Data science influencers (data scientists) are delivering industry thought leadership about leveraging data as a critical business asset, and the development of new technologies such as artificial intelligence, natural language processing, and predictive analytics are making it easier for organizations to access real business insights from their data.
Data Science Influencers of 2023
Data science is an interdisciplinary field that combines mathematics, computer science, and other scientific disciplines to extract knowledge from data. It involves the application of various methods, such as machine learning algorithms, natural language processing (NLP), and statistical analysis, to uncover patterns in large datasets.
Data scientists can use advanced analytics techniques such as predictive models, clustering algorithms, and deep learning networks to discover meaningful insights that inform decisions in various industries. As a result, data science has become an integral part of business operations. Today, its applications range from medical diagnosis to customer segmentation to fraud detection.
Data science is essential for businesses because it enables them to make better decisions based on data-driven insights. Data analytics can help companies identify areas of opportunity, understand customer behavior and trends, and gain a competitive edge in the market.
Data scientists use their expertise to extract real value from data by uncovering patterns and relationships that otherwise remain hidden. This helps businesses increase efficiency, optimize operations, reduce costs, and improve products or services. Therefore, data science has become essential for driving innovation and staying ahead of the competition.
How were these data science influencers ranked?
My methodology for identifying and measuring influence differs slightly from others in the industry. For the most part, power is measured using three core social data points-reach, relevance, and resonance:
- Reach: Community size. Essentially it is an aggregate sum of an influencer’s audience across all channels.
- Topical Relevance: How often is the influencer discussing and mentioning the core topics across their social channels? Was it a mention in a YouTube video three years ago, or do they consistently talk about it in the media where they participate the most?
- Resonance: Engagement. When they mention the topics in question, are audiences engaging with the content? Is it resonating with them?
I want to add a layer of influencer analytics, but it’s somewhat of a manual process. I like to call it a reference, and it answers the following questions:
- Do other influencers reference, follow or link to the influencer?
- Has the influencer been mentioned in any media coverage?
- Is the influencer mentioned by a specific audience important to the brand?
Unfortunately, some of this analysis must be done manually, but this is good. It requires spending some time in the data and providing contextual insights as to why you chose this influencer.
Data Science Influencers 2022
Below are the top data science influencers from 2022.
Using the same methodology above, we created additional technology influencer lists below. Don’t forget: Use these lists as a starting point.