DS101 Introduction to Data Science
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Course Title: Introduction to Data Science
Course Code: DS101
Course Description:
This course provides an introduction to the field of data science, focusing on fundamental concepts, techniques, and tools used in the analysis and interpretation of data. Students will learn about data collection, cleaning, exploration, visualization, statistical analysis, and machine learning. Practical exercises and projects will be used to reinforce the concepts discussed.
Course Objectives:
1. Understand the fundamental principles and concepts of data science.
2. Acquire skills in data collection, cleaning, and preprocessing.
3. Explore and visualize data using appropriate tools and techniques.
4. Apply statistical analysis methods to draw insights from data.
5. Learn basic machine learning algorithms and their applications.
6. Develop practical data science skills through hands-on exercises and projects.
Textbook:
- Title: An Introduction to Data Science
- Authors: Jeffrey Saltz et al.
- Edition: First Edition
Grading:
- Class participation: 10%
- Assignments: 30%
- Midterm exam: 20%
- Final project: 40%
Course Outline:
Module 1: Introduction to Data Science
- Overview of data science
- Historical development and applications of data science
- Data science life cycle
- Ethical considerations in data science
Module 2: Data Collection and Preprocessing
- Data sources and types
- Data collection methods
- Data cleaning and preprocessing techniques
- Data quality assessment
Module 3: Exploratory Data Analysis
- Descriptive statistics
- Data visualization using charts and graphs
- Exploratory data analysis techniques
- Data summarization and aggregation
Module 4: Statistical Analysis in Data Science
- Probability and distributions
- Hypothesis testing
- Confidence intervals
- Correlation and regression analysis
Module 5: Machine Learning Fundamentals
- Introduction to machine learning
- Supervised learning algorithms
- Unsupervised learning algorithms
- Model evaluation and selection
Module 6: Introduction to Big Data
- Introduction to big data concepts
- Big data processing frameworks (e.g., Hadoop, Spark)
- Distributed computing for big data analytics
- Scalable data storage and retrieval
Module 7: Data Science Applications and Case Studies
- Applications of data science in various domains (e.g., finance, healthcare, marketing)
- Case studies highlighting real-world data science projects
- Ethical considerations in data science applications
Module 8: Final Project
- Students will work on a data science project of their choice, applying the knowledge and skills gained throughout the course.
Please note that the topics and sequencing may be adjusted based on the instructor's discretion and the pace of the class. This syllabus outline provides a general framework for the course based on the book "An Introduction to Data Science" by Jeffrey Saltz et al.
2023-07-18