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##### Cases

Explore our pool of datasets, have** a hands-on experience **and learn from the real-world data problems any time

##### DataChat

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## News

### Getting to know the awesome Datathon mentors – Part 3

We have already published the first two groups of #GlobalDatathon2018 mentors, together with their thoughts about the Datathons. Now is the time for you to get to know the third group. We would like to say a big “Thank you!” to all the mentors that will be joining this edition of the event. All of […]

### Getting to know the awesome Datathon mentors – Part 2

With the Dathathon start approaching rapidly, many of you are looking forward to speak to the mentors and tap into their knowledge and expertise. But some of you might be asking exactly what the role of the mentor is, so here is a non-exhaustive list. Mentors are there to help you when you get stuck […]

### Getting to know the awesome mentor line-up – Part 1

Mentors are an integral part of a great datathon. This is why for #GlobalDatathon2018, we have invested a lot of effort to bring together a great group of mentors with diverse backgrounds, experience, and superpowers. So… undoubtedly we would like to brag about them and show them our appreciation 🙂 In this sequence of articles […]

## Learn

### The Kaufland Case [Global Datathon 2018] – Guidelines by Simon Stiebellehner

The Kaufland Case poses an interesting Predictive Maintenance challenge. First, make sure that you understand what the goals and deliverables are. This is perhaps the most important step in the entire Data Science process. It’s crucial for the business value of the result and it ensures that you spend the little time you have on […]

### An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples

What actually does Machine Learning mean and what types of problems does it solve? This Machine Learning tutorial introduces the basics of ML theory, laying down the common themes and concepts. The practical examples substituted with the mathematical functions of univariate linear regression, linear least squares and others make it easy to follow the logic and get comfortable with machine learning basics.

### A venture in crypto-currency trading

In [315]: import numpy as np import pandas as pd import matplotlib.pyplot as plt from statsmodels.tsa.seasonal import seasonal_decompose from statsmodels.tsa.arima_model import ARIMA from statsmodels.graphics.tsaplots import plot_acf, plot_pacf import time Enthusiast Team: Datathon Case¶Predicting Cryptocurrency prices¶Reading the Currencies for the First Problem¶ In [88]: currency_info = pd.read_csv(“currencies.txt”, sep = “\t”) currency_info Out[88]: Currency ticker CoinID 0 Bitcoin BTC […]