Imagine you own a restaurant and you want to analyze not only the trend of your revenue, but also the reason behind periods of particularly high earnings, moment of the day where a particular kind of clients comes to your restaurant, why some days tips are higher than others and so on. Knowing all those …
Continue reading “Interactive analytics and predictions on Restaurant tips”
In most of my previous articles, I’ve often been stretching the importance of visualizing the results obtained by a technical analysis. Ideally, your charts should be able to summarize in a glimpse what you have been working on for days. Plus, those charts have to do so in a way which is clear and comprehensible …
A time series is a series of data points indexed in time order, normally with equally spaced points in time. Examples of time series are stocks’ prices, monthly returns, company’s sales and so forth. Time series can be seen as data with a target variable (price, returns, amount of sales…) and one feature only: time. …
Continue reading “Time Series: why do we need Stationarity and Ergodicity”
In my previous article (Part 1 of this series), I’ve been implementing some interesting visualization tools for a meaningful exploratory analysis. Then, with the Python package Streamlit, I made them interactive in the form of a web app. In this article, I’m going to continue working on the same dataset as before, this time focusing …
Data Science and Analytics have a huge variety of fields of applications, basically every time pieces of information are delivered in the form of data. The sports industry makes no exception. There is a great business all around, and having the possibility to study the market of sports via powerful analytics tools is a great …
In my previous article, I’ve been providing an introduction to some useful graphical tools available in Plotly, an opensource library which can be used both in Python and R. Here, I’m going to play a bit more with Plotly’s functionalities, using as input some data about USA exports in 2011. So let’s import and explore …
Python is a general purpose language and, as such, it offers a great number of extensions which range from scientific programming to data visualization, from statistical tools to machine learning. It is almost impossible knowing every available extension, however there are a few of them which are pivotal if your task consists of analyzing data …
Continue reading “5 Python Packages a Data Scientist can’t live without”
Whenever you are about to inspect and manage some data, one of the first inconvenient which might arises is the presence of some missing values. Together with eventual outliers, they might affect the robustness of your Machine Learning model, it is worth spending some extra time during your cleaning procedure and investigating about the nature …
The world of social networks could be considered, today, one of the largest free data source available in the market. When you think about Big Data, probably the first example that comes to your mind is Twitter. Like many other social networks, Twitter allows its users to post, comment, like and follow, to express their …
Today, if you are about to order some foods on a restaurant’s website or you need assistance because your router is not working properly, you will probably get in touch with a chatbot. They appear to you like instant messaging chats, in one of the corners of the screen, and gently ask you whether you …
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