**Data scientist **

**HOW WE CAN HELP YOUR BUSINESS **

**Explore your business data. **

In this unit, we care about understanding our clients' business.

We develop models to extract the maximum benefit from the information that is continuously captured by the CRM (Customer Relationship Management) and ERP (Enterprise Resource Planning) systems, thus generating proactive strategies that take into account historical events to diagram the future of each business.

**OUR METHODOLOGIES **

**We support you by exploring your data. **

**Toolbox**

**Unsupervised Learning **

Data have a lot to say if we just let them talk. Unsupervised learning constitutes a set of techniques oriented towards finding hidden patterns in the information. Data are naturally grouped and associated to find these associations, correlations and groupings; our main goal is to achieve optimizations.

**Association Algorithms **

In terms of transactional data, we are interested in analyzing:

◆ How are shopping baskets made up?

◆ What is the adoption sequence for products? Example of product 1: credit card, and product 2: savings account.

◆ What are the sequences of steps a user follows on a webpage? Find the rules capable of describing the association of consumer decisions, shopping baskets, browsing routines, patterns of cross-selling and staggered selling; these are just some of the applications of association algorithms.

**Cluster Analysis**

Our goal is to find groups (customers, points of sale, websites).

The elements classified within these groups are very much alike but, when comparing between groups, these must behave very differently.

Clustering techniques are generally used to find segments, natural groupings, profiles, etc.

Segmentation of customers, risks, contracts, etc., are some of the applications.

**Reducing Dimensions**

When faced with many variables, it is common to find that these tend to be correlated.

Our goal is to build new variables or vectors that group correlated variables and that make future forecasts more accurate and stable.

Techniques such as principal component analysis, factor analysis and multidimensional scaling are some of the examples.

**Self-organized Maps**

This family of tools comes from artificial neural networks.

It seeks to find segments and ultimately profiles, revealing hidden patterns in the information.

Based on discovering close neighbors in space or in time, which is why this is an efficient technique when the analysis units tend to vary through time or topologically

**Supervised Learning **

On some occasions, we need to predict a certain phenomenon; for instance: loss of clients, consumption volume, fraud, etc. The implication is that some cases have the phenomenon and others don’t. To find the features that differentiate the groups and generate efficient forecasts, we turn to supervised learning techniques.

The variable we want to predict “supervises” or organizes the learning of the model.

Our objective here is to predict by using auxiliary variables to estimate a variable different from the one we will call objective.

“Grosso modo”, there are three large families of supervised models:

**Regression methods **

These models generate equations where the X variables (explanatory) acquire relevance and can then estimate the Y variable (factor we want to predict).

Some models in this family of techniques are:

◆ Linear Regression: To predict quantitative variables.

◆ Logistic Regression: To predict binary or multinomial qualitative variables.

◆ Poisson Regression: To predict variables that are expressed as percentages or tallies.

◆ Cox Regression: To predict durations.

**Tree methods **

Decision trees build partitions in the data according to the impact of X variables (explanatory) on the Y variable.

It creates classification rules, not equations.

Some algorithms are CHAID, CRT, Entropies.

Some advanced machine learning techniques are based on this family of techniques. Example: Random Forest, Gradient boosting.

**Neural Networks methods **

The neural network tries to learn on Y based on the X variables available, the same way the brain tries to establish neural connections to generate recognition and memory.

Equations and rules are created simultaneously to achieve the prediction.

The multilayer perceptron and the radial basis function are two of the most frequently used algorithms to train models of this nature.

**Forecasting**

A lot of information in the business context and even in the digital context tends to be collected over time; for instance, sales, inventories, risk factors, cases of fraud, etc.

Time series usually have at least three features: Trend, seasonality and cycles. What forecasting seeks is to generate the best future scenario taking into account the behavior of the same variable over time.

Our objective here is TO FORECAST: use the behavior of a variable in the past to estimate its future behavior.

**Smoothing Methods **

These are efficient methodologies to model the historic behavior of a variable, but not so optimal to generate its future representation.

These methods tend to be efficient when there is little data.

Moving averages, exponential smoothing, Holt line, Winters method.

**Box-Jenkins Method**

Also known as ARIMA, is a comprehensive methodology to decompose the series based on three elements:

a. AR: Autoregressive component.

b. I: Integrated component – differentiation or seasonality.

c. MA: Moving average component.

If the three components predict the series, an ARIMA is established; when only components AR and MA are significant, an ARMA is established, etc.

**Neural Networks Methods **

Neural networks have shown excellent accuracy levels to predict time series.

Especially the radial basis function models, which position themselves as a new strategy to improve the forecast accuracy of forecast models.

**Transfer and Co-integration functions**

We may come across opportunities where we have different variables deployed over time and on the same dates.

In these cases, we want to construct integrative models called transfer functions.

Our goal is to analyze how a time series predicts or estimates another one.

**Optimization **

The overarching goal of applied mathematics could well be optimization.

What interests us here is minimizing, maximizing or equalizing a process; which implies distributing limited resources within unlimited needs.

Allocate a marketing budget within different communication channels or spread inventories throughout points of sale to reduce inventory days, are examples of applied optimization problems.

**Mathematical Optimization **

The relationship between processes and results; they tend to assume a behavior that describes these relationships.

Linear, non-linear, quadratic optimization, etc.

**Discrete Event Simulation**

Qualitative results are also results and we need to predict them, estimate them and take them into account in order to optimize them.

**Optimization of Resources and Processes**

Throughout productive processes, when managing projects or in similar scenarios, time and money need to be strategically allocated.

Resource assignment to achieve the best result with less represents the most frequent optimization issues in companies.

**Design and Optimization of Routes**

Moving things – persons from one point to another, as quickly as possible, with the highest feasible occupation and at the lowest cost, is a challenge.

Operations research has an efficient answer to this type of questions.

**Image recognition**

We take the data and quickly turn it into results. We generate solutions and applications that use Artificial Intelligence and Big Data to rapidly achieve the business objectives.

**Video processing **

Isolation of graphic patterns on videos to alert on outcomes of interest.

Multidimensional analysis: Shape, color, depth, speed, etc., to discover findings and generate applications.

**Sound processing **

Recognition of voice, words and establishment of patterns.

Training bots and use of this knowledge / insight to develop business solutions and applications.

**Photo processing **

Isolating graphic patterns of images, face recognition and development of predictive applications within said dimension.

**Unsupervised Learning **

Data have a lot to say if we just let them talk. Unsupervised learning constitutes a set of techniques oriented towards finding hidden patterns in the information. Data are naturally grouped and associated to find these associations, correlations and groupings; our main goal is to achieve optimizations.

**Association Algorithms **

In terms of transactional data, we are interested in analyzing:

◆ How are shopping baskets made up?

◆ What is the adoption sequence for products? Example of product 1: credit card, and product 2: savings account.

◆ What are the sequences of steps a user follows on a webpage? Find the rules capable of describing the association of consumer decisions, shopping baskets, browsing routines, patterns of cross-selling and staggered selling; these are just some of the applications of association algorithms.

**Cluster Analysis**

Our goal is to find groups (customers, points of sale, websites).

The elements classified within these groups are very much alike but, when comparing between groups, these must behave very differently.

Clustering techniques are generally used to find segments, natural groupings, profiles, etc.

Segmentation of customers, risks, contracts, etc., are some of the applications.

**Reducing Dimensions**

When faced with many variables, it is common to find that these tend to be correlated.

Our goal is to build new variables or vectors that group correlated variables and that make future forecasts more accurate and stable.

Techniques such as principal component analysis, factor analysis and multidimensional scaling are some of the examples.

**Self-organized Maps**

This family of tools comes from artificial neural networks.

It seeks to find segments and ultimately profiles, revealing hidden patterns in the information.

Based on discovering close neighbors in space or in time, which is why this is an efficient technique when the analysis units tend to vary through time or topologically

**Supervised Learning **

On some occasions, we need to predict a certain phenomenon; for instance: loss of clients, consumption volume, fraud, etc. The implication is that some cases have the phenomenon and others don’t. To find the features that differentiate the groups and generate efficient forecasts, we turn to supervised learning techniques.

The variable we want to predict “supervises” or organizes the learning of the model.

Our objective here is to predict by using auxiliary variables to estimate a variable different from the one we will call objective.

“Grosso modo”, there are three large families of supervised models:

**Regression methods **

These models generate equations where the X variables (explanatory) acquire relevance and can then estimate the Y variable (factor we want to predict).

Some models in this family of techniques are:

◆ Linear Regression: To predict quantitative variables.

◆ Logistic Regression: To predict binary or multinomial qualitative variables.

◆ Poisson Regression: To predict variables that are expressed as percentages or tallies.

◆ Cox Regression: To predict durations.

**Tree methods **

Decision trees build partitions in the data according to the impact of X variables (explanatory) on the Y variable.

It creates classification rules, not equations.

Some algorithms are CHAID, CRT, Entropies.

Some advanced machine learning techniques are based on this family of techniques. Example: Random Forest, Gradient boosting.

**Neural Networks methods **

The neural network tries to learn on Y based on the X variables available, the same way the brain tries to establish neural connections to generate recognition and memory.

Equations and rules are created simultaneously to achieve the prediction.

The multilayer perceptron and the radial basis function are two of the most frequently used algorithms to train models of this nature.

**Forecasting**

A lot of information in the business context and even in the digital context tends to be collected over time; for instance, sales, inventories, risk factors, cases of fraud, etc.

Time series usually have at least three features: Trend, seasonality and cycles. What forecasting seeks is to generate the best future scenario taking into account the behavior of the same variable over time.

Our objective here is TO FORECAST: use the behavior of a variable in the past to estimate its future behavior.

**Smoothing Methods **

These are efficient methodologies to model the historic behavior of a variable, but not so optimal to generate its future representation.

These methods tend to be efficient when there is little data.

Moving averages, exponential smoothing, Holt line, Winters method.

**Box-Jenkins Method**

Also known as ARIMA, is a comprehensive methodology to decompose the series based on three elements:

a. AR: Autoregressive component.

b. I: Integrated component – differentiation or seasonality.

c. MA: Moving average component.

If the three components predict the series, an ARIMA is established; when only components AR and MA are significant, an ARMA is established, etc.

**Neural Networks Methods **

Neural networks have shown excellent accuracy levels to predict time series.

Especially the radial basis function models, which position themselves as a new strategy to improve the forecast accuracy of forecast models.

**Transfer and Co-integration functions**

We may come across opportunities where we have different variables deployed over time and on the same dates.

In these cases, we want to construct integrative models called transfer functions.

Our goal is to analyze how a time series predicts or estimates another one.

**Optimization **

The overarching goal of applied mathematics could well be optimization.

What interests us here is minimizing, maximizing or equalizing a process; which implies distributing limited resources within unlimited needs.

Allocate a marketing budget within different communication channels or spread inventories throughout points of sale to reduce inventory days, are examples of applied optimization problems.

**Mathematical Optimization **

The relationship between processes and results; they tend to assume a behavior that describes these relationships.

Linear, non-linear, quadratic optimization, etc.

**Discrete Event Simulation**

Qualitative results are also results and we need to predict them, estimate them and take them into account in order to optimize them.

**Optimization of Resources and Processes**

Throughout productive processes, when managing projects or in similar scenarios, time and money need to be strategically allocated.

Resource assignment to achieve the best result with less represents the most frequent optimization issues in companies.

**Design and Optimization of Routes**

Moving things – persons from one point to another, as quickly as possible, with the highest feasible occupation and at the lowest cost, is a challenge.

Operations research has an efficient answer to this type of questions.

**Image recognition**

We take the data and quickly turn it into results. We generate solutions and applications that use Artificial Intelligence and Big Data to rapidly achieve the business objectives.

**Video processing **

Isolation of graphic patterns on videos to alert on outcomes of interest.

Multidimensional analysis: Shape, color, depth, speed, etc., to discover findings and generate applications.

**Sound processing **

Recognition of voice, words and establishment of patterns.

Training bots and use of this knowledge / insight to develop business solutions and applications.

**Photo processing **

Isolating graphic patterns of images, face recognition and development of predictive applications within said dimension.

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**HOW CAN WE HELP YOU?**

**Contact us and we will find the bestsolution for your company. **