Our goal is to provide data science products for your business to help you measure, predict, generate and resonate hype over your data.
Any biological (Is that stochastic?) process has specific behavioral patterns. The leafs get green in spring while fading in the fall. You wake up in the morning and feel sleepy at midnight. Not only you - but also all other guys you know. How about robots and servers? They also go asleep, get sick, overloaded or even excited from a monthly Amazon bill ;)
What if you manage to control the hype data? For a salesman it will result in a better sales pipeline, as a DJ you will get more excitement for your audience, doctors can better understand patient’s mental health.
We are rather a research agency. Still, tools we create are very attractive for marketing agencies to be integrated into their data processing pipelines.
But not all hypes are so interesting as the following ones are:
The world is changing all the time. So, we tend to updating the roadmap as soon as we do something great or update plans for the future.
☐ Hype data kernel HTML export.
☐ Hype data kernel PDF export.
☐ A Telegram chat-bot providing subscriptions to OpenSource hype data sets.
☐ Creating data kernels through the Telegram chat-bot.
☐ Adding keywords through the Telegram chat-bot.
☐ Billing while creating data kernels.
☐ Customer authentication;
☐ Generic customer’s dashboard;
☐ Data sources listing page;
☐ Data source details page.
☐ A summary report on the predicted cryptocurrency investment portfolio based on analyzing https://coinmarketcap.com data;
☑ Computing a generalized Telegram crypto-currency hype aggregate;
☑ Researching statistical data for stable-coin crypto-currency hypes;
☑ Researching statistical data for ICO hypes;
☑ Researching statistical data for STO hypes;
☑ Researching statistical data for abandoned crypto-currency hypes;
☑ A summary report on statistical data research.
☑ Data pipeline components for filtering time-series data based on regular expressions;
☑ Data pipeline components for extracting Telegram links from time-series data;
☑ Data pipeline components for extracting Telegram links separately for channels and groups;
☑ Data pipeline components for aggregating time-series data to acquire countable observations statistics;
☑ Update the Jupyter Notebook Docker container for time series analysis to include pipeline libraries;
☐ Configure the Telegram observer component to extend the crypto-currency content extraction;
☑ Data reducer for displaying time-series data as a Plotly time-series graph.