Our data science team handles projects across a range of industries. We have experts in statistics, GIS, machine learning, database architecture, and data visualization.
We have an extensive background modelling and tuning databases at scale. We’ll select a data persistence and retrieval mechanism that meets your business needs. Elastic has been used not only for log aggregation and SEIM (Security Information and Event Management), but also for application search and reporting purposes. We also cache in high speed memory stores such as Redis when fast, scalable reads meet your goals.
For mobile applications, we use Firebase’s real-time database for synchronization of data between hosted data and data persisted on the device.
We’re able to develop big data collection, analysis, and reporting pipelines. We’ve done real-time queue aggregations with queues like Kinesis and Kafka for real-time reporting of streaming data. We’ll model your data warehouse for you, then extract, transfer, and load (ETL) your data with tools including Apache Spark, Apache Airflow, Amazon Glue and Hadoop.
We support real-time queries of massive data sets with databases such as Redshift, Bigquery, and Hadoop.
As well as devising our own algorithms, our team has worked in tandem with data science groups at Fortune 500 companies. We understand probability distributions, variance, hypothesis testing and inference (p and t scores), and regression and regression model selection (logistic, linear, and multivariate).
Often times, these tried and true approaches to data yield insights that are clearer and quicker than ML techniques.
We’ll make sure you get the full picture by harnessing the power of your data, enabling your business and your customers to make better decisions. We leverage our own proprietary visualization tools, open source systems, and enterprise third party technology to create the right solution. We can integrate with Tableau, Power BI, and Google Data Studio, and we collaborate with looker.com on BI data visualization.
We’re ready to help with classification and regression analysis of your data sets. We’ve worked on unstructured imagery classification problems with TensorFlow, and are familiar with a variety of structured modeling and classification techniques, such as regression, random forest, GBM, and K-means clustering.
In addition to R and Python-based local machine learning implementations, we can leverage Amazon and Google cloud-based services for natural language processing in chatbots and sentiment analysis.
Location is built into our everyday lives. Tracking a package, finding a restaurant, checking the weather. Whether it’s detecting, counting, or monitoring location-based data, we know our way around the leading mapping analysis systems, including ESRI ArcGIS and Mapbox.