Windowing functions in Hive are super useful. They make analysis that would otherwise be challenging, much easier. Let’s look at examples of the key windowing functions in Hive. We will use the below dataset for our analysis, this is the ‘payments’ table. customerid department payment_amount 1 1 10000 2 1 9000 3 2 12000 4 […]Read more
SQL is one of the most in-demand data skills. The language has been adopted by many database platforms, including Apache Hive. This article will serve as a crash couse into the key functionality of Hive QL. Throughout this article, we will use the two sample tables as the basis for our code. THE CUSTOMERS TABLE […]Read more
When you look online at what it takes to become a data scientist, it’s enough to make your brain melt. You have people telling you that you need to be an expert statistician / mathematician; you need to be a top-level coder in 15 different languages; be proficient with every type of SQL/NoSQL database on […]Read more
Agile is about iterative development and delivering tangible products/features quickly, which provides the business with value and ROI faster than a traditional waterfall project. Consider the example of a piece of accounting software. Overall, it’s going to have 50 features to support the accounts team. To deliver all of the features in a waterfall fashion, […]Read more
As data scientists, we need to be comfortable with mathematics. If you Google what you need to know, you’ll find answers stating you need to fully understand linear algebra; calculus and how to calculate all of the algorthms we use by hand. I’m not going to downplay the importance of understanding how the algorithm works, […]Read more
Often, you’ll be looking around on the web for an answer to a question you have about an algorithm & you are presented with a formulae-heavy answer on a forum. If you don’t know the notation, this is going to give you a headache. So this article aims to cover off much of the common […]Read more
Regression is about finding the relationship between some input variable(s) and an outcome. Let’s think about a simple example of height and weight. We need to understand the relationship between the two – intuition can tell us that as height increases, so does weight. The idea of regression is to create a mathematical formula which […]Read more
Let’s say, we’re working for a retailer. They have two distribution centres in Australia – the geographic area covered by each distribution centre is coloured in orange. Our task is to, from this image, work out to how many square kilometers of Australia can we deliver and what percentage of the country is that? Right, […]Read more
Welcome back to the series on timeseries analysis. In this article, we’re going to discuss: plotting timeseries data and smoothing the data to handle outliers & make finding a trend a little bit easier. In the below, I have ingsted the timeseries data into a dataframe called df. From that dataframe, I have then set […]Read more
The purpose of time series analysis is to analyse timeseries data and extrapolate the patterns you identify into the future, enabling us to make predictions. It’s important to understand when we can use timeseries analysis. If the timeseries has a clear pattern, trend and seasonality then you can accurately model a forecast. If a dataset […]Read more
Pattern matching can be really very useful in large bodies of text. Yes, you could, for the most part, use a regular expression to achieve the same thing; but I tend to find the Spacy functionality a lot more straightforward – afterall, nobody enjoys the syntax of a regular expression. Further to this, Spacy makes […]Read more
In this article, we are going to look into an unsupervised machine learning model, which aims to cluster documents into topics – the algorithm is called Latent Dirichlet Allocation (LDA). Essentially, we can assume that documents which are written about similar things, use a similar group of words. LDA tries to map all the documents […]Read more
Text classification is all about classifying a body of text, without actually reading it. It’s a supervised machine learning algorithm which uses term frequency (how often words occur) to classify the document. The classic example is to determine whether an email is spam or not. To do this, we can ingest a dataframe which includes […]Read more
Recently, I have been presented with some problems which require Natural Language Processing to solve. Natural Language Processing (NLP) is a field of Artificial Intelligence which enables a machine to interpret human text for the purpose of analysis – for example, understanding the main topics discussed in a document or how positive customers reviews are […]Read more
As data engineers and data scientists, we’re spend a lot of time exploring data. When you’re working with huge datasets, you may find you need to utilise Apache Spark or similar to conduct this exploratative analysis but for the majority of use-cases, Pandas is the defaqto tool we choose. The Pandas library has been so […]Read more
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Hi, I’m Lillie. Previously a magazine editor, I became a full-time mother and freelance writer in 2017. I spend most of my time with my kids and husband over at The Brown Bear Family but this blog is for my love of food and sharing my favorites with you!
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