What data scientists can learn from qualitative research Open-ended survey questions often provide the most useful insights, but if you are dealing with hundreds or thousands of people’s answers, summarising them will give you the biggest headache. If you are…

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How to Use Machine Learning Algorithms in Weka: A big benefit of using the Weka platform is the large number of supported machine learning algorithms. The more algorithms that you can try on your problem the more you will learn…

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Quantifying Productivity I’m always on a lookout for interesting datasets to collect, analyze and interpret. And what better dataset to collect/analyze than the meta-dataset of my own activity collecting/analyzing other datasets? How much time do I *really spend working per…

### 4 Major Trends Disrupting Data Science Market

4 Major Trends Disrupting Data Science Market The evolution of data science, the maturation of data scientists, and the disruption taking place in many of the industries in which they work all raise the question – where do we go…

### Monitoring Correlation and Clustering

Monitoring Correlation and Clustering: Finding metrics with similar behavior and analyzing internal system dependencies. There are a lot of situations when you see an unexpected change in one metric (e.g. increased latency or error rate) and need to find the…

### Recommending Subreddits by Computing User Similarity: An Introduction to Machine Learning in Python

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### Brief Primer on Linear Regression Part II

Brief Primer on Linear Regression Part II In the first part, we had discussed that the main task for building a multiple linear regression model is to fit a straight line through a scatter plot of data points in multidimensional…

### Brief Primer on Linear Regression Part 1

Brief Primer Linear Regression Part 1 Prediction has always been a curious topic in life due to a key attribute – the extreme human desire to know what is coming next. Let’s ponder over our thoughts to answer a simple…