Course Title: Introduction to Data Science

Course Description:

This course introduces students to the principles and practices of designing data-driven solutions and applications. Through a combination of theory and hands-on projects, students will learn how to apply design thinking methodologies to solve real-world problems using data science techniques. Topics covered include data collection and preprocessing, exploratory data analysis, predictive modeling, and data visualization. By the end of the course, students will have the skills to design and implement data science projects that address complex challenges across various domains.

Course Objectives:

• Understand the principles of design thinking and its application to data science.
• Learn how to collect, clean, and preprocess data for analysis
• Explore exploratory data analysis techniques to gain insights from data.
• Develop predictive models using machine learning algorithms.
• Master the art of data visualization for effective communication of insights.
• Apply ethical considerations and best practices in data science design
• Work on real-world data science projects to gain practical experience.

Course Syllabus:

Week 1: Introduction to Data Science Design

• Overview of data science and its applications
• Introduction to design thinking
• Ethical considerations in data science design

Week 2-3: Data Collection and Preprocessing

• Data collection methods and sources
• Data cleaning and preprocessing techniques
• Handling missing data and outliers
• Data transformation and feature engineering

Week 4-5: Exploratory Data Analysis (EDA)

• Data visualization techniques
• Summary statistics and distribution analysis
• Correlation and relationships in data
• Dimensionality reduction methods

Week 6-7: Predictive Modeling

• Introduction to machine learning
• Supervised learning algorithms (e.g., regression, classification)
• Model evaluation and validation
• Hyperparameter tuning and model selection

Week 8-9: Data Visualization

• Principles of effective data visualization
• Visualization tools and libraries (e.g., matplotlib, seaborn, ggplot2)
• Designing interactive visualizations
• Storytelling with data

Week 10-11: Advanced Topics in Data Science Design

• Time series analysis and forecasting
• Unsupervised learning techniques (e.g., clustering, dimensionality reduction)
• Natural Language Processing (NLP) for text data
• Big data processing frameworks (e.g., Apache Spark)

Week 12: Capstone Project

• Work on a data science design project
• Apply design thinking and data science techniques
• Present project findings and insights
• Final project presentations and discussions.