Extract data from source.
Transform into a useful form.
Load into database or visualisations.
Combine multiple ETLs.
Sequential data processes.
Daily or weekly triggers.
Host data pipelines on the cloud.
AWS or Google Cloud.
Production environment.
Scheduled pipeline runs.
Overnight productivity.
Overview of automated data pipeline.
Proactive fixes and updates.
360 view at all times.
Share your data with a web app.
Share your data with the world.
Easy access to specific data viewpoints.
Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.
Insights - an accurate and deep understanding of your data.
Presentations - an accurate and deep understanding of your data.
Dashboards - an accurate and deep understanding of your data.
Views - an accurate and deep understanding of your data.
Supervised Learning - Regressions, Random Forests, SVMs, Neural Nets.
Unsupervised Learning - Clustering, Dimensionality Reduction.
Reinforcement Learning - Positive, Negative.
Statistical Modeling - Use of models and statistical assumptions to generate sample data and make predictions.
Machine Learning - Learning patterns within data and use it to make predictions.
An AI that responds to inquiries in human-like languages in text or voice format.
It leverages natural language processing (NLP) to process, understand, and generate responses to system users in a conversational manner.
Can be embedded into a webapp for integrated end-user experience.
An AI that uses generative models to produce text, images, videos, or other forms of data.
Can be embedded into a webapp for integrated end user experience.
Advise - review and recommend how best to utilise your data.
Plan - create a roadmap of engineering, analytics and machine learning to unlock your data.