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...
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.
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...
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...
Imagine someone were to mint a new coin and give it to you. You then buy something and give it to someone else, and so on. Every time this coin changes hands, a record of the transaction is engraved on the coin. Every transaction in the history of that coin’s existence is in plain sight on the face of the coin—and the longer the coin...
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.
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...
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...
Frequently, we encounter projects that require the combined use of Python, Microsoft Excel and some external databases that can only be accessed via Excel, or use cases that require the end product to be output to that format. Excel is still used as a key program for the vast majority of businesses and we are often challenged to create...