A series of instructions or recipe for manipulating data to achieve an end goal. We use programming languages such as Python or R to implement algorithms. Algorithms can include processes ranging from simple addition to extremely complex neural networks.
Artificial Intelligence (AI)
AI is a solution that learns to recognize and react to patterns, emulating traditionally human tasks like understanding language, recommending business actions, and synthesizing large amounts of information. AI works best when it assists humans by learning very repetitive tasks that depend on large amounts of information.
Data should be used to derive actionable business intelligence. Our primary goal is to use data to contribute to business value. This is done through statistical analysis, data visualization/reporting, and machine learning.
An interactive data visualization or series of visualizations that allow stakeholders to explore various dimensions of data. We develop dashboards using tools such as Tableau or PowerBI with the goal of decipherability and ease of use such that the end user can independently drill into data details of explore high level summary information.
Data engineering involves planning, designing, and implementing information systems. This includes data storage as well as the pipelines that data scientists use to access and transform data.
An organization’s data can be augmented in ways that improve business insight and empower predictive analytics. We use extensive knowledge of open source data to supplement and enrich your proprietary data sources.
Data Lake vs Data Warehouse
Where you store your data is dependent on what type of data you have. A “Data Lake” is used when all you have is raw, unprocessed data that frequently has varying structures that do not have any relations between one another. A “Data Warehouse” is used to store structured or relational data from many sources, not just one.
Data Science exists at the intersection of math, statistics, computer programming, and business. Data Science is the application of these tools to provide insight and value from data.
Data can be used to provide business intelligence, but if a stakeholder cannot understand it, it is difficult to convert that intelligence into business value. Visualization and reporting bridge that gap. This is also necessary when presenting results from statistical analysis or machine learning.
Using architectures like deep neural networks to perform machine learning. If the situation calls for it, deep learning can outperform classical methods and provide state of the art performance. We find that deep learning is most useful with sequential data, image data, or learning from simulated environments.
Exploratory Data Analysis (EDA)
A critical early stage in any data-related project, EDA involves exploring the available data and summarizing the main characteristics, often using visualizations. It can provide additional insight to the data set, and result in ideas and hypotheses to explore with more formal statistical modeling.