Thursday, 19 August 2021 / Published in Data, Analytics, Data Science, Data Visualization, Case Studies, Private Equity
Background Recognizing an opportunity to expand their customer base, Red Oak Strategic’s client, a global CPG manufacturer, formed a private equity arm to help develop FinTech platforms in developing economies. This involved buying or building various applications to serve specific needs for distribution partners across several markets. The...
Thursday, 19 August 2021 / Published in Data, Analytics, Data Science, Data Visualization, Business Intelligence, Case Studies
Time and again, across Red Oak Strategic’s corporate engagements and particularly in the Financial Services industry, our team has found that key stakeholders often lack insight and analysis into the data behind their operations. Be it client holdings, portfolio company performance or even their own deal pipelines, there is often a massive blind...
Interaction Design for Data Exploration Visualizations capable of launching detail views can add value to a data analyst’s user experience. Programming in this kind of interaction automates the creation of complementary charts and increases ease of exploration by linking varied views of the data in a logical way.
Thursday, 21 June 2018 / Published in R, Code, Analytics, Apache Spark, Data Science, h2o, Predictive Analytics, Tutorials, Sparkling Water, Machine Learning
At Red Oak Strategic, we utilize a number of machine learning, AI and predictive analytics libraries, but one of our favorites is h2o. Not only is it open-source, powerful and scalable, but there is a great community of fellow h2o users that have helped over the years, not to mention the staff leadership at the company is very responsive...
Monday, 7 May 2018 / Published in Data, R, Code, Analytics, Time Series, Data Science, Forecasting, Predictive Analytics, Tutorials, Machine Learning
Introduction This post will demonstrate how to use machine learning to forecast time series data. The data set is from a recent Kaggle competition to predict retail sales. You will learn how to: Build a machine learning model to forecast time series data (data cleansing, feature engineering and modeling) Perform feature engineering to...
Wednesday, 28 March 2018 / Published in Data, R, Code, Analytics, Exploratory Data Science, Data Science, Data Visualization, R Shiny
Introduction In Part 1, we built an application to geographically explore the 500 Cities Project dataset from the CDC. In this post, we will demonstrate other exploratory data analysis (EDA) techniques for exploring a new dataset. The analysis will be done with R packages data.table, ggplot2 and highcharter.
Wednesday, 31 January 2018 / Published in RegEx, Data, Code, Data Processing, Analytics, Python 3, Data Science, Databases, Python, Tutorial
Applying Regular Expressions This is a tutorial on processing data with regular expressions using Python. It is also a reflection on the advantages and trade-offs that come into play when you use regular expressions. Once you have identified and defined a set of patterns, you can strategically search and extract data from raw text according...
Friday, 5 January 2018 / Published in Data, R, Code, Analytics, Data Science, Data Visualization, R Shiny
Exploratory data analysis (EDA) is generally the first step in any data science project with the goal being to summarize the main features of the dataset. It helps the analyst gain a better understanding of the available data and often can unearth powerful insights. Data visualization is the most common technique in EDA. During this post, I...
Thursday, 7 December 2017 / Published in R, Code, Maps, Analytics, Data Science, Data Visualization, Tutorials, R Shiny
Our team recently designed a dashboard using R Shiny Leaflet allowing users to select many locations at one go on an interactive map. We created the map using the package leaflet.extras, which enables users to draw shapes on R Shiny Leaflet maps. When combined with the package sp and a function called findLocations, the leaflet.extras drawing...
Thursday, 17 November 2016 / Published in Data, Politics, Analytics, Political Analytics, Data Science, 2016 Election, Polling
This year, the Red Oak Strategic team decided to undertake a new challenge in the world of polling and analytics : conduct a bi-weekly, public, national survey that we would execute and release using the Google Surveys platform. Beginning in August and continuing through the final week of the 2016 election, our partnership with GCS led to the...
6 Strategies for Migrating Applications to CloudAt Red Oak Strategic, we understand that...
Business Intelligence Across a Private Equity PortfolioBackground Recognizing an opportunity to expand...
Data Visualization: Empowering Decision MakersTime and again, across Red Oak Strategic’s...
Tracking Coronavirus: Building Parameterized Reports to Analyze Changing Data SourcesThe pace of our modern world, and the impressive...
Draw Rotatable 3D Charts in R Shiny with Highcharts and JQueryWhile it might be tempting to liven up a report...
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