Work with thought leaders and academic experts in Data science

Companies can benefit from collaborating with academic researchers in the field of Data science in several ways. These researchers have deep knowledge and expertise in data analysis, machine learning, and statistical modeling. They can help companies gain valuable insights from their data, identify patterns and trends, and make data-driven decisions. Academic researchers can also assist in solving complex problems by developing advanced algorithms and models. Their expertise can be particularly useful in areas such as predictive analytics, fraud detection, and optimization. Furthermore, collaborating with academic researchers can drive innovation by exploring new techniques and methodologies, pushing the boundaries of what is possible in data science.

Researchers on NotedSource with backgrounds in Data science include Christos Makridis, Suhas Chelian, Jo Boaler, Dr. Justin Whalley, Ph.D, Adam Kimbler, Hector Klie, Anirudha Menon, Magdalena Masello, DVM, PhD, Ayse Oktay, Anindya Ghose, Giuliana Noratto, and Mengying Li.

Christos Makridis

Nashville, TN
Web3 and Labor Economist in Academia, Entrepreneurship, and Policy
Research Expertise (16)
Finance
Economics and Econometrics
Accounting
Pharmacology (medical)
Law
And 11 more
About
Christos A. Makridis holds academic appointments at Columbia Business School, Stanford University, Baylor University, University of Nicosia, and Arizona State University. He is also an adjunct scholar at the Manhattan Institute, senior adviser at Gallup, and senior adviser at the National AI Institute in the Department of Veterans Affairs. Christos is the CEO/co-founder of [Dainamic](https://www.dainamic.ai/), a technology startup working to democratize the use and application of data science and AI techniques for small and mid sized organizations, and CTO/co-founder of [Living Opera](https://www.livingopera.org/), a web3 startup working to bridge classical music and blockchain technologies. Christos previously served on the White House Council of Economic Advisers managing the cybersecurity, technology, and space activities, as a Non-resident Fellow at the Cyber Security Project in the Harvard Kennedy School of Government, as a Digital Fellow at the Initiative at the Digital Economy in the MIT Sloan School of Management, a a Non-resident Research Scientist at Datacamp, and as a Visiting Fellow at the Foundation for Defense of Democracies. Christos’ primary academic research focuses on labor economics, the digital economy, and personal finance and well-being. He has published over 70 peer-reviewed research papers in academic journals and over 170 news articles in the press. Christos earned a Bachelor’s in Economics and Minor in Mathematics at Arizona State University, as well a dual Masters and PhDs in Economics and Management Science & Engineering at Stanford University.

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Example Data science projects

How can companies collaborate more effectively with researchers, experts, and thought leaders to make progress on Data science?

Customer Segmentation for Retail

An academic researcher can collaborate with a retail company to develop a customer segmentation model. By analyzing customer data, the researcher can identify distinct customer segments based on demographics, purchasing behavior, and preferences. This information can help the company tailor marketing strategies, personalize customer experiences, and optimize product offerings.

Predictive Maintenance for Manufacturing

In collaboration with an academic researcher, a manufacturing company can develop a predictive maintenance system. By analyzing sensor data from machines, the researcher can build models to predict equipment failures and recommend maintenance actions. This can help the company reduce downtime, improve operational efficiency, and save costs.

Churn Prediction for Telecom

An academic researcher can work with a telecom company to develop a churn prediction model. By analyzing customer data, the researcher can identify factors that contribute to customer churn and build a predictive model to forecast churn probability. This can enable the company to proactively retain customers, improve customer satisfaction, and reduce revenue loss.

Demand Forecasting for E-commerce

Collaborating with an academic researcher, an e-commerce company can develop a demand forecasting model. By analyzing historical sales data, the researcher can build a model to predict future demand for different products. This can help the company optimize inventory management, plan production, and improve customer satisfaction.

Sentiment Analysis for Social Media

An academic researcher can collaborate with a social media company to develop a sentiment analysis system. By analyzing user-generated content, such as tweets and comments, the researcher can classify sentiment as positive, negative, or neutral. This can help the company understand customer opinions, monitor brand reputation, and improve marketing strategies.