# Data Research Statistical Approaches

Data Research    |    Intermediate
• 13 Videos | 48m 19s
• Includes Assessment
Likes 13
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.

## WHAT YOU WILL LEARN

• 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

## IN THIS COURSE

• 1.
Course Overview
• 2.
Role of Statistics in Data Research
• 3.
Discrete vs. Continuous Distribution
• 4.
PDF and CDF
• 5.
Binomial Distribution
• 6.
Interval Estimation
• 7.
Point and Interval Estimation
• 8.
Data Visualization Techniques
• 9.
Data Visualization Using R
• 10.
Data Integration Techniques
• 11.
Creating Plots
• 12.
Missing Values and Outliers
• 13.
Exercise: Statistical Methods for Data Research

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