Predicting the Popularity of NY Times Comments

Dive into machine learning and recommendation systems. Statistical arbitrage techniques. Python for trading. Predicting the popularity of NY Times comments, and more!  [DataCamp](  ISSUE #61 — SEPTEMBER 4, 2018  Hi there - this week we're diving into machine learning and recommendation systems. Followed by statistical arbitrage techniques, Python for trading, predicting the popularity of NY Times comments, and much more! We also have a new DataFramed episode where Hugo speaks with Tanya Cashorali about data science consulting in health & telcos, dashboards, data products, rapid prototyping, data literacy & more! [Listen Now](  Community Top Posts  [1]( [How to Implement Machine Learning Based Recommendation Systems](  MACHINE LEARNING  |  Posted by rizwankhan19  [Upvotes]( 10 This module covers how to implement machine learning based recommendation systems.  [2]( [How to Utilize K Means Clustering Model for Unsupervised ML Problems](  MACHINE LEARNING  |  Posted by rizwankhan19  [Upvotes]( 10 This article covers K Means Clustering which is an unsupervised machine learning algorithm. It is very efficient and mostly used for content based recommendation systems.  [3]( [Kalman Filter Techniques And Statistical Arbitrage In China's Futures](  FINANCE  |  Posted by virajb  [Upvotes]( 9 The project focuses on China's futures market using Statistical Arbitrage and Pair trading techniques. You will run an Augmented Dickey-Fuller test on the spread to confirm statistically whether the series is mean reverting or not, and will calculate Kalman Filter regression on the spread series.  [4]( [Getting Started With Python For Trading](  PYTHON  |  Posted by virajb  [Upvotes]( 9 Python is widely used in the field of machine learning and now trading. This article covers all that is required to get started with Python. It can be used to code your own trading strategies and test them.  [5]( [Predicting Popularity of The New York Times Comments (Part 1)](  DATA SCIENCE  |  Posted by sakshigupta88  [Upvotes]( 9 DataCamp user sakshigupta88's 2018 summer capstone project!  [6]( [Using Quadratic Discriminant Analysis To Optimize An Intraday Momentum](  FINANCE  |  Posted by virajb  [Upvotes]( 7 This article explains how to create an intraday momentum strategy and to use QDA as a means of optimizing our strategy. Beginning by reviewing Linear Discriminant Analysis or LDA and how it is associated with QDA, gaining an understanding of QDA and when we might implement this technique instead.  [7]( [Calculating Value At Risk In Excel & Python](  FINANCE  |  Posted by virajb  [Upvotes]( 7 This post will take you through the step-by-step process to understand and compute VaR in Excel and Python using Historical Method and Variance-Covariance approach. You can also download the excel and Python codes to calculate the VaR for yourself.  [8]( [How I got in the top 1% on Kaggle](  KAGGLE  |  Posted by 1community  [Upvotes]( 7 This author participated in Santander Customer Satisfaction challenge, ran on Kaggle for 2 months and got into the top 1%. Here, he discusses his approach to this problem.  [9]( [Beyond the Unicorn: 4 Developing Data Scientist Career Paths](  CAREER  |  Posted by lindaburtch  [Upvotes]( 6 As the data science market continues to evolve, we examine four growing specialty areas within data science that are in increasingly high demand.  [10]( [Data Science and Insurance (Transcript)](  DATAFRAMED  |  Posted by hugobowneanderson  [Upvotes]( 6 What are the biggest challenges in insurance and reinsurance that data science can impact?   [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