Data Research Exploration Techniques

Data Research Exploration Techniques

• Course Overview
• specify the essential features and benefits provided by implementing exploratory data analysis
• recognize the prominent approaches that can be adopted to implement data exploration
• install and prepare R for data exploration
• demonstrate how to implement data exploration using R
• implement data exploration using plots in R
• specify the essential packages provided by Python that can be used to explore data
• implement data exploration using Python's data exploration packages
• describe the approach of implementing data research using linear algebra
• work with vectors and metrics using Python and R
• explore data using R, explore data with Python packages, and work with vectors using Python

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 Techniques

Data Research Techniques

• Course Overview
• recall the fundamental concepts of data research that can be applied on data inference
• identify implementation steps for drawing data hypothesis conclusions
• define the values, variables, and observations that are associated with data from the perspective of quantitative and classification variables
• specify the different scales of standard measurements with a critical comparison between the Generic and JMP model
• identify the key features of non-experimental and experimental research approaches using real-time scenarios
• compare the differences between the descriptive and inferential statistical analysis
• illustrate the prominent usages of the different types of inferential tests
• describe the approaches and the steps involved in the implementation of clinical data research using real-time scenarios
• describe the approaches and the steps involved in the implementation of sales data research using real-time scenarios
• specify the key features of experimental and non-experimental research and recall the differences between descriptive and inferential statistical analysis

To master data science, you must learn the techniques surrounding data research. In this 10-video course, learners will discover how to apply essential data research techniques, including JMP measurement, and how to valuate data by using descriptive and inferential methods. Begin by recalling the fundamental concept of data research that can be applied on data inference. Then learners look at steps that can be implemented to draw data hypothesis conclusions. Examine values, variables, and observations that are associated with data from the perspective of quantitative and classification variables. Next, view the different scales of standard measurements with a critical comparison between generic and JMP models. Then learn about the key features of nonexperimental and experimental research approaches when using real-time scenarios. Compare differences between descriptive and inferential statistical analysis and explore the prominent usage of different types of inferential tests. Finally, look at the approaches and steps involved in the implementation of clinical data research and sales data research using real-time scenarios. The concluding exercise involves implementing data research.

Data Research Statistical Approaches

Data Research Statistical Approaches

• Course Overview
• describe the features provided by statistical methods and approaches in data research
• identify the relevance of discrete vs continuous distribution in simplifying data research
• recognize the features of PDF and CDF from the perspective of data research
• implement binomial distribution using R
• specify the types of interval estimation that can be used to enhance data research
• implement point and interval estimation using R
• describe the relevance of data visualization techniques in projecting the outcome of data research
• plot visualizations using R to depict the outcome of data research graphically
• recall the data integration techniques that facilitate using statistical methods
• create Histograms, Scatter plots, and Box plots using Python libraries
• implement missing values and outliers using Python
• implement data research using various statistical approaches

This 12-video course explores implementation of statistical data research algorithms using R to generate random numbers from standard distribution, and visualizations using R to graphically represent the outcome of data research. You will learn to apply statistical algorithms like PDF (probability density function), CDF (cumulative distribution function), binomial distribution, and interval estimation for data research. Learners become able to identify the relevance of discrete versus continuous distribution in simplifying data research. This course then demonstrates how to plot visualizations by using R to graphically predict the outcomes of data research. Next, learn to use interval estimation to derive an estimate for an unknown population parameter, and learn to implement point and interval estimation by using R. Learn data integration techniques to aggregate data from different administrative sources. Finally, you will learn to use Python libraries to create histograms, scatter, and box plot; and use Python to implement missing values and outliers. The concluding exercise involves loading data in R, generating a scatter chart, and deleting points outside the limit of x vector and y vector.