Top 10 Data Science Leaders to Follow on LinkedIn in 2023

Top 10 Data Science Leaders to Follow on LinkedIn in 2023

LinkedIn News

Data Science has become an integral part of our lives, revolutionizing various aspects of technology and simplifying our daily activities. If you are a beginner in data science and want to stay updated with the latest trends and innovations, following industry experts on LinkedIn can be extremely helpful. Here, we highlight 2023’s top 10 data science leaders whom you can follow on LinkedIn for valuable insights and knowledge.

1. JOSE MARCIAL PORTILLA: Jose is the Head of Data Science at Pierian Data Inc. He is known for his Python for Machine Learning and Data Science BootCamp. With his expertise, he provides consultation services and offers informative courses to help beginners in data science.

2. KOO PIN SHUNG: Koo is a co-founder of DataScience SG and a prominent figure in the data science community. He often speaks about ethics in Artificial Intelligence, leveraging Big Data for business, and the best resources for data scientists.

3. FEI FEI LI: Fei-Fei is a professor of data science at Stanford University and a driving force behind the ImageNet project. With over 180 papers in data science, she has made significant contributions to the field. Fei-Fei is interested in creating intelligent healthcare systems based on cognitive machine learning, computer vision, and AI.

4. BEN TAYLOR: With 13 years of experience in Machine Learning, Ben is a data scientist with expertise in domains like natural language processing, deep learning, and data science. He specializes in genetic programming and automated network design.

5. ERIC WEBER: Eric is the Head of Experimentation at Yelp and teaches the mindset of a data scientist. He emphasizes the importance of balancing theory and practical applications in data science. With an MBA degree, Eric frequently appears in podcasts, conferences, and webinars related to data science.

6. KEVIN TRAN: Kevin, a Stanford University graduate, has over 7 years of expertise in data science and engineering. He has contributed significantly to projects like Scikit-learn, pandas, and Matpotib. Currently working as a senior data scientist, Kevin has made notable contributions to companies like Dropbox, Google, and Analyst.

7. GEOFFREY HILTON: Geoffry Hilton, known as the Godfather of Deep Learning, has done exceptional work in the field of neural networks. With a PhD in AI and extensive experience at the University of Toronto and Google, he is a leading figure in deep learning research.

8. DEAN ABBOTT: Dean is the Co-founder and Chief Data Scientist of SmarterHQ. He has pioneered innovative data science solutions for business and research, including machine learning models for fraud detection systems and survey analysis. Dean runs the Abbott Analytics website and is a renowned expert in data mining.

9. MERV ADRIAN: With over 30 years of expertise in the IT industry, Merv serves as the Vice President of Gartner. He has worked at Microsoft as a lead analyst and actively advocates for open-source machine learning technologies. Merv has extensive experience with various machine learning platforms, such as Spark, relational and non-relational DBMS, and Apache Hadoop.

10. ANDREW NG: Andrew Ng is a renowned data scientist who previously served as the Chief Data Scientist at Baidu and led the Google Brain Project. He is the creator of Landing AI and specializes in applying Deep Learning algorithms to real-world business problems. Through Landing AI, Andrew provides SaaS products and AI solutions to help businesses meet their customers’ expectations.

Following these data science leaders on LinkedIn can provide you with valuable insights, knowledge, and the opportunity to stay updated with the latest trends and innovations in the field.

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– Data Science: A multidisciplinary field that combines scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
– Machine Learning: A branch of artificial intelligence that enables computer systems to learn and improve from experience without being explicitly programmed.
– Big Data: Large and complex data sets that cannot be processed using traditional data processing techniques.
– Artificial Intelligence: The simulation of human intelligence processes computer systems, including the ability to learn, reason, and make decisions.
– Cognitive Machine Learning: A subfield of artificial intelligence that focuses on developing intelligent systems capable of understanding, reasoning, and learning from and adapting to human-like cognitive processes.
– Computer Vision: A field of artificial intelligence and computer science that focuses on enabling computers to interpret and understand visual information from digital images or videos.
– Neural Networks: A series of algorithms that mimic the human brain’s structure and function, enabling computers to recognize patterns and make decisions based on data and experience.
– Data Mining: The process of discovering patterns, relationships, and insights from large datasets using techniques from fields such as statistics, machine learning, and database systems.
– Deep Learning: A subfield of machine learning that focuses on developing algorithms inspired the structure and function of the human brain’s neural networks.
– Open Source: Software or technologies that are freely available and can be modified and distributed anyone.]

Note: The definitions provided are general explanations and may not cover all nuances and details of the terms mentioned.