Cursos de ciencia de datos

Learn the basics of A/B testing in R, including how to design experiments, analyze data, predict outcomes, and present results through visualizations.

Learn key object-oriented programming concepts, from basic classes and objects to advanced topics like inheritance and polymorphism.

Learn how to analyze survey data with Python and discover when it is appropriate to apply statistical tools that are descriptive and inferential in nature.

Learn how to produce interactive web maps with ease using leaflet.

In this course youll learn how to perform inference using linear models.

Learn to build pipelines that stand the test of time.

Learn about experimental design, and how to explore your data to ask and answer meaningful questions.

Learn how to use Python parallel programming with Dask to upscale your workflows and efficiently handle big data.

Learn how bonds work and how to price them and assess some of their risks using the numpy and numpy-financial packages.

Discover branches and remote repos for version control in collaborative software and data projects using Git!

This course is for R users who want to get up to speed with Python!

Learn how to manipulate, visualize, and perform statistical tests through a series of HR analytics case studies.

Gain an overview of all the skills and tools needed to excel in Natural Language Processing in R.

Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.

Learn how to import, clean and manipulate IoT data in Python to make it ready for machine learning.

Learn how to translate your SAS knowledge into R and analyze data using this free and powerful software language.

Learn how to create interactive data visualizations, including building and connecting widgets using Bokeh!

Transition from MATLAB by learning some fundamental Python concepts, and diving into the NumPy and Matplotlib packages.

Discover the power of discrete-event simulation in optimizing your business processes. Learn to develop digital twins using Pythons SimPy package.

GAMs model relationships in data as nonlinear functions that are highly adaptable to different types of data science problems.

Learn the principles of feature engineering for machine learning models and how to implement them using the R tidymodels framework.

Get ready to categorize! In this course, you will work with non-numerical data, such as job titles or survey responses, using the Tidyverse landscape.

Explore HR data analysis in Tableau with this case study.

Strengthen your knowledge of the topics covered in Manipulating Time Series in R using real case study data.

In ecommerce, increasing sales and reducing expenses are top priorities. In this case study, youll investigate data from an online pet supply company.

This course will introduce the support vector machine (SVM) using an intuitive, visual approach.

In this course, you’ll learn to classify, treat and analyze time series; an absolute must, if you’re serious about stepping up as an analytics professional.

Learn to use the Bioconductor package limma for differential gene expression analysis.

In this course youll learn how to leverage statistical techniques for working with categorical data.

Learn how to analyse and interpret ChIP-seq data with the help of Bioconductor using a human cancer dataset.