Here we present several projects/tutorials that illustrates how data science techniques are applied and how they can benefit businesses.
These projects were inspired on projects developed by data science and entrepreneurship master students of JADS at JADS MKB Datalab while helping many SME's becoming more data driven.
Web scraping allows us to retrieve data stored on web pages across the Internet. This is a very handy skill for a data scientist since sometimes the data we need is not available via APIs or datasets.
In this project/tutorial we show that is possible to web scrape using few knowledge on HTML and with help
of Python packages requests and BeautifulSoup. We extract
text, information from tables, and also from hyperlinks within a webpage.
Since data is what we get from web scraping the applications are endless. They go from a business, to governmental, until personal applications.
In this tutorial we give an overview of how to use APIs (Application Programming Interfaces) to retrieve data. Via API one is able to retrieve real time data as well as historical data. An example of interesting business application involves combining real time data together with historical data to predict demand of products.
We start this tutorial with some basics on JSON (JavaScript Object Notation) files which are standard form for transferring data through APIs. After that we start exploring some APIs starting by a simple one: The Open Movie database (OMDb) API. Then we take a look on how to get information from both NS and KNMI Weer APIs (Weerlive and meteoserver). To close it we check out how to pull data from Twitter.
Repository | Blog part 01 | Blog part 02 | Blog part 03
Time series analysis deals with data that is ordered in time. Time series data is one of the most common data types and it is used in a wide variety of domains: finance, climate, health, energy, governance, industry, agriculture, business etc. Being able to effectively work with such data is an increasingly important skill for data scientists, especially when the goal is to report trends, forecast, and even detect anomalies.
In this tutorial we introduce some basics on time series analysis. We also give an overview some important models used to forecasting: ARIMA models and its variants, as well as the Facebook Prophet forecasting model.
This tutorial is divided in 3 parts each one presented in the following notebooks and blogs:
- 01-Intro_time_series_tutorial.ipynb | Time Series Part 1: An Introduction to Time Series Analysis
- 02-Forecasting_with_SARIMAX.ipynb | Time Series Part 2: Forecasting with SARIMAX models: An Intro
- 03-Forecasting_with_Facebook_Prophet.ipynb | Time Series Part 3: Forecasting with Facebook Prophet: An Intro
This repository opens a series of tutorials introducing techniques that will help you making better data-driven marketing decisions. We start by introducing Market Basket Analysis (MBA); a powerful tool used for product promotion and recommendation. We will discuss several techniques and show you their implementation such that you can employ them yourself.