Using Python to Power Spreadsheets

Data Science to build a World Cup team. ML for algo trading. Redesign your Shiny dashboard. Data loading for TensorFlow. Jupyter Notebook Tutorial.  [DataCamp](  ISSUE #51 — JUNE 19, 2018  Hi there - here’s the Community roundup from last week! Starting with using Python to power spreadsheets, data loading techniques for TensorFlow, AI in trading, FIFA World Cup data-driven team building, some essential reading, and more! But first, a little information about our newest podcast episode. Episode 27 of DataFramed has launched! Data Security, Data Privacy and the GDPR. What are the biggest challenges currently facing data security & privacy? Is data anonymization actually possible or a pipe dream? Find out from this episode featuring DataCamp instructor Katharine Jarmul. [Podcast Episode](  Community Top Posts  [1]( [Using Python to Power Spreadsheets in Data Science](  PANDAS  |  Posted by jasongraham  [Upvotes]( 20 Learn how Python can be used more effectively than Excel, with the Pandas package.  [2]( [Understand Keras by Building a Neural Network Model](  KERAS  |  Posted by rizwankhan19  [Upvotes]( 11 This article explains Keras, a framework for building deep neural networks, by building a small Neural Network model in comparison with TensorFlow.  [3]( [Data Loading Techniques for TensorFlow](  TENSORFLOW  |  Posted by rizwankhan19  [Upvotes]( 10 There are many data loading techniques for TensorFlow supported by Python depending on the size of data set. This article covers the most commonly used techniques.  [4]( [Artificial Intelligence and Machine Learning in Trading](  MACHINE LEARNING  |  Posted by nitint  [Upvotes]( 8 Machine learning is logical and overcomes human limitations. This is important in trading where emotions can lead to decision making pitfalls.  [5]( [Jupyter Notebook Tutorial: The Definitive Guide](  JUPYTER  |  Posted by darshandandavathi  [Upvotes]( 8 This tutorial explains how to install, run, and use Jupyter Notebooks for data science, including tips, best practices, and examples.  [6]( [Create Outstanding Dashboards with the New semantic.dashboard Package](  SHINY  |  Posted by karolinadziamska  [Upvotes]( 8 Tutorial on how to redesign your R Shiny dashboards using the new semantic.dashboard package.  [7]( [FIFA World Cup 2018: A Data-Driven Approach to Ideal Team Lineups](  DATA SCIENCE  |  Posted by khanhle1013  [Upvotes]( 8 With the World Cup 2018 underway in Russia, every soccer fan around the world is eager to make predictions on what team will win this year. What formation should be used? Which players should be chosen? Which ones should be left on the bench or eliminated from the tournament?  [8]( [Machine Learning and Its Application in Forex Markets](  MACHINE LEARNING  |  Posted by nitint  [Upvotes]( 7 To use Machine Learning in trading, you start with historical data (stock price/forex data) and add indicators to build a model in R/Python/Java. You then select the right Machine learning algorithm to make the predictions.  [9]( [Essential Books on Algorithmic Trading](  READING  |  Posted by nitint  [Upvotes]( 7 This article details the core focus areas for aspiring quants and also contains a comprehensive list of essential books on algorithmic trading in each category.  [10]( [Predictive Modeling in R for Algorithmic Trading](  MODELING  |  Posted by nitint  [Upvotes]( 7 Step by step tutorial to implement Predictive Modeling in R for automated trading. Use a predictive model to glance at historical data for algorithmic trading.   [DataCamp Community]( BY THE COMMUNITY, FOR THE COMMUNITY [Discover](   That's all for now. Have a great week!   [DataCamp](  DataCamp Inc. | 350 Fifth Avenue | Suite 7730 | New York, NY 10118  [Facebook]( [Twitter]( [LinkedIn]( [Instagram]( [YouTube](  [Download on the App Store]( [Get it on Google Play](  [Unsubscribe]( Â