Why Kaggle will NOT make you a great data-scientistPranay DaveBlockedUnblockFollowFollowingDec 6Want to be an Eagle or Kaggle data scientist ?There is no doubt that Kaggle is a great place to learn data science..There are many data scientists who invest a lot of time in Kaggle..That is fantastic.But you should not rely only on Kaggle to learn data science skills.And here are the reasons whyData science is not only about predictionKaggle focusses only on problems which require to predict something..These type of problems require one to understand different data types and customer touch points such as web navigation, billing, call-center interactions, store visits ..These type of problems cannot be solved only with predictive algorithms..It is nice to know how to solve predictive problems, but as a data scientist you are expected to solve multiple type of problems..You will have to look outside Kaggle to develop skills on solving real world data science challengesYou will not develop skills on Graph AlgorithmsSocial Network Analysis, Influencer prediction, community analysis, fraudster network analysis — all these are very interesting analytical problems which a data scientist is required to solve..These type of problems require knowledge on graph algorithms such as Pagerank, Modularity, ShortestPath, EigenVectorCentrality and many moreNetwork or community type problems are rare in Kaggle..The graph and network style problems require notion of data of nodes and links, which is not the way most of the data are available in Kaggle.Of course you can convert a problem to use graph algorithms, but it is rare..Absence of such type of competitions represent a huge gap between Kaggle and kind of problems which the data scientist are expected to solve in enterpriseYou will not put effort in Algorithm ExplicabilityExplicability of algorithms is becoming very important..Have a vantage view of different type of data science problems.. More details