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Data Sciences & Data Analytics Certificate Program Internship (Spring 2024)

DATA 1001
Closed
Main contact
Georgetown University
Georgetown University
Washington, District of Columbia, United States
Lisa Andrews
Director of Integrated Career Development
(6)
6
Timeline
  • April 9, 2024
    Challenge start
  • April 27, 2024
    Meet with Dr. Lisa Andrews
  • May 7, 2024
    Meet with Dr. Lisa Andrews
  • May 11, 2024
    Challenge end
Challenge
8/5 project matches
Dates set by challenge
Preferred companies
Anywhere
Any company type
Any industries

Challenge scope

Categories
Machine learning Data visualization Data analysis Data modelling Data science
Skills
python data engineering
Learner goals and capabilities

Georgetown University student consultants, proficient in Python, provide comprehensive support to both data science and data engineering teams through a hands-on applied machine learning program. The program consists of two main phases:


Software Engineering for Data Science: Skills development covering databases, data ingestion, and wrangling. Exploration of various software engineering topics.


Distributional Statistical Analysis and Modeling: Utilization of scikit-learn for modeling and statistical analysis. Exploration of various models and selection techniques.


Throughout the semester, learners engage in a challenging main project, maintaining virtual communication as needed.


Additionally, Python-savvy student consultants actively contribute to data analytics teams, receiving training in Python, SQL, statistics, data visualization, and culminating their learning in a capstone project. The capstone project modules include:


  1. Foundations of data analytics and Python basics.
  2. Advanced data analytics in Python and SQL.
  3. Statistics for pattern uncovering and analysis.
  4. Data visualization for creating compelling visualizations.
  5. Application of knowledge in a capstone project.


Throughout the semester, learners undertake a main project, maintaining virtual communication as required.

Learners

Learners
Certificate
Any level
15 learners
Project
30 hours per learner
Learners self-assign
Individual projects
Expected outcomes and deliverables

Students engage in projects following the data analytics pipeline, covering data collection, processing, analysis, visualization, and interpretation. They conduct data-driven investigations to inform business decisions and organizational strategies.


Student Work:

  • Identify data sources and create analytical data sets.
  • Process and analyze data using Python and SQL.
  • Use Python libraries and third-party packages for efficient data handling.
  • Perform basic statistical analysis for insights.
  • Create various common chart types with Python and Tableau.
  • Communicate statistical ideas clearly.


Deliverables:

Varied deliverables, including presentations on data characteristics, trained scikit-learn models, data wrangling Python code, and documentation with visualizations. Specifics depend on the project scope.


Project timeline
  • April 9, 2024
    Challenge start
  • April 27, 2024
    Meet with Dr. Lisa Andrews
  • May 7, 2024
    Meet with Dr. Lisa Andrews
  • May 11, 2024
    Challenge end

Project examples

Requirements

Students engage in projects following the complete data science and analytics pipeline, covering data collection, ingestion, wrangling, computational storage, modeling, analytics, and visual diagnostics. They conduct hypothesis-driven development, building models from datasets for business decision-making or software applications.

Student(s) Work Includes:


  • Identifying model hypotheses for classification, regression, and clustering.
  • Ingesting, wrangling, and storing data for querying and visualization.
  • Utilizing Python and SQL for distributional analysis and data visualization.
  • Applying scikit-learn for feature extraction and building transformer pipelines.
  • Training classification, regression, and clustering models.
  • Using Yellowbrick for visual analytics and diagnostics.
  • Communicating analysis results clearly and addressing ethical considerations, bias, and fairness in modeling.


Additionally, students can pursue projects following the data analytics pipeline, covering data collection, processing, analysis, visualization, and interpretation. They conduct data-driven investigations, generating insights from datasets for informed business decisions or organizational strategies.


Student(s) Work Includes:

  • Identifying data sources and creating analytical datasets.
  • Processing and analyzing data using Python and SQL.
  • Utilizing Python libraries and third-party packages for efficient data handling.
  • Conducting basic statistical analysis for data insights.
  • Creating various common chart types with Python and Tableau.
  • Communicating statistical ideas clearly and concisely to a broad audience.


Additional company criteria

Companies must answer the following questions to submit a match request to this challenge:

  • Q1 - Checkbox
     *