Call for Speakers: DATAcated Industry Series

Data science has applications in a wide range of industries, including healthcare, food, finance, and retail. These are the four industries that we are focused on as part of the DATAcated Industry Series in 2023; we'll host one industry per quarter.

APPLY TO SPEAK: Do you want to submit a presentation for any of the four conferences? Here is your chance - use this quick form to submit a talk and we'll reach out if the talk is relevant to the sessions.

We are seeking speakers who are able to deliver a 10-minute presentation on data analytics use cases, best practices, challenges, or success stories in the relevant industry. This can include sharing real-life examples of how data analytics has been successfully applied, or discussing the challenges and lessons learned from using data analytics in this industry.

DATES: Here is the breakdown of the schedule for these events:

• DATAcated Health: March 8, 2023
• DATAcated Food: June 6, 2023
• DATAcated Finance: September 12, 2023
• DATAcated Retail: December 6, 2023

They start at 11am ET and last about 4 hours each.

HEALTH: In healthcare, data science can be used to analyze patient data to identify patterns and trends, predict outcomes, and improve patient care. For example, data scientists can use machine learning algorithms to predict which patients are at risk of developing a particular disease, or to personalize treatment plans based on a patient's individual characteristics and medical history.

FOOD: In the food & beverage industry, data science can be used to optimize production and supply chain processes, improve food safety, and enhance the customer experience. For example, data scientists can use data on consumer preferences and purchasing behavior to design more effective marketing campaigns, or to optimize the placement and pricing of products in stores.

FINANCE: In finance, data science can be used to analyze market trends, identify investment opportunities, and reduce risk. For example, data scientists can use machine learning algorithms to predict stock prices or to detect fraudulent activity in financial transactions.

RETAIL: In retail, data science can be used to optimize pricing and inventory management, improve customer experience, and personalize marketing efforts. For example, data scientists can use data on consumer behavior and preferences to recommend products to customers or to optimize the placement of products in stores.

Data science has the potential to transform many industries and has the ability to significantly improve decision-making and efficiency. We can't wait to hear from the very best speakers in each industry!

Here are some sample talks we'd love to include in the conference series:

• Predictive analytics in healthcare: Using machine learning algorithms to predict patient outcomes or to identify high-risk patients.
• Personalized medicine: Using data analytics to tailor treatment plans to an individual's specific characteristics and medical history.
• Supply chain optimization: Using data analytics to improve efficiency and reduce waste in the food industry.
• Food safety monitoring: Using data analytics to detect and prevent foodborne illness outbreaks.
• Customer segmentation and targeting: Using data analytics to understand consumer behavior and personalize marketing efforts in retail.
• Inventory management: Using data analytics to optimize inventory levels and reduce waste in retail.
• Fraud detection: Using data analytics to identify fraudulent activity in financial transactions.
• Risk assessment: Using data analytics to identify and mitigate risk in the financial industry.
• Customer loyalty programs: Using data analytics to design and evaluate customer loyalty programs in retail.
• Price optimization: Using data analytics to optimize pricing strategies in retail.

Make sure you register for the events here.

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