ensemble learning, and feature importance. materials:
    • name: Lecture Notes url: /assets/pdf/example_pdf.pdf
    • name: Assignment 2 url: /assets/pdf/example_pdf.pdf
  • week: 5 date: Oct 3 topic: Support Vector Machines description: Margin maximization, kernel methods, and support vectors. materials:
    • name: Lecture Notes url: /assets/pdf/example_pdf.pdf
    • name: Review Materials url: /assets/pdf/example_pdf.pdf

  • week: 6 date: Oct 10 topic: Midterm Exam description: Covers weeks 1-5.

  • week: 7 date: Oct 17 topic: Neural Networks Fundamentals description: Perceptrons, multilayer networks, and backpropagation. materials:
    • name: Lecture Notes url: /assets/pdf/example_pdf.pdf
    • name: Assignment 3 url: /assets/pdf/example_pdf.pdf
  • week: 8 date: Oct 24 topic: Deep Learning description: Convolutional neural networks, recurrent neural networks, and applications. materials:
    • name: Lecture Notes url: /assets/pdf/example_pdf.pdf
    • name: Coding Lab url: https://github.com/ —

Course Overview

This introductory course on machine learning covers fundamental concepts and algorithms in the field. By the end of this course, students will be able to:

  • Understand key machine learning paradigms and concepts
  • Implement basic machine learning algorithms
  • Evaluate and compare model performance
  • Apply machine learning techniques to real-world problems

Prerequisites

  • Basic knowledge of linear algebra and calculus
  • Programming experience in Python
  • Probability and statistics fundamentals

Textbooks

  • Primary: “Machine Learning: A Probabilistic Perspective” by Kevin Murphy
  • Reference: “Pattern Recognition and Machine Learning” by Christopher Bishop

Grading

  • Assignments: 40%
  • Midterm Exam: 20%
  • Final Project: 30%
  • Participation: 10%

Schedule

Week Date Topic Materials
1 Feb 5 Introduction to Data Science

Overview of the data science workflow and key concepts.
2 Feb 12 Data Collection and APIs

Methods for collecting data through APIs, web scraping, and databases.
3 Feb 19 Data Cleaning and Preprocessing

Techniques for handling missing values, outliers, and data transformation.
4 Feb 26 Exploratory Data Analysis

Descriptive statistics, visualization, and pattern discovery.
5 Mar 4 Statistical Analysis

Hypothesis testing, confidence intervals, and statistical inference.
6 Mar 11 Data Visualization

Principles and tools for effective data visualization.