This course explores EDA (exploratory data analysis) and data research techniques necessary to communicate with data management professionals involved in application, implementation, and facilitation of the data research mechanism. You will examine EDA as an important way to analyze extracted data by applying various visual and quantitative methods. In this 10-video course, learners acquire data exploration techniques to derive different data dimensions to derive value from the data. You will learn proper methodologies and principles for various data exploration techniques, analysis, decision-making, and visualizations to gain valuable insights from the data. This course covers how to practically implement data exploration by using R random number generator, Python, linear algebra, and plots. You will use EDA to build learning sets which can be utilized by various machine learning algorithms or even statistical modeling. You will learn to apply univariate visualization, and to use multivariate visualizations to identify the relationship among the variables. Finally, the course explores dimensionality reduction to apply different dimension reduction algorithms to deduce the data in a state which is useful for analytics.
Data Research Exploration Techniques