Using Data to Find Data: Correction & Categorization

Data Science    |    Intermediate
  • 12 videos | 50m 17s
  • Includes Assessment
  • Earns a Badge
Rating 4.1 of 7 users Rating 4.1 of 7 users (7)
Data professionals working with various data management systems must be able to implement data correction by using R and have a good understanding of data and data management systems. In this 12-video course, learners explore how to apply and implement various essential data correction techniques; to follow transformation rules; and to use deductive correction techniques and predictive modeling by using critical data and analytical approaches. Learn more about data wrangling, essentially the process of transforming and mapping data into another format to ensure that data are appropriate for analytical requirements. Along the way, you will learn key terms and concepts, including how to design data dimension; dimensional data design; cleansing data, and cleansing data with Python; data operations for fact finding; and common data operations for fact-finding. Next, learn about data categorization with Python; data visualization in general; and data visualization with Python. In a concluding exercise, you create a series data set by using Python; create a data frame using the series data; and, finally, calculate the standard deviation of the data frame.

WHAT YOU WILL LEARN

  • Describe approaches for facilitating data management with emphasis on data layout
    Recognize the benefits of dimensional data design
    Demonstrate the methods of cleansing data using python libraries
    Describe how to facilitate common operations of finding data facts using python
    Demonstrate data wrangling approaches using python
    Recognize how to facilitate variance measurement using python
  • Specify the importance of data categorization and describe data categorization types
    Work with classification to facilitate data categorization using python
    Implement data categorization using the clustering
    Use python to facilitate data visualization and depict data graphs
    Wrangle and categorize data using python and clustering

IN THIS COURSE

  • 1m 21s
  • 5m 18s
    After completing this video, you will be able to describe approaches for facilitating data management with an emphasis on data layout. FREE ACCESS
  • Locked
    3.  Dimensional Data Design
    4m 30s
    Upon completion of this video, you will be able to recognize the benefits of data design in multiple dimensions. FREE ACCESS
  • Locked
    4.  Data Cleansing Using Python
    5m 36s
    During this video, you will learn how to cleanse data using Python libraries. FREE ACCESS
  • Locked
    5.  Common Data Operations
    5m 2s
    Upon completion of this video, you will be able to describe how to find data facts using Python. FREE ACCESS
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    6.  Data Wrangling with Python
    4m 3s
    During this video, you will learn how to apply data wrangling approaches using Python. FREE ACCESS
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    7.  Data Variance Measurement with Python
    4m
    After completing this video, you will be able to recognize how to measure variance using Python. FREE ACCESS
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    8.  Data Categorization
    5m 39s
    Upon completion of this video, you will be able to specify the importance of data categorization and describe different types of data categorization. FREE ACCESS
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    9.  Data Categorization Using Classification
    4m 20s
    Find out how to work with classification to help data categorization using Python. FREE ACCESS
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    10.  Data Categorization Using Clustering
    3m 50s
    In this video, you will learn how to implement data categorization using clustering. FREE ACCESS
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    11.  Data Visualization Using Python
    3m 10s
    In this video, find out how to use Python to facilitate data visualization and depict data in graphs. FREE ACCESS
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    12.  Exercise: Wrangle and Categorize Data
    3m 29s
    In this video, learn how to organize and categorize data using Python and clustering. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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