## A comprehensive guide to windowing functions in PySpark for data science

Window functions are incredibly useful. Within a single query, you can find out things which may have otherwise been tricky. In this article, I will cover all of the key window functions in Pyspark. First off, we need to define our dataframe – you can get the data to play along here. Now, we have […]

Read more## A guide to windowing functions in Hive for data analysis

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## The Hive SQL Crash Course For Data Analysts

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## The data scientist learning plan for 2021

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## Can we successfully implement Agile in data science?

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## The Data Scientist Statistics Learning Plan For 2021

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## A Guide To Basic Linear Algebra Notation For Machine Learning

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## The Ultimate Guide To Linear Regression For Aspiring Data Scientists

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## A Simple Way To Analyse Image Pixels And Make Inferences From Images For Data Engineers

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## How Do Data Scientists Deal With Outliers In Timeseries Analysis To Reveal Trends and Patterns?

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 Basics of Timeseries Analysis – Stationarity and Autocorrelation For Data Scientists

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 In Large Bodies Of Text Using Spacy’s NLP Capabilities For Data Scientists

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 […]

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