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ITE 307: Data Analysis with Python

Course Description

Data Analysis with Python introduces students to the powerful tools and libraries available in Python for data analysis. The course covers key concepts such as data manipulation, cleaning, and exploration using libraries like Pandas and NumPy. Students will also learn to visualize data using Matplotlib and Seaborn and perform statistical analysis to uncover patterns and trends. By the end of the course, students will have the skills to handle real-world datasets, conduct meaningful analyses, and draw insights, making Python a valuable tool in their data science toolkit. (3 credits)

Prerequisite

  • ITE 115: Program Logic and Design with Python

Student Learning Outcomes (SLOs)

Students who successfully complete this course will be able to:

  1. Demonstrate proficiency in using Python libraries such as Pandas and NumPy to load, clean, and manipulate datasets, addressing common data quality issues like missing values and duplicates.
  2. Perform exploratory data analysis (EDA) using descriptive statistics and data visualization techniques to identify patterns, trends, and outliers in real-world datasets.
  3. Visualize data insights using Matplotlib and Seaborn, creating clear and informative plots that effectively communicate findings to various audiences.
  4. Conduct statistical analyses in Python, including correlation, regression, and hypothesis testing, to draw data-driven conclusions from complex datasets.
  5. Evaluate the quality and reliability of datasets, identifying potential biases or limitations that could affect the accuracy of data analysis results.
  6. Apply Python-based data analysis techniques to a capstone project or case study, integrating data cleaning, manipulation, analysis, and visualization to provide actionable insights.

Course Activities and Grading

AssignmentsWeight

Discussions (Weeks 1-8)

6%

Quizzes (Weeks 1-8)

34%

Labs & Programming Assignments (Weeks 1-8)

60%

Total

100%

Required Textbooks

  • This course uses Open Educational Resources (OER). OER are openly licensed, educational resources that can be used for teaching, learning and research. OER may consist of a variety of resources such as textbooks, videos and software that are no cost for students.

Course Schedule

Week

SLOs

Readings and Exercises

Assignments

1

1,5

Topics: Python Refresher and Data Analytics

  • Read and Review:
    • Python Data Analytics - Modules 1-5

 

  • Read assigned material
  • Participate in the Discussions
  • Submit the Week 1 quizzes and programming assignments

2

2

Topic: Basic Data Analysis with Python

  • Read and Review:
    • Data Analysis with Python - Modules 1-6

 

 

  • Read assigned material
  • Participate in the Discussions
  • Submit the Week 2 quizzes and labs

3

1,5

Topics: Mastering Pandas and NumPy

  • Read and Review:
    • Python for Data Science, AI & Development - Module 4
    • Mastering Data Analysis with Pandas
  • Read assigned material
  • Participate in the Discussions
  • Submit the Week 3 quizzes and labs

4

1,5,6

Topic: Scrape and Analyze Data

  • Read and Review:
    • Python Project for Data Science
  • Read assigned material
  • Participate in the Discussions
  • Submit the Week 4 quizzes

5

4

Topic: Statistical Foundation

  • Read and Review:
    • Statistics Foundations - Modules 1-4
  • Read assigned material
  • Participate in the Discussions
  • Submit the Week 5 quizzes

6

2,3

Topics: Python, Databases, and Data Analysis

  • Read and Review:
    • Databases and SQL for Data Science with Python - Modules 1-4
  • Read assigned material
  • Participate in the Discussions
  • Submit the Week 6 quizzes and labs

7

3

Topic: Data Visualization with Python

  • Read and Review:
    • Data Visualization with Python - Modules 1-4
  • Read assigned material
  • Participate in the Discussions
  • Submit the Week 7 quizzes and labs

8

3,6

Topic: Exploring Data Visualization Techniques with Matplotlib and Seaborn

  • Read and Review:
    • Python for Data Visualization: Matplotlib & Seaborn

 

  • Read assigned material
  • Participate in the Discussions
  • Submit the Week 8 quiz and lab
  • Complete course evaluation

COSC Accessibility Statement

Charter Oak State College encourages students with disabilities, including non-visible disabilities such as chronic diseases, learning disabilities, head injury, attention deficit/hyperactive disorder, or psychiatric disabilities, to discuss appropriate accommodations with the Office of Accessibility Services at OAS@charteroak.edu.

COSC Policies, Course Policies, Academic Support Services and Resources

Students are responsible for knowing all Charter Oak State College (COSC) institutional policies, course-specific policies, procedures, and available academic support services and resources. Please see COSC Policies for COSC institutional policies, and see also specific policies related to this course. See COSC Resources for information regarding available academic support services and resources.