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Introduction to Data Science

CISC295
Fermé
Contact principal
Mercy University
Mercy University
Dobbs Ferry, New York, United States
Tianyu Wang
Assistant Professor
(1)
4
Chronologie
  • mars 31, 2025
    Début de expérience
  • avril 7, 2025
    Get Access to the Dataset
  • avril 14, 2025
    Data Modeling
  • mai 8, 2025
    Final Presentation
  • mai 12, 2025
    Fin de expérience
Expérience
1/1 match de projet
Dates fixées par le expérience
Entreprises privilégiées
New York, United States
Large enterprise, Non profit, Small to medium enterprise
N'importe qu'elle industrie

Portée de Expérience

Catégories
Visualisation des données Analyse des données Modélisation des données Data science
Compétences
numpy pandas matplotlib scikit-learn data analysis data modeling
Objectifs et capacités de apprenant.es

Elevate your organization by partnering with dedicated learners from Mercy University as your data-focused consultants in a project-based experience. Over the semester, our students will engage with your team to complete a targeted project, using virtual communication tools to collaborate effectively and ensure alignment with your goals.


Through this experience, our learners apply their skills with Python libraries, including IPython, NumPy, Pandas, and Matplotlib, for data storage, manipulation, and analysis. They gain hands-on practice with real-world datasets, learning to clean, transform, and visualize data; conduct thorough exploratory data analysis;


Partnering with our students provides your organization with fresh insights and actionable data solutions while empowering students to build professional-level experience with valuable analytical tools and techniques.

Apprenant.es

Apprenant.es
Premier cycle universitaire
Niveau Intermédiaire
6 apprenant.es dans le programme
Projet
30 heures par apprenant.e
Les Professeur.euses affectent les apprenant.es à des projets
Équipes de 2
Jusqu'à 1 équipe(s) ou 3 apprenant.e(s) par projet.
Chaque apprenant.e peut rejoindre jusqu'à 2 équipes
Résultats et livrables attendus

We expect students to complete a mini data science project utilizing the skills gained throughout the course, including Python libraries such as Pandas, NumPy, and Scikit-Learn. The students will work with datasets provided by the partnering company or employer, conducting hands-on data analysis and applying data science techniques. Through this experience, learners will connect with the partner organization as needed via virtual communication tools, ensuring alignment with project goals and relevance to organizational needs.


Project Deliverables Presentation

  • Format: 15-20 minute online presentation
  • Content: Key findings, insights, and recommendations based on the data analysis
  • Purpose: To communicate project outcomes, highlight actionable insights, and suggest potential solutions


Internship or Further Engagement Opportunities

  • Format: Follow-up discussions between the student and employer regarding potential internships
  • Purpose: Encourages continued collaboration for high-performing students, supporting internship placements with the employer for real-world experience
Chronologie du projet
  • mars 31, 2025
    Début de expérience
  • avril 7, 2025
    Get Access to the Dataset
  • avril 14, 2025
    Data Modeling
  • mai 8, 2025
    Final Presentation
  • mai 12, 2025
    Fin de expérience

Exemples de projets

We’re looking for real-world projects that allow students to apply their data analysis skills to meaningful datasets provided by employers. Our learners excel in analyzing structured datasets to uncover insights, identify trends, and make data-driven recommendations. Ideal projects should focus on data exploration, cleaning, statistical analysis, and basic predictive modeling. Here are a few examples of suitable project types:


Customer Behavior Analysis

  • Description: Analyzing customer purchase history to identify patterns, predict customer lifetime value, or assess factors influencing customer retention.
  • Goal: Generate insights into customer preferences and behaviors, with potential recommendations for marketing strategies.


Health and Wellness Analytics

  • Description: Using anonymized patient data to analyze factors contributing to health outcomes, such as risk factors for chronic conditions or trends in wellness program engagement.
  • Goal: Provide insights into health trends or predict health risks, helping healthcare providers enhance patient care or preventive measures.


Quality Control and Process Optimization

  • Description: Analyzing manufacturing or quality assurance data to detect patterns or anomalies affecting product quality.
  • Goal: Identify root causes of quality issues and recommend process optimizations for enhanced production quality.


Dataset Requirements

I need real employers who can provide datasets for students to analyze. Datasets should be in .csv, .xlsx, or any other tabular format to ensure compatibility with standard Python libraries.

Note:

  1. This is a data analysis course, not a data scraping course. Students will not be responsible for data collection or web scraping due to privacy, security, and legal policy concerns.
  2. This is a more like traditional data mining undergraduate level class that teaches students to use Python data framework (NumPy, Pandas) to analyze data (EDA, and simple machine learning like linear regression, SVM, etc.) The class will not cover anything about Artificial Intelligence.



Critères supplé mentaires pour entreprise

Les entreprises doivent répondre aux questions suivantes pour soumettre une demande de jumelage pour cette expérience:

  • Q1 - Texte long
    How is your project relevant to the experience?  *
  • Q2 - Texte court
    Can you provide the dataset for this project?  *
  • Q3 - Case à cocher
     *
  • Q4 - Case à cocher
     *