Data Research Statistical Approaches

Data Research
  • 13 Videos | 48m 19s
  • Includes Assessment
  • Earns a Badge
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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

  • Playable
    1. 
    Course Overview
    1m 52s
    UP NEXT
  • Playable
    2. 
    Role of Statistics in Data Research
    3m 33s
  • Locked
    3. 
    Discrete vs. Continuous Distribution
    3m 16s
  • Locked
    4. 
    PDF and CDF
    6m 33s
  • Locked
    5. 
    Binomial Distribution
    3m 16s
  • Locked
    6. 
    Interval Estimation
    3m 16s
  • Locked
    7. 
    Point and Interval Estimation
    3m 35s
  • Locked
    8. 
    Data Visualization Techniques
    2m 11s
  • Locked
    9. 
    Data Visualization Using R
    2m 39s
  • Locked
    10. 
    Data Integration Techniques
    3m 11s
  • Locked
    11. 
    Creating Plots
    3m 59s
  • Locked
    12. 
    Missing Values and Outliers
    3m 5s
  • Locked
    13. 
    Exercise: Statistical Methods for Data Research
    2m 23s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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