CENG 474 Introduction to Data Science (3-0) 3

Requirements: Students enrolled in the course should be comfortable with programming and be reasonably mathematically mature.

Description:  Introduction to data science by using tools and writing programs for acquiring, cleaning, analyzing, exploring, and visualizing data; making data-driven inferences and decisions; and effectively communicating results. Learning data manipulation, data analysis with statistics and machine learning, data communication with information visualization, working with big data using scalable techniques.




Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by. Wes McKinney, O’Reilly

Python  :

Objectives and Learning outcomes

  1. Understand the key concepts in data science, including their real-world applications and the toolkit used by data scientists
  2. Use exploratory tools such as clustering and visualization tools to analyze data
  3. Produce programming code to statistically analyze a dataset
  4. Plan and generate visualizations from data using programming
  5. Implement data collection and management scripts; acquire data through web-scraping and data APIs
  6. Clean and reshape messy datasets
  7. Perform linear regression analysis
  8. Use and implement classification and clustering methods
  9. Evaluate outcomes and make decisions based on data
  10. Effectively communicate results



Weekly Lectures (subject to change)

  1. Introduction
  2. Python
  3. Data visualization
  4. Linear Algebra
  5. Descriptive statistics
  6. Advanced data structures
  7. Midterm
  8. Working with data
  9. Machine learning
  10. Classification
  11. Clustering
  12. Big data processing


  • Assignments 20%
  • Midterm 20%
  • Project 40%
  • Final Exam 20%

Web site

  • Course web site is at webonline  and students are responsible to follow the announcements on the web site about the requirements.

Academic honesty

  • All course work you submit (assignments, exams, programs, papers, etc.) must be done on your own. Note that academic dishonesty includes not only cheating, fabrication, and plagiarism, but also includes helping other students commit acts of academic dishonesty by allowing them to obtain copies of your work. You are allowed to use the web for reference purposes, but you may not copy code or other written materials from any website or any other source as your own work.
  • Cases of academic dishonesty will be dealt harshly. Each such case will be referred to the university administration. If the student is found to be responsible of academic dishonesty, he/she can get suspension from the university for a semester and even expelled from the university in repeating cases.