Introduces Python programming and data science, focusing on key libraries such as Pandas, Matplotlib and Scikit-learn. Students will use Jupyter notebooks for interactive coding, exploring topics such as data analysis, regression, clustering, decision trees, and web scraping. Hands-on projects develop skills in Python programing, data interpretation, decision-making, and effective communication.
Athena Title
Intro to Python and Data Sci
Non-Traditional Format
This course will be taught 95% or more online.
Semester Course Offered
Offered fall
Grading System
A - F (Traditional)
Student learning Outcomes
Students will master fundamental Python programming techniques and tools for data science applications.
Students will utilize Python data structures (e.g., lists, dictionaries, and tuples) to organize and manipulate data effectively.
Students will import, clean, and preprocess complex datasets for analysis and visualization.
Students will design and generate data visualizations to present insights clearly and effectively.
Students will identify trends, relationships, and anomalies in datasets by conducting exploratory data analysis (EDA).
Students will construct machine learning models, including regression, decision trees, and clustering, to represent and predict data behaviors.
Students will develop web scraping pipelines to extract, structure, and process online data sources.
Students will assess ethical, legal, and privacy considerations in data science applications and decision-making.
Topical Outline
Python Programming Basics & Markdown
• Introduction to Python syntax, control structures, and functions.
• Using Jupyter notebooks and Markdown for documentation and reporting.
Introduction to Data Structures
• Overview of Python data structures, e.g., lists, dictionaries, tuples, and sets.
• Working with pandas DataFrames and NumPy arrays.
Importing Data & Data Visualization
• Importing datasets from various file formats (e.g.: CSV, JSON).
• Creating advanced visualizations using Seaborn and Matplotlib.
Exploratory Data Analysis (EDA)
• Techniques for data cleaning and handling missing values.
• Statistical summaries and visualization techniques for understanding data.
Introduction to Machine Learning
• Overview of supervised and unsupervised learning methods.
• Understanding train-test split and evaluation metrics.
Regression Analysis
• Linear regression and regularization techniques.
• Model evaluation and interpretation.
Decision Trees and Random Forests
• Building decision trees for classification and regression.
• Introduction to ensemble methods and random forests.
• Interpretability versus accuracy in decision trees and ensembles.
• Handling imbalanced datasets in classification tasks.
• Ethical considerations when using decision trees in high-stakes applications.
Clustering Techniques
• K-means clustering and hierarchical clustering.
• Evaluating cluster quality.
• Challenges of determining the optimal number of clusters.
• Ethical concerns in segmentation and profiling.
• Risks of overgeneralization in unsupervised learning applications.
Dimensionality Reduction
• Principal Component Analysis (PCA).
• Applications of dimensionality reduction for visualization and preprocessing.
Introduction to Web Scraping
• Basics of web scraping.
• Ethical considerations and best practices for web scraping.
Institutional Competencies Learning Outcomes
Analytical Thinking
The ability to reason, interpret, analyze, and solve problems from a wide array of authentic contexts.
