Time is limited. How much of yours is spent acquiring, processing, and cleaning data?
The answer is probably “too much.” When the success of your projects (and by extension, your job) is dependent on a foundation of reliable, high-quality data, it’s important to get it right.
Fortunately, quality data is our raison d’être. Read on to find out how we save data scientists time, money, and headaches with our data science tools.
The Core Challenge of Data Scientists
Data scientists face a massive number of challenges, from building accurate models to getting buy-in from stakeholders. We can’t make your CEO trust your recommendations (sorry), but we can help with the basic need of all data scientists: clean data.
Anyone in the data game knows that 99% of your time can be devoted to finding the right datasets, cleaning and standardizing the data, and testing it to make sure it fits your use case. This doesn’t leave much time for finding actionable value from the data (i.e., the thing you get paid to do) – especially on a tight deadline.
Skip ahead to the valuable 1% with data that is pre-cleansed, standardized, normalized, and flexible. Here are a few of the reasons data scientists love our data:
The Financial Data Marketplace
More data providers equal more contracts, more licensing headaches, and more access methods. Intrinio created the Financial Data Marketplace to bring together 300+ data products under one umbrella. Subscribe only to the feeds you want, without wading through a bundle of data you didn’t ask for. Or download massive amounts of historical data with a one-time purchase. Data is available via our web API and other flexible formats. It’s data on-demand – no contracts, licenses, or vetting required.
Data Quality via Machine Learning
Once cleansed, data is integrated into our system, where we remove errors with machine learning. The Intrinio Engine is a human-led AI framework built to normalize and standardize financial data from multiple sources, historical and real-time. Our algorithms evaluate millions of data points for error risk every day and take appropriate action. High-risk data is flagged by default and sent to our Q/A teams. Intrinio’s data framework eliminates the need for time-consuming and error-prone manual standardization.
SDKs for Popular Programming Languages
Recently, we added SDKs for R and Python to simplify data access. The Python SDK has a built-in object method in almost every endpoint that allows the use of the Pandas SDK. The R SDK has built-in functionality for the RStudio integrated development environment (IDE) that provides recursive unwrapping of the return object for simple display of data in a data table. Combining this with the built-in graphing functions has produced interesting graphs with just a few clicks.
Free Sandbox Access for Testing
Raise your hand if you enjoy reaching out to vendors, downloading test data, realizing it doesn’t work for you, and hitting restart on the process. Trust us – there’s an easier way. When you sign up for a free Intrinio account, you get access to our sandbox environment, with more than 10 million data points. Experiment with nearly our entire data catalog, build out your proof-of-concept, and then easily transition to a production environment when you find the right data for your needs.
We want people to hit the ground running with our data, which is why we provide tons of avenues for support. Find the answers to your questions – or learn how to get started – with our extensive documentation. Venture into the user community to leverage the knowledge of your peers. Launch a live chat with our customer success team in one click. Or submit a ticket to our data quality team with our built-in ticketing system. Your time is valuable – why wait around for help?
Intrinio-Driven Data Science in Real Life
From September 2018 to July 2019, nearly 3,000 data science teams competed in Two Sigma: Using the News to Predict Stock Movements. The global data science competition, which was jointly sponsored by Kaggle and Two Sigma, attempted to answer the question: can we use the content of news analytics to predict stock price performance?
As the competition’s website noted:
“The ubiquity of data today enables investors at any scale to make better investment decisions. The challenge is ingesting and interpreting the data to determine which data is useful, finding the signal in this sea of information.
By analyzing news data to predict stock prices, Kagglers have a unique opportunity to advance the state of research understanding the predictive power of the news. This power, if harnessed, could help predict financial outcomes and generate significant economic impact all over the world.”
Participants use two sources of data for the competition:
- Market data (2007 to present) provided by Intrinio – financial market information such as opening price, closing price, trading volume, calculated returns, etc.
- News data (2007 to present) provided by Thomson Reuters – information about news articles/alerts published about assets, such as article details, sentiment, and other commentary.
This unique competition encouraged collaboration among some of the brightest minds in data science. In the first stage, entrants trained their models in Kaggle Kernels and submitted their two best algorithms. In the second stage, entrants and spectators watched as the weekly leaderboard unfolded. Winners received a total of $100,000 worth of prizes, with the first-place winner, Renjie Qian, taking home $25,000.
We believe in the power of data scientists to solve massively complex problems – which is why finding accurate and clean data should be the easy part. Ready to test out Intrinio’s platform for yourself?