University of North Dakota (UND)
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Data Scientists

Springboard
Closed
Springboard
Springboard
San Francisco, California, United States
Lorena Galvan
Senior Program Manager
(2)
3
Timeline
  • November 7, 2022
    Experience start
  • November 9, 2022
    Kick Off Meeting
  • November 16, 2022
    Finalize Project Plan
  • November 22, 2022
    Mid-Point Check in
  • December 17, 2022
    Final Presentation
  • December 22, 2022
    Experience end
Experience
7/7 project matches
Dates set by experience
Preferred companies
United States
Any
Any industries
Categories
Databases Data visualization Data analysis Data modelling Data science
Skills
machine learning data analytics data science natural language processing
Learner goals and capabilities

Looking to elevate your organization, and bring it to the next level? Bring on learners from Springboard's Data Science Bootcamp to be your student-consultants, in a project-based experience. Students will work on one main project over the course of the semester, connecting with you as needed with virtual communication tools.

Students in this program go beyond just the technical skills, to focus on areas where employers find the biggest gaps -- strategic thinking, problem-solving, and communication. Students have an analytical mindset and are ready to work with your company with their technical skills and business and professional prowess. They possess an excellent understanding of data visualization, data storytelling, and how to think about data problems in a business context.

Springboard data scientists typically have about 3 years of professional experience before enrolling in the 9-12 month course. They demonstrate real-world experience while helping solve a business problem.

Learners
Bootcamp
Any level
25 learners
Project
80 hours per learner
Learners self-assign
Teams of 4
Expected outcomes and deliverables

Deliverables are negotiable, and will seek to align the needs of the students and the client.

Some final project deliverables might include:

  1. A 10-15 minute presentation on key findings and recommendations
  2. A detailed report including their research, analysis, insights and recommendations
Project timeline
  • November 7, 2022
    Experience start
  • November 9, 2022
    Kick Off Meeting
  • November 16, 2022
    Finalize Project Plan
  • November 22, 2022
    Mid-Point Check in
  • December 17, 2022
    Final Presentation
  • December 22, 2022
    Experience end
Project Examples

Students in groups of 3-5 will work with your company over 6-8 weeks to identify your needs and provide actionable recommendations, based on their in-depth research and analysis.

These are fully remote services and companies of all stages are welcomed. We ask that companies with interest in hosting a Springboard Data Science student have access to relevant data necessary for execution of the project and have a data scientist, or someone with a related background, on staff.

Project activities that Springboard students can complete may include:

  • Data cleaning and wrangling
  • Data analysis
  • Modeling and experimentation
  • Predictions
  • Insights
  • Live Visualizations

Common Business Problems requested to be solved include, but are not limited to:

  • Predicting customer churn
  • Predicting how much revenue you’ll get from any individual customer
  • Predicting which patients will be re-hospitalized after a stay / Predicting which patients are at the highest risk for a poor health outcome
  • Predicting company revenue (time-series)
  • Predicting activity from wearables data
  • Investigating why there was a big change in customer activity
  • Investigating what’s the fastest area of revenue growth
  • What factors are related to revenue growth?
  • Are your services priced competitively / appropriately?
  • What is your largest manufacturing costs? What are your areas of greatest cloud spending?
  • Visualizing survey data for employee satisfaction from HR (~60k employees) without needing to read every response
  • Get back a lot of data from the devices out in the field
  • Figuring out ways of visualizing battery life from devices of different ages (can see what is going on right now, and how does it change over time?)
  • Using Excel to create a default financial analysis module covering revenue and operational cost modelling.
  • Financial concepts including revenue, cost of goods sold, profit, balance sheets, cash flow statements, income statements and EBIT
  • Economic concepts including supply and demand curves, cost curves
  • Statistical concepts including descriptive statistics (mean, mode, standard deviation, correlations etc.), correlations, simple and multivariate regression, confidence intervals
Companies must answer the following questions to submit a match request to this experience:

Be available for a quick phone/virtual call with the instructor to initiate your relationship and confirm your scope is an appropriate fit for the course.

Provide a dedicated contact person who is available for weekly/bi-weekly check-in meetings to unblock the team, provide feedback, as well as periodic messages over the duration of the project.

Provide an opportunity for students to share their work on job search materials; redactions of confidential information can be discussed.

Provide relevant information/data as needed for the project.

How does the project/ business problem you are hoping to have solved, benefit the data scientists in their job search?

Does your company have an established data team or data individuals who will work with students in this project?