list of statistical tools for data analysis

Knowledge of excel will help you in your data science career. Excel is still a basic tool in data science and analytics. It is very easy to learn and master excel. All the features such as exploring data, summarizing data, and visualizing data through various graphical tools are done in excel. There are many free online tutorials available that teach about Excel and VBA through which you can master excel. Many toolbox algorithms can be used on data sets that are too big to be stored in memory.Excel still attracts people to do data analysis and yes it is indispensable still as an analytics tool. Native Simulink blocks let you use predictive models with simulations and Model-Based design. You can apply interpretability techniques such as partial dependence plots, Shapley values and LIME, and automatically generate C/C++ code for embedded deployment.

The toolbox provides supervised, semi-supervised, and unsupervised machine learning algorithms, including support vector machines (SVMs), boosted decision trees, shallow neural nets, k-means, and other clustering methods.

Regression and classification algorithms let you draw inferences from data and build predictive models either interactively, using the Classification and Regression Learner apps, or programmatically, using AutoML.įor multidimensional data analysis and feature extraction, the toolbox provides principal component analysis (PCA), regularization, dimensionality reduction, and feature selection methods that let you identify variables with the best predictive power. You can use descriptive statistics, visualizations, and clustering for exploratory data analysis fit probability distributions to data generate random numbers for Monte Carlo simulations, and perform hypothesis tests. Statistics and Machine Learning Toolbox™ provides functions and apps to describe, analyze, and model data.

List of statistical tools for data analysis