Immersive AI Session Schedule

We are delighted to announce our schedule which lists ~50% of our sessions. Session times will be added in the coming week in addition to more Workshops and Career Mentoring talks. 
Keynote & Career Lab Talks
Friday, June 28th
-Saturday, June 29th
Friday, June 28th
-Saturday, June 29th
08:45
Resume Review by Merrimack College
Resume Review by Merrimack College image
Merrimack College
08:45
Resume Review by Boston College
Resume Review by Boston College image
Boston College
09:00
From Healthcare to Life Insurance: Transitioning Domains in Data Science

Mentor Talk

 

In order to do data science, one needs skills in math, statistics, and computer science. In this talk, I will take you through how while these skills create the technical foundation for a data scientist’s work, other highly important transferable skills include critical thinking and problem solving, data visualization and the ability to translate and present quantitative data into actionable business insights. From having worked in healthcare, finance and life insurance, I discuss how the idea is more about the fundamental technical problem, scientific investigative skills, creativity and the story than about the field...more details

From Healthcare to Life Insurance: Transitioning Domains in Data Science image
Bisakha Peskin, PhD
Lead Data Scientist, Assistant Vice President | Massachusetts Mutual Life Insurance Company
09:30
Building A Better Data Science Resume

Mentor Talk

 

Coming soon!..more details

Building A Better Data Science Resume image
Sheamus McGovern
Chairman & Founder | ODSC
10:00
How to Succeed at Data Science Jobs That Don’t Exist Yet

Mentor Talk | Beginner

 

In 2019, every company is a technology company whether they like it or not. Regardless of industry or vertical, future successful organizations will be required to embrace the power of data science. From Robotic Process Automation to AI, prescriptive analytics to machine learning algorithms – data science is being incorporated into every aspect of business regardless of discipline or geography.
While this represents tremendous opportunities for data scientists, it also comes with challenges. Because the field is evolving so rapidly, it means that data scientists will have to stay ahead of the curve in order to be successful. They will need to constantly be aware of trending and understand how their skills are going to map to what is in demand, what employers are paying for.
Today’s economic climate also requires data scientists to understand the business value of data science as companies develop unique, innovative and monetizable portfolios. These include but are not limited to areas like:
• new mobility models (autonomous vehicles, ride-sharing, Hyperloop)
• genetic engineering (CRISPR, immunotherapies)
• extraterrestrial travel (taking cargo and humans to the ISS, the Moon, Mars)
• robotics (supply chain to surgery)
• financial services (cryptoassets, blockchain)
• media and entertainment (AR/VR, mixed reality)
In my session, I will help attendees better understand career and learning paths for data science. I will start with socio-historical context, and then describe trending and future opportunities across a range of sectors and verticals. I will also share concrete guidance on how to stay on top of these trends using my three Future Career Tools – Voice, Antenna and Mesh…more details

How to Succeed at Data Science Jobs That Don’t Exist Yet image
Christopher Bishop
TEDx Speaker, Chief Reinvention Officer | Improvising Careers
11:00
A Career Path to Data Science Through Privacy & Confidentiality

Mentor Talk

 

Most empirical work involves data on units such as individuals, households and/or organizations of various types. In circumstances such as these, analysts must ensure that such data are used responsibly and ethically. In practical terms, this requires that the private interests of individual privacy and data confidentiality be balanced against the social benefits of access and use.

It is critical to address privacy and confidentiality issues if the full public value of data is to be realized. This presentation will highlight why the challenges need to be met; review the past, point out challenges with this approach in the new data world; briefly describe the current state of play from a legal, technical, and statistical perspective; and point to open questions that need to be addressed in the future…more details

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Julia Lane, PhD
Professor | NYU Center Of Urban Science And Progress
11:30
Picking The Right Program: Formats, Credentials, and MOOCs, Oh my!

Given the proliferation of options for education in data analytics and data science, it is not easy to choose the right program to help one achieve his/her goals. Credit vs. non-credit; degree vs. non-degree; online vs. face-to-face vs. hybrid; quick vs. protracted are all questions facing those that want to further their education. In this session, I will help you learn what questions to ask of different programs in order to determine the best fit for YOU.

Picking The Right Program: Formats, Credentials, and MOOCs, Oh my! image
Dr. Aleksandar Tomic
Associate Dean for Strategy, Innovation, and Technology | Boston College
12:00
Transitioning Your Career To Data Science In Quant Finance

Mentor Talk

 

Quantitative finance is a rich field in finance where advanced mathematical and statistical techniques are employed by both sell-side and buy-side institutions.
Machine learning techniques are now being increasingly used by financial firms for generating profitable trading strategies and for automating various processes. Quants typically have background in hard sciences and mathematical finance. In addition to classical techniques like derivatives modeling, asset allocation etc. quants now need a good understanding of machine learning models and statistical methods. Experience in deep learning techniques for text and image processing is required for handling unstructured datasets (also called alternative data).

Data scientists transitioning into quant finance should develop a solid foundation in financial concepts and business knowledge. Data visualization and tools to explain how the models work under the hood are also crucial. Python is becoming the language of choice for scientific computing and machine learning. Data scientists should develop strong computing skills with focus on data analysis, storage and handling of unstructured datasets…more details

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Chakri Cherukuri
Senior Quantitative Researcher | Bloomberg LP
12:20
Data Science Management Fundamentals: My Perpetual Work-in-Progress

Keynote

 

Data Science as a professional discipline is still quite young. As such, much of the collective effort thus far has been dedicated to codifying the technical approaches to building data science tools and products. The long-term success of the discipline, however, will be highly dependent on our ability to manage teams and build career paths. In this talk, Drew will discuss three core components of data science management: recruiting and hiring processes; project definition and execution; and people management and performance reviews. ..more details

Data Science Management Fundamentals: My Perpetual Work-in-Progress image
Drew Conway, PhD
Senior Vice-President | Two Sigma
13:30
From Political Science to Data Science: How I Learned to Build the Right Data Science Career for Myself

Mentor Talk

 

Ever since Harvard declared “Data Scientist” the sexiest job of the 21st century, there’s been a mad rush to build the coding and statistics skills to get this title. Yet the focus on being a ‘scientist’ ignores the myriad of other roles and skills that are important in every data science team. In this talk, I’ll take you through how I built a career that allows me to leverage all my strengths – not just the most technical ones. Hopefully, by the end of the talk you’ll gather that being in data science isn’t just for math and computer wizards- but also for subject matter experts, great communicators, and even product people, and will walk away with the steps to forge your own path moving forward…more details

From Political Science to Data Science: How I Learned to Build the Right Data Science Career for Myself image
Triveni Gandhi, PhD
Data Scientist | Dataiku
14:00
How Contributing to Open Source Can Help Advance Your Career

Mentor Talk

 

Coming soon!…more details

How Contributing to Open Source Can Help Advance Your Career image
Thomas Fan
Software Developer - Machine Learning | Columbia Data Science Institute
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Schedule Summary

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