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Formation in Data Science

The Reason Why

Today, we have to be able to handle large volumes of data. We also need enough skills to analyze data to make decisions, test scientific hypotheses and also show results in a useful and clear way.

What We Offer

OneMind-DataScience deliver many statistical courses and lectures. Topics can range from a basic overview of statistical methods to pertinent to a specific discipline. We  will can perform the course/lecturer/workshop that better adapts to your interests.

The tools

R is an environment and programming language with a focus on statistical analysis and data science. With our courses you will obtain the knowledge to use this potent  tool to resolve your analytical data problems, manage your data and create useful graphics.

Courses in R programming

  • You can choose one of the established courses or you can define a meeting and tell us which are your needs.
  • We perform the courses in function of your general statistical and/or programming level and considering your final expectancy.
  • The courses can be in-person (we move where you decide) or if you prefer they can be online (via skype or similar platform).
  • For individual or for groups.  Intensive or with partial schedule.
  • We provide a follow-up care for two months after the end of the course. We know that sometimes doubts come later.

Image from A.J.Canepa

Tailor-made courses for our clients:

Established courses:

Basic Formation

Basic course of introduction to R and Rstudio (20 h.)

Description of the R Software and the graphic interface RStudio

Download, installation and maintenance of R, RStudio and Packages

Definition of the workspace and records

Operation of the workspace and scripts ``House keeping``

Basic programming oriented to objects (vectors, lists, matrices and databases)

Importing data from different platforms

Basic data processing for statistical purposes

Visualization and exploration tools data

Fundamentals of Data Science with R and Rstudio (25 h.)

Definition and concepts of Data Science

Data types and structures in R

Introduction to programming in R and R Studio

Import and handling of data in R

Basic functions in R

Funciones avanzadas en R

Visual exploration for statistical purposes

Statistical modeling with R

Experimental design and analysis of data with R and Rstudio (30 h.)

Introduction to programming in R and Rstudio

• Introduction to the R Software and Rstudio
• Download, installation of R, RStudio and other packages
• Operation of the workspace and scripts

Basic programming oriented to objects

• Data import from different platforms
• Basic data processing for statistical purposes

Diseño experimental y muestreo

• Sampling theory, Replication, Pseudo-replication
• Experimental control: Design B.A.C.I.
• Practical data collection design activities
• Campaigns / Experiments with Real examples

Statistical Exploration of Data with R

• Introduction to the “ggplot2” package
• Visual exploration of data for statistical purposes
• Violation of the assumptions of linear models

Data analysis with R

• Introduction to statistical models
• Linear models
• Generalized linear models

Group work and final discussion table

• Presentation of the problem to be evaluated by the students
• Work in discrete groups using what has been learned
• Presentation and final discussion of the results

Multivariate Statistics with R (30 h.)

Introduction to programming in R and RStudio

• Introduction to the R Software and Rstudio
• Download, installation of R, RStudio and other packages
• Operation of the workspace and scripts

Exploratory data analysis

• Species data
• Environmental data
• Transformations

Association Measures and Matrices

• Q mode: calculation of distance matrices between objects
• R mode: calculation of distance matrices between variables

Cluster Analysis

• Hierarchical Cluster
• Non-hierarchical Cluster
• Comparison with environmental data
• Assemble of species

Independent ordering

• Principal component analysis
• Correspondence Analysis
• Non-metric multidimensional scaling

Dependent ordering

• Redundancy analysis
• Canonical correspondence analysis

Non-Parametric Statistics with R (26 h.)

Introduction to programming in R and Rstudio

• Introduction to the R Software and  Rstudio
• Download, installation of R, RStudio and other packages

• Operation of the workspace and scripts

Non-parametric statistics in the biological sciences

• Nominal data
• Ordinal data
• Interval data
• Rate data

Sign test

• Null hypothesis
• R code necessary to execute it
• Visual exploration of the Data
• Statistical analysis of signs

Chi-square test

• Null hypothesis
• R code necessary to execute it
• Visual exploration of the Data
• Statistical analysis of chi-square

Mann-Whitney U test

• Null hypothesis
• R code necessary to execute it
• Visual exploration of the Data
• Mann-Whitney statistical analysis

Wilcoxon test

• Null hypothesis
• R code necessary to execute it
• Visual exploration of the Data
• Statistical analysis of Wilcoxon

Kruskal-Wallis Test H for one way ANOVA by ranges

• Null hypothesis
• R code necessary to execute it
• Visual exploration of the Data
• Statistical analysis H of Kruskal-Wallis

Friedman test for two-way ANOVA by ranges

• Null hypothesis
• R code necessary to execute it
• Visual exploration of the Data
• Statistical analysis of Friedman

Range difference correlation coefficient from Spearman

• Null hypothesis
• R code necessary to execute it
• Visual exploration of the Data
• Statistical analysis of Spearman

Introduction to the analysis of Time Series with R (22 h.)

Introduction to programming in R and RStudio

• Introduction to the R Software and  Rstudio
• Download, installation of R, RStudio and other packages
• Operation of the workspace and scripts

Exploratory analysis of time series

• Data management of time series
• Time series visualization

Decomposition of time series

• Decomposition of non-seasonal series
• Decomposition of seasonal series
• Seasonal adjustment

Prediction using exponential smoothing

• Simple exponential smoothing
• Holt exponential smoothing
• Exponential smoothing of Holt-Winters

ARIMA models

• Differentiating a time series
• Selecting a potential ARIMA model
• Prediction using an ARIMA model

Ecological Data Analysis with R (40h.)

Introduction to programming in R and RStudio

• Introduction to the R Software and Rstudio
• Download, installation of R, RStudio and packages
• Operation of the workspace and scripts

Exploratory analysis

• Loading and pre-processing of data
• Statistical data exploration

Statistical analysis Univariate

• Linear models
• Generalized Linear Models (GLM)
• Additive and Generalized Additive Models (GAM)
• Time series

Statistical analysis Multivariate

• Statistical data exploration
• Association Measures and Matrices
• Cluster Analysis
• Independent ordering
• Dependent ordering

Oceanographic data processing with Ocean Data View (ODV) (22 h.)

Introduction to ODV software

• Software installation and access
• Type of data
• Derived variables
• Basic statistics

First steps of the Graphic Interface

• Accessing the help
• Application windows and meta-data
• Menus and drop-down menus
• Sampling and stations
• Mapping and drawings
• Settings

ODV collections

• Data models
• Examples and creation of collections

Import of data

• ODV files and forms
• NetCDF files
• World Ocean Database
• Data of ARGO buoys
• Other Databases

Data Exportation

• Returns in general
• ODV collections
• NetCDF files
• Iso-surface data
• Window data x, y, z

Derived Variables

• Pre-adjusted shunts
• Macros and Expressions
• Patches

Selection criteria

Stations map

• Properties
• Sections
• Station distribution

Data Window

• Properties
• Automatic zoom
• Changing the layout
• Graphics and Statistics
• Other analysis

Graphic objects

• Lines, polygons and more
• Handling graphic objects

Management of Collections

• Copied, renamed, stored and removed from collections
• Change properties of a collection
• Properties and editing of Information

Data Management netCDF

Tools and Miscellaneous

Advance Formation

Generalized Models (GLM and GAM) with R (35 h.)

Introducción a la programación en R y RStudio

• Introduction to the R Software and the Rstudio Graphical Interface
• Download, installation of R, RStudio and other packages
• Operation of the workspace and scripts

Linear and additive models

• Data exploration
• Outliers
• Colinearity
• Linear Regression, Anova, Ancova
• Additive modeling

Limitations of the models applied to the biological sciences

• Data exploration
• Assumptions

Generalized linear models (GLM)

• Data exploration
• Model selection
• Validation of the model
• Interpretation of the model

Modelos Aditivos Generalizados (GAM)

• Exploración de datos
• Selección del modelo
• Validación del modelo
• Interpretación del modelo

Generalized Mixed Models (GLMM and GAMM) with R (40 h.)

Introduction to programming in R and RStudio

• Introduction to the R Software and the Rstudio Graphical Interface
• Download, installation of R, RStudio and other packages
• Operation of the workspace and scripts

Limitations of the models applied to the biological sciences

• Data exploration
• Violation of Assumptions

Linear models and Generalized Additives (GLM and GAM)

• Data exploration
• Model selection
• Validation of the model
• Interpretation of the model

Mixed Linear Models and Generalized Additives (GLMM and GAMM)

• Working with Heterogeneity
• Variance Structures
• Variance structure selection protocol
• Nested Models
• Violation of Independence # 1
• Violation of Independence # 2

GLMM and GAMM for binary data

• Data exploration
• Model selection
• Validation of the model
• Interpretation of the model

GLMM and GAMM for counting data

• Data exploration
• Selection of the model
• Validation of the model
• Interpretation of the model

Multivariate Modeling with R (35 h.)

Introduction to programming in R and Rstudio

• Introduction to R Software and Rstudio
• Download, installation of R, RStudio and other packages
• Operation of the workspace and scripts

Association Measures and Matrices

• Q mode: calculation of distance matrices between objects
• R mode: calculation of distance matrices between variables

Cluster Analysis

• Hierarchical Cluster
• Non-hierarchical Cluster
• Comparison with environmental data
• Assemble of species

Independent ordering

• Principal component analysis
• Correspondence Analysis
• Non-metric multidimensional scaling

Dependent ordering

• Redundancy analysis
• Canonical correspondence analysis

Spatial analysis of ecological data

• Structures and spatial analysis
• Multivariate analysis of surface trends
• Spatial modeling
• Multi-scale ordering

Distribution models of species with R (40 h.)

Introduction to programming in R and RStudio

• Introduction to R Software and Rstudio
• Download, installation of R, RStudio and other packages
• Operation of the workspace and scripts

Introduction to the study of species distribution

• Nomenclature and niche concept
• Why and for what model the distribution of a species

Modeling tools in R

• Algorithms
• Packages
• Assembly modeling (assembly)

Modeling exercises of species distribution # 1

• Preparation of terrestrial biological data
• Preparation of terrestrial environmental data
• Preparation of algorithms
• Model adjustment
• Evaluation of the models
• Results analysis

Modeling exercises of species distribution # 2

• Preparation of marine biological data
• Preparation of marine environmental data
• Preparation of algorithms
• Model adjustment
• Evaluation of the models
• Results analysis

Modeling exercises of species distribution # 3

• Analysis of results with user’s own data

Advanced analysis of time series with R (41 h.)

Introduction to programming in R and RStudio

• Introduction to R Software and Rstudio
• Download, installation of R, RStudio and other packages
• Operation of the workspace and scripts

Characteristics of time series

• Nature of time series data
• Statistical models for time series
• Dependency measures
• Stationary time series
• Correlation estimation

Regression of time series and analysis of exploratory data

• Classical linear regression in the context of time series
• Analysis of exploratory data other packages
• Smoothing in the context of time series

ARIMA models

• Auto-correlation and partial auto-correlation
• Prediction
• Correlation estimation

Spectral analysis and filtering

• Circular behavior and periodicity
• Spectral density and periodograms
• Parametric and non-parametric spectral estimation
• Linear filters
• Outdated regression models

Dynamic linear models

• Gauss linear model
• Filtering, Smoothing and Prediction
• Structural models
• Stochastic volatility
• Bayesian analysis

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Statistical Consulting

The Reason Why

Scientific research needs adequate sampling/experimental planning and up-to-date data analysis techniques for handling, processing and modeling your data and also the most effective communication platform of your results.

What We Offer

We support you from the start of your data journey giving advice in sampling and experimental design, data processing and up-to-date statistical techniques. We also advise you how to make your findings shine, by using the most appropriate visualization graphics.

The tools

We have a wide background in scientific research and statistical analysis of data. We work mostly in R, the most important software to perform statistical programming and data science. The R language gives you complete control over the design of the planning and subsequent handling of data, statistical analysis and offers you cutting-edge techniques to better visualize your results.

Consulting Services

  • Advice and design of sampling and experimental protocols.
  • Statistical support for research. Selection of the statistical test and analysis protocol most appropriate for each study case
  • Reviewing and understanding the methodology published in scientific manuscripts.
  • Support for congress/workshop/meetings presentations.

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Data Analysis

The Reason Why

Today having a big amount of good data is not enough. Newly data science techniques have evolved providing new algorithms and visualizations graphics not available few years ago. Thus, we can get the full information behind the data you have and we can collaborate any time. Those days when a person did everything for its own, are over. Teamwork makes knowledge really move forward.

What We Offer

We offer to you the data analysis process from the beginning until the final product. We collaborate with you step by step from the process of data collection until the manuscript just prepared for publishing in a journal. Do you need the entire process or just some steps? You choose!

The tools

The key is our background in scientific research and statistical analysis of data together with continuous communication. The results of our work will be shared and discussed with you during the whole data analysis process. We provide comprehensive follow-up until the required purpose has been achieved.

Data Analysis Services

  • Raw data entry.
  • Exploratory data analysis.
  • Post-processing and data cleaning.
  • Statistical analysis (Classical: univariate, multivariate, modeling, parametric and non-parametric; Bayesian: Under preparation).
  • Data visualization.
  • Producing reports (Scientific manuscript, conference proceedings, etc).

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