# Data Research Statistical Approaches

Data Research    |    Intermediate
• 13 videos | 42m 49s
• Includes Assessment
Rating 3.3 of 14 users (14)
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

• Upon completion of this video, you will be able to describe the features provided by statistical methods and approaches to data research.
• 3.  Discrete vs. Continuous Distribution
In this video, learn how to identify the relevance of discrete or continuous distribution in simplifying data research.
• 4.  PDF and CDF
After completing this video, you will be able to recognize the features of PDFs and CDFs from the perspective of data research.
• 5.  Binomial Distribution
In this video, you will learn how to implement binomial distribution using R.
• 6.  Interval Estimation
Upon completion of this video, you will be able to specify the types of interval estimation that can enhance data research.
• 7.  Point and Interval Estimation
In this video, you will learn how to use point and interval estimation using R.
• 8.  Data Visualization Techniques
After completing this video, you will be able to describe the relevance of data visualization techniques in data research outcomes.
• 9.  Data Visualization Using R
In this video, you will plot visualizations using R to depict the outcome of your data research graphically.
• 10.  Data Integration Techniques
After completing this video, you will be able to recall the data integration techniques that facilitate using statistical methods.
• 11.  Creating Plots
During this video, you will learn how to create histograms, scatter plots, and box plots using Python libraries.
• 12.  Missing Values and Outliers
In this video, learn how to deal with missing values and outliers using Python.
• 13.  Exercise: Statistical Methods for Data Research
Find out how to implement data research using various statistical approaches.

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