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Big DataData ScienceData Science 101FeaturedUnderstanding Big Data

Data Science, Data Analytics, Data Everywhere Jargon can be downright intimidating and seemingly impenetrable to the uninformed. While complicated vernacular is an unfortunate side effect of the similarly complicated world of machines, those involved in computers, data and whole host of other tech-intensive sectors don’t do themselves any favors with

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Data ScienceData Science 101FeaturedMachine Learning

We hear the term “machine learning” a lot these days (usually in the context of predictive analysis and artificial intelligence), but machine learning has actually been a field of its own for several decades. Only recently have we been able to really take advantage of machine learning on a broad

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BI & AnalyticsFeaturedinfographicsResources

The world is increasingly digital, and this means big data is here to stay. In fact, the importance of big data and data analytics is only going to continue growing in the coming years. It is a fantastic career move and it could be just the type of career you

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Big DataData ScienceData Science 101FeaturedUnderstanding Big Data

If you are new to the field, Big Data can be intimidating! With the basic concepts under your belt, let’s focus on some key terms to impress your date, your boss, your family, or whoever. Let’s get started: Algorithm: A mathematical formula or statistical process used to perform an analysis of

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Data ScienceFeaturedMachine LearningResourcesTopics

In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I’ve observed that some actually lack the necessary mathematical intuition and

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ContributorsData NativesData Science 101Featured

Katharine Jarmul and Data Natives are joining forces to give you an amazing chance to delve deeply into Python and how to apply it to data manipulation, and data wrangling. By the end of her workshop, Learn Python for Data Analysis,  you will feel comfortable importing and running simple Python analysis on your

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Data Science 101FeaturedFinTech

Blockchain: the single most confusing term since Bitcoin. Everyone has a vague idea of what it does. It’s either the ultimate evolution of financial technologies, or a silly fad that can be summed up in the disconcerting phrase: “dogechain.” In reality, major companies around the world have already shown favor

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Data Science 101Featured

Pandas is the most widely used tool for data munging. It contains high-level data structures and manipulation tools designed to make data analysis fast and easy. In this post, I am going to discuss the most frequently used pandas features. I will be using olive oil data set for this

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Data ScienceFeaturedMachine Learning

Wiki Supervised Learning Definition  Supervised learning is the Data mining task of inferring a function from labeled training data.The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called

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BI & AnalyticsData Science 101FeaturedMachine Learning

One of most popular posts this year came from Ferris Jumah, a data scientist at LinkedIn, who mapped the most popular skills of data scientists by scraping LinkedIn profile data. One of the common comments amongst data scientists who came across this post- as with most of our posts focused

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