Top discussion forums for data scientists
Data science and analytics deal with complex mathematical and statistical concepts, different programming languages and complicated algorithms. While starting in data science or even well into the career, one might encounter several instances where they are stuck with a business problem or concept. In such situations, a discussion forum or community can be a saviour. A forum with professionals and enthusiasts connected globally can help suggest quick solutions, generate a productive discussion, and raise the knowledge level for participants. This article looks at some popular data science forums that every analytics professional should be a part of. MachineHack MachineHack, a popular choice for data science, AI and ML professionals to assess their skills and compete with others, also has a discussion forum. The MachineHack Discussion forum is a community-driven space to answer any technical challenges in machine learning, artificial intelligence, data science, and data engineering. It focuses on important topics every data science professional should know—from salary ranges to technical doubts (such as splitting datasets into training and test sets, merging two Pandas data frames, etc.) For more details, click here. Global Data Science Forum – IBM Data Science Community The IBM Community is where users share and discuss their know-how on IBM products. It has different communities for data science, security, middleware and others. It is a very active platform where members share insights and experiences to help others. The Global Data Science Forum has around 20,000 members, 210 libraries and 527 blog posts. Data science professionals can discuss any technical difficulties they are facing. For more details, click here. Machine Learning & Data Science Forum Discussions | Kaggle Kaggle is one of the most popular platforms for data scientists as it contains huge datasets data scientists can work on, build models and get a real-world feel. It also has a vibrant and active forum to discuss all data science related issues. It works through the simple mechanism of feedback, asking questions and peers answering them. The discussions can range from preliminary topics to advanced topics in NLP, computer vision, neural networks, visualisation and many more. For more details, click here. Discussion Forum – Data Science Central Data Science Central is a community for big data practitioners. It conducts discussions on technical topics (covers deep, specialised knowledge of the technical aspects of data science), business topics, and different sectors (on topics associated with the specific industry or societal sectors). It also contains information and discussions on programming languages that focus primarily on coding techniques in various languages. For more details, click here. Reddit: r/datascience Reddit can be a great source to clarify all your doubts and queries regarding data science. As a popular site, people are quite active there, and you can expect very quick responses to your questions. This can be a huge time saver for people looking for solutions almost instantly. Currently, the discussion forum has over six lakh members. Popular topics of discussion here are data science career options, project discussions, job listings, among others. For more details, click here.
We look at some popular data science forums that every analytics professional should be a part of.
We look at some popular data science forums that every analytics professional should be a part of.
["AI (Artificial Intelligence)", "Data Science", "Data Scientist", "Discussion", "Machine Learning", "review", "skills"]
["data science", "artificial intelligence", "machine learning", "AI", "neural network", "ML", "Machine Learning", "computer vision", "Data Scientist", "NLP", "analytics", "skills", "review", "Data Science", "Discussion", "AI (Artificial Intelligence)", "Pandas"]
["AI", "artificial intelligence", "machine learning", "ML", "neural network", "NLP", "computer vision", "data science", "analytics", "Pandas"]
I am a technology journalist at AIM. What gets me excited is deep-diving into new-age technologies and analysing how they impact us for the greater good.

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