What does the real world of data science look like? | #WiDSConference
by Marlene Den Bleyker | Nov 13, 2015
https://siliconangle.com/2015/11/13/what-does-the-real-world-of-data-science-look-like-widsconference/
Working in the real world of data science is much different from studying it. Moreover, working as a data scientist in the industry doesn’t always mean the big picture is clear. Working with an independent advisor can expose and rectify these issues.
Monica Rogati, a data science advisor for Insight Data Science, spoke with Jeff Frick, cohost of theCUBE, from the SiliconANGLE Media team, at the Women in Data Science Conference, held at Stanford University, about advice she provides for academia and business clients.
Creating real-world data scientists
Rogati’s advice for academia to produce better data scientists is to create project-based learning courses. “Having real data, real projects, having to deal with messy data … being data detectives, I feel can contribute to preparing data scientists for the industry,” explained Rogati.
Acknowledging that there are many trade-offs when it comes to algorithm selections, Rogati’s preference is to bring new, fresh signals into your problem and consider other data to collect. She feels this will make the result and experience better. “Sometimes that can have a much bigger impact compared to your algorithm selection,” she said. “This is something we would like to see coming out of academia.”
Generally, students are handed data and required to stay within certain boundaries. Rogati clarified that when working in data science it is crucial to think outside the box and to determine what other signals apply. She noted that some people consider this cheating. However, she asserted that this is the right way to solve a problem.
Advice for industry
When Rogati advises clients where to begin with data, her recommendation is to think about the impact data will have on their business and to access the effort going into the project. Ideally, it is important to establish the best return on investment for the business.
She said that data scientists are usually excited about new methods and tend to focus on the latest technology and techniques, but they need to put it into perspective and work toward the ultimate goal. The key to starting a project is the ability to record data and anticipate eventual use cases.
When asked about the challenge of implementing change on the client level, she said, “It is necessary to show value through a careful choice of metrics, and then a lot of people come on board and understand data science.”
@Stanford #WomenInDataScience #WiDS @Stanford Video @SiliconANGLE theCUBE @theCUBE #theCUBE #WomenInTech
Forgot Password
Almost there!
We just sent you a verification email. Please verify your account to gain access to
Stanford Women In Data Science (WiDS) 2015 | Stanford, CA. If you don’t think you received an email check your
spam folder.
Sign in to Stanford Women In Data Science (WiDS) 2015 | Stanford, CA.
In order to sign in, enter the email address you used to registered for the event. Once completed, you will receive an email with a verification link. Open this link to automatically sign into the site.
Register For Stanford Women In Data Science (WiDS) 2015 | Stanford, CA
Please fill out the information below. You will recieve an email with a verification link confirming your registration. Click the link to automatically sign into the site.
You’re almost there!
We just sent you a verification email. Please click the verification button in the email. Once your email address is verified, you will have full access to all event content for Stanford Women In Data Science (WiDS) 2015 | Stanford, CA.
I want my badge and interests to be visible to all attendees.
Checking this box will display your presense on the attendees list, view your profile and allow other attendees to contact you via 1-1 chat. Read the Privacy Policy. At any time, you can choose to disable this preference.
Select your Interests!
add
Upload your photo
Uploading..
OR
Connect via Twitter
Connect via Linkedin
EDIT PASSWORD
Share
Forgot Password
Almost there!
We just sent you a verification email. Please verify your account to gain access to
Stanford Women In Data Science (WiDS) 2015 | Stanford, CA. If you don’t think you received an email check your
spam folder.
Sign in to Stanford Women In Data Science (WiDS) 2015 | Stanford, CA.
In order to sign in, enter the email address you used to registered for the event. Once completed, you will receive an email with a verification link. Open this link to automatically sign into the site.
Sign in to gain access to Stanford Women In Data Science (WiDS) 2015 | Stanford, CA
Please sign in with LinkedIn to continue to Stanford Women In Data Science (WiDS) 2015 | Stanford, CA. Signing in with LinkedIn ensures a professional environment.
Are you sure you want to remove access rights for this user?
Details
Manage Access
email address
Community Invitation
Monica Rogati, Data Collective - Stanford Women In Data Science 2015 - #WiDSConference #theCUBE
What does the real world of data science look like? | #WiDSConference
by Marlene Den Bleyker | Nov 13, 2015
https://siliconangle.com/2015/11/13/what-does-the-real-world-of-data-science-look-like-widsconference/
Working in the real world of data science is much different from studying it. Moreover, working as a data scientist in the industry doesn’t always mean the big picture is clear. Working with an independent advisor can expose and rectify these issues.
Monica Rogati, a data science advisor for Insight Data Science, spoke with Jeff Frick, cohost of theCUBE, from the SiliconANGLE Media team, at the Women in Data Science Conference, held at Stanford University, about advice she provides for academia and business clients.
Creating real-world data scientists
Rogati’s advice for academia to produce better data scientists is to create project-based learning courses. “Having real data, real projects, having to deal with messy data … being data detectives, I feel can contribute to preparing data scientists for the industry,” explained Rogati.
Acknowledging that there are many trade-offs when it comes to algorithm selections, Rogati’s preference is to bring new, fresh signals into your problem and consider other data to collect. She feels this will make the result and experience better. “Sometimes that can have a much bigger impact compared to your algorithm selection,” she said. “This is something we would like to see coming out of academia.”
Generally, students are handed data and required to stay within certain boundaries. Rogati clarified that when working in data science it is crucial to think outside the box and to determine what other signals apply. She noted that some people consider this cheating. However, she asserted that this is the right way to solve a problem.
Advice for industry
When Rogati advises clients where to begin with data, her recommendation is to think about the impact data will have on their business and to access the effort going into the project. Ideally, it is important to establish the best return on investment for the business.
She said that data scientists are usually excited about new methods and tend to focus on the latest technology and techniques, but they need to put it into perspective and work toward the ultimate goal. The key to starting a project is the ability to record data and anticipate eventual use cases.
When asked about the challenge of implementing change on the client level, she said, “It is necessary to show value through a careful choice of metrics, and then a lot of people come on board and understand data science.”
@Stanford #WomenInDataScience #WiDS @Stanford Video @SiliconANGLE theCUBE @theCUBE #theCUBE #WomenInTech
