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Data Science

This Category is all about Data Science. : – )

[Internal] Mapping the Updated Sheet with Orignal File | IBV Self Service Tool

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Now, once we have our sheet ready with updated labels. The next step is the getting the required file all set for marketsight by making the updated sheet with original file from IBM Self Service Tool here; http://ibmibv117.com/svnweb/sss/ Here, we will map the updated sheet that is ready after doing the required changes like update labels etc…

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JumpStart with Hadoop

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Motivation; Whenever we will hear about Big Data implementation and its tools, we would definitely hear about Hadoop community. According to IBM analytics, some companies are delaying data opportunities because of organizational constraints. Others are not sure what distribution to choose and still, others simply can’t find time to mature their Big Data delivery due to…

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JumpStart Programming with Python 3

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Motivation; Well, if you want to play with data and deal with complex analytics problems then Python is the best for you. We can use Python for developing complex scientific and numeric apps. Python is designed with features to facilitate data analysis and visualization. The syntax in Python helps the programmers to do coding in fewer steps as compared…

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Understanding of Data Science Methodology

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Motivation: It’s all about the different methods used in data science. Data Science Methodology: There is the following methodology used in data science which can further categories into different phases; From Problem to Approach Business Understanding Analytical Approach Working with Data Data Requirements Data Collection Data Understanding Data Preparation Deriving the Answer Modeling Evaluation Development Feedback…

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Hands-On with Supervised & Unsupervised Learning | Machine Learning

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Motivation In Machine Learning, all types including supervised, unsupervised and reinforcement learning have their own way of implementation. Let’s do hands-on with them; Supervised Learning It’s the “Task Driven” (Predict next value). Here, we teach the model! then with that knowledge, it can predict unknown or future instances. Let’s implement the simple code in the jupeter…

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