Hi Sara,  although we are not a Community College, I know we can greatly benefit from the pathway development process. Our students are mostly commuters, and we seek to increase employability skills through our newly developed Professional Certificate program.  Thank you for sharing your work!

Thanks, Majd! The skills are captured in the following report: Profile of the Data Practitioner which was developed with input from a panel of experts from industries including biotechnology, finance, law enforcement, health care, agriculture, and public policy. Our Tools for Creating a Big Data Career Path includes gap analysis tools for both the school and employer, and in comparing these faculty are able to identify “gaps” in their existing program offerings as well as revealing skills that are covered in the curriculum but are not priorities for employers.

Hi Madj, I will describe the process from a faculty standpoint. 

After the Profile of a Data Practitioner was created, different individuals who work in the data industry reviewed the profile.  They rated each item in the profile based on expected knowledge levels of a middle skill data employee coming from a community college.  Each item was totaled, and a heat map of the profile was created.  The heat map was used by faculty to create learning outcomes for courses based on industry needs.  After the coursework was developed, industry was called in again to review any areas missing in the coursework with a gap analysis.  The analysis identified areas or both weaknesses and strength, which helped finalize the content in the programs. 

Thanks Ann -  Our work on developing pathways to data science careers that are aligned with industry expectations has been extremely successful.  On another project we are working with high schools in MA to integrate foundational data science skills into social studies Civics projects and developing courses in Data Visualization and Python+Data which will connect to our community college data science programs in MA.  The colleges in this Mentoring project are linchpins in our data pathway development effort,

Hi Gerhard, I would love to hear your insight about mathematical requirements for two year colleges and data science.  One of the struggles for community college data science is the meshing of various mathematical concepts and teaching it all in a 2 year program.  Right now, our students take Pre-Calc (if needed), Calc I, Linear Algebra, and Statistics.  Ideally, a "Math for Data Science I & II" course would be created where students are exposed to the concepts needed.  I have had discussions with Nick Horton (Amherst) and Ben Baumer (Smith) about this very topic and how to address it. 

To answer your questions, in the Boston area we don't place many students in manufacturing, but usually more technical fields.  We have placed students working in finance, bio-medical, renewable energy, health care and other technical fields.  Traditionally, students going to industry with an AS degree will be doing the grunt work of data cleaning and mining.  The idea is they will alleviate the tedious which allows the higher paid data scientists to spend their time doing the analysis.

Hi Gerhard, 

We are definitely dealing with the manufacturing analytics issue in Utah. At Bridgerland Technical College we are in the process of developing two courses which are both title "Manufacturing Analytics". One course will be for data technicians looking towards manufacturing and the other course will be for automated manufacturing/controls engineering techs looking towards data analytics. We are in the process of building both courses. 

Here is the course description for the controls engineering techs: 

This course will provide students with experience working with data as a control systems technician. Students will become familiar with the types of tasks which will be required of control systems technicians working with data in manufacturing. Students will learn several manufacturing data concepts while using multiple sets of data based on real-world scenarios, and apply the principles learned using real-world systems.

Objectives:

• Analyze data from multiple real-world manufacturing scenarios for multiple real-world manufacturing purposes.
• Present findings using a Human Machine Interface (HMI) or a Data Visualization program.
• Setup data transfer from a Programmable Logic Controller (PLC)-driven manufacturing system to a database table or spreadsheet.

Here is the course description for the data technicians:

The Manufacturing Analytics course provides students with experience working as a data practitioner in the field of manufacturing. Utilizing real-world situations, they gain experience with the types of tasks which are required of data practitioners working in manufacturing. Students go through the data cycle with multiple sets of data and different scenarios that can arise in manufacturing situations. Students optimize manufacturing data and practice predictive maintenance. They access a Programmable Logic Controller (PLC)-driven manufacturing system to a database and process that data as though in a live working environment utilizing data analysis programs and techniques. Students who complete this course are able to work with manufacturing data.

Objectives:

• Apply techniques learned throughout the program to datasets from the field of manufacturing.
• Analyze data from multiple real-world scenarios.
• Present findings using a visualization tool.
• Setup data transfer from a Programmable Logic Controller (PLC)-driven manufacturing system to a database using Kepware.
• Analyze data in a manufacturing optimization scenario.
• Analyze data in a manufacturing predictive maintenance scenario.

I would love to stay in touch with your developments and collaborate. We are just getting started and sounds like it would be mutually beneficial. We are also going through a BILT knowledge and skills analysis with our advisory board where they will be ranking the needed knowledge. This will include questions about manufacturing and statistical skills. We are very willing to share what we learn. 

I believe the advisory board is a great idea that we should also implement in our Data Science Professional Certificate program.  Great ideas from your project.  Thank you for sharing.

Dear Joyce, I would also love to see your occupational profiles examples and process.  I believe we would greatly benefit from your work.  Thank you for shaing.  

Carmen, thank you for your interest in the occupational profiles! Both can be found on the oceansofdata.org website. The Profile of the Data Practitioner is focused on the skills and knowledge for middle-skilled data workers and the Profile of a Big-Data-Enabled Specialist is our first and general occupational profile for an "individual who wrangles and analyzes large and/or complex data sets to enable new capabilities including discovery, decision support, and improved outcomes."

Because working effectively with data has become foundational across all industry sectors to perform technical work as well as to operate successful businesses, there are many more options for transition from a community college data analytics program into 4 year university programs.  We don't look only at transition into formal data science programs at the university level but also to business analytics, biosiences, financial services and other programs in which working with data is essential. It is becoming more widely recognized that having solid data skills today provides workers with a solid grounding and enables people to be more successful in all types of work.

To answer your question re challenge:  In my view the greatest challenge today is to help employers recognize the emergence of a middle skilled data workforce and seek graduates from community colleges as well as from universities to fill their data workforce needs.