Project on Data Visualization with R

Data Visualisation is a vital tool that can unearth possible crucial insights from data. If the results of an analysis are not visualized properly, they will not be communicated effectively to the desired audience. In this introductory course on data visualization in R, you will learn about the different resources you can use to explore and showcase your data visually. Most importantly, you'll learn how to use ggplot2, a powerful and immensely popular data visualization library for R.

R is a language and environment for statistical computing and graphics. R is also extremely flexible and easy to use when it comes to creating visualizations. One of its capabilities is to produce good-quality plots with minimum codes.

In this course, you will learn about the Grammar of Graphics, a system for describing and building graphs, and how the ggplot2 data visualization package for R applies this concept to basic bar charts, histograms, pie charts, scatter plots, line plots, and box plots. You will also learn how to further customize your charts and plots using themes and other techniques. You will then learn how to use another R data visualization package called Leaflet to create map plots, a unique way to plot data based on geolocation data.

Anyone who wants to learn Project on Data Visualization with R

Data Analyst/Data Scientist - Python/R Head Analytics - Machine Learning Senior Manager - Analytics - SQL/R/Python/Visualization

Certificate

– Importance of analytics and visualization in the era of data abundance. – Review of probability, statistics and random processes. - Brief introduction to estimation theory. – Introduction to machine learning, supervised and unsupervised learning, gradient descent, overfitting, regularization. – Clustering techniques: K-means, Gaussian mixture models and expectation-maximization, agglomerative clustering, evaluation of clustering - Rand index, mutual information based scores, Fowlkes-Mallows index – Regression: Linear models, ordinary least squares, ridge regression, LASSO, Gaussian Processes regression. – Supervised classification methods: K-nearest neighbor, naive Bayes, logistic regression, decision tree, support vector machine. – Introduction to artificial neural networks (ANNs), deep NNs, convolutional neural network (CNN). – Data visualization: Basic principles, categorical and continuous variables. – Exploratory graphical analysis - Creating static graphs, animated visualizations - loops, GIFs and Videos. – Data visualization in Python and R, examples.

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