A gentle Introduction to the TIMi Suite

Once you have followed the above 8 videos about Anatella, you’ll be able to do almost any data-related activities such as: collecting data, importing data, transforming data, data wrangling, data cleaning, plotting data, exporting data to other BI tools or to cloud services, feature engineering, etc.
 
We strongly encourage you to follow the above 8 videos about Anatella because they’ll greatly help you in learning the “TIMi Suite”. The other videos here below are only useful if you have very specific needs: i.e. if you have a machine learning or clustering exercise to do.
 
Within Anatella, all the open source libraries to create predictive models to perform machine learning activities are directly available, just in a few mouse clicks. But, if you want to engage in the most advanced machine learning exercices, you’ll need an Automated-Machine-Learning tool such as “TIMi Modeler”. Here are a few Youtube video tutorials about Modeler:
  1. A short introduction to predictive modeling and how to create your first predictive model with Modeler
  2. How to use Modeler directly from within Anatella to automatically re-create new model on-the-fly each week?
  3. Solving a very advanced Text Mining problem with TIMi Modeler
Always within Anatella, all the open source libraries to segment/cluster your datasets are directly available, just in a few mouse clicks. But, if you want to engage in the most advanced clustering exercices, you’ll need an advanced 3D segmentation tool such as “Stardust”. Here are a few Youtube video tutorials about Stardust:
  1. Segmentation theory: Theoretical Introduction to the PCA technique – part 1.
  2. Segmentation theory: Theoretical Introduction to the PCA technique – part 2
  3. A demonstration of the multivariate segmentation engine of Stardust – part 1.
  4. A demonstration of the multivariate segmentation engine of Stardust – part 2.

LATEST TUTORIALS

Anatella Basics: Part 8

The most common boxes.

Anatella Basics: Part 7

Best practices.

Anatella Basics: Part 6

Put a graph in production.

BUSINESS INTELLIGENCE BASICS

In this guide, we will see which functionnalities each type of analytic solution has to offer,
how they compare to each other and how to make the right choice for your needs.
Classical solutions for Business-Intelligence can be roughly categorized as followed:

Data integration: ETL

The main objective of ETL tools (Extract, Transform, Load) is to gather the content of various databases or operational systems across an organization and centralize all data in one place, named “data warehouse”.

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Data warehousing

A data warehouse is a centralized place where you can find all the data of your company. Data Warehouse software are very often simple database: Oracle, Teradata, SQL Server, MySQL,…

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Reporting & BI

Reporting software (i.e. BI tools – Business Intelligencetools) allow you to create charts and graphs about the evolution of different KPI (Key Performance Indexes) that characterizes your business.

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CRM softwares

The CRM acronym stands for “Customer Relationship Management”. This category covers Operational CRM and Analytical CRM softwares.

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Advanced Analytics: 2 different approaches

Analytical CRM can be classified in 2 categories: Analytical CRM tools based on segmentation techniques and Analytical CRM tools based on predictive techniques. By its very nature, segmentation is a technique well-adapted for exploratory work. In opposition, predictive analytics is discriminatory in nature. Analytical CRM tools based on predictive techniques usually generates a higher ROI for marketing campaigns.

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Of the utility of the test dataset

When comparing the accuracy of 2 different predictive models to know which predictive model is the best one (the one with the highest accuracy & the highest ROI), you must compare ONLY the “lift curves computed on the TEST dataset” and if possible, use always the same TEST dataset.

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Not all lifts are born equal

When we are constructing a model that makes no mistakes on the “training dataset”, we obtain a model that doesn’t perform well on unseen data. The “generalization ability” of our model is poor. The predictive model is using some information there were in reality noises. This phenomenon is named “Over-fitting”. A model that “overfits” the data can have an accuracy of 100% when applied on the learning set.

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