Graphical Methods

Statistical Analysis and Research

Description

We love to explore data and provide insights! Here we work to define the problem with you and go to work exploring. The methods may incorporate machine learning, data mining, and prescriptive statistics. All industries are of interest.

Methods

Association Mining, Distribution Modeling, Forecasting, Time Series Analysis, Descriptive Statistics, Regression, Report Generating, Network (Graph) Analysis, Text Analysis, Factor Analysis, A/B Testing, Variance Modeling, Geo Spatial. See Machine Learning for additional methods.

Example

You have data and are not sure what to do with it. Or, maybe you have a project in mind. Give us your data and we'll identify trends, important statistics, and apply statistical methods to your data.

Machine Learning

Description

Providing useful insights, predictive and prescriptive, based on prior collected data. This typically is data stored in a database. The data collected centers around customers, or generally, people, places, things. For best results, there should be a large volume of data -- more than can fit in a spreadsheet.

Methods

Clustering, (Linear/Logistic) Regression, Tree Methods, Random Forests, Neural Nets, NLP, PCA, LDA, Factor Analysis, Variable Imputation.

Example

Suppose you have subscription based customers and have collected data on them for some time. This data includes things like, purchase amount, location, duration of purchases, and types of purchases. Using this data we can tell you things like whether or not a customer is likely to end their subscription and depending on the methods used even why!

A/B/N Testing

Description

This answers the question, "are my changes contributing to a measurable change?" This is one our favorites. We'll work on test design and implementation. This involves defining the hypothesis and outcome analysis. We strongly emphasize identifying the importance of the change over just statistical significance.

Methods

T-Tests, ANOVA, (Linear/Logistic) Regression, Contrasts, Variance Reduction Methods, Sample Sizing.

Example

You've design a feature or features, maybe it's even a new process. We'll help you explore possible designs for your experiment and how they affect the end analysis. Then we start collecting data. Once the process is completed we'll prepare a report and review discoveries and make suggestions.

Data Engineering

Description

We build data pipelines. Part of analyzing data is being able to move and transform it! We have experience in a variety of databases and tools. Let us help build your modeling or BI data pipeline.

Methods

APIs, Python, Snowflake, Postgres, SQLite, MongoDB, Airflow, cron, Data marts.

Example

You need to aggregate and transform your data to be consumed by your analytics team.

Shiny Apps

Description

Data is only useful if people can use it! We love to build interactive ways to explore and interepret data. These are web based applications that feature logins, custom visualizations, interactivity, and our favorite generating even more data.

Methods

Shiny, Shiny Proxy, HTML, JavaScript, CSS

Example

You want to embed an analysis into your website or are looking for a very custom output for your analysis. Shiny Gallery Example

Dashboards

Description

We have lots of experience with implementing and building dashboards. Let us setup your analytics reporting infrastructure. We also help define KPIs!

Methods

Shiny, Flexdashboards, Looker, Metabase, Redash.

Example

You have the data and want to establish a self-serve model for your analytics. We'll create dashboards that are scalable and reproducible.