CompTIA Data+: Data Acquisition & Cleansing

CompTIA    |    Intermediate
  • 20 videos | 2h 35m 2s
  • Includes Assessment
  • Earns a Badge
Rating 4.6 of 13 users Rating 4.6 of 13 users (13)
Data, when coming in from a source en masse, is rarely structured the way that data analysts would like it to be. When you consider the multitude of sources that data comes from, it would be highly unrealistic to assume that you could take a tranche of data and begin working with it without some sort of processing to make it more useful. In this course, you will explore data acquisition and cleansing, beginning with data integration and data integration tools, focusing on the roles and characteristics of the extract, transform, load (ETL) and extract, load, transform (ELT) processes. Then you will examine tools and methods such as delta load and data acquisition application programming interfaces (APIs). Next, you will learn how to clean datasets and investigate common data issues, including data redundancy, missing values, non-parametric data, and outliers. Finally, you will take a look at key characteristics of data type validation. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Outline the key aspects of data integration in data acquisition activities
    Outline various data integration techniques
    Describe the role and characteristics of extract, transform, load (etl)
    Identify the role and characteristics of extract, load, transform (elt)
    List various data integration tools and their uses
    Perform a delta load
    Describe various data acquisition apis, the purpose of apis, and different types of apis
    Perform data acquisition with an api
    Identify reasons for cleaning datasets
  • Describe the role and characteristics of and common reasons for data cleansing
    Clean duplicate data in datasets
    Outline the characteristics of data redundancy in datasets and identify common reasons for addressing redundancy
    Outline the key aspects of missing values in datasets and common reasons for addressing missing values
    Identify characteristics of bad data and common reasons for addressing bad data
    Describe characteristics of non-parametric data in datasets and common reasons for addressing non-parametric data
    Outline characteristics of outliers in datasets and common reasons for addressing outliers
    Find and remove outliers in datasets
    Outline key characteristics of data type validation and reasons for performing data type validation
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 57s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 9m 6s
    After completing this video, you will be able to outline the key aspects of data integration in data acquisition activities. FREE ACCESS
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    3.  Data Integration Techniques
    8m 59s
    Upon completion of this video, you will be able to outline various data integration techniques. FREE ACCESS
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    4.  Extract, Transform, Load (ETL)
    12m 3s
    After completing this video, you will be able to describe the role and characteristics of extract, transform, load (ETL). FREE ACCESS
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    5.  Extract, Load, Transform (ELT)
    13m 38s
    Upon completion of this video, you will be able to identify the role and characteristics of extract, load, transform (ELT). FREE ACCESS
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    6.  Data Integration Tools
    11m 46s
    After completing this video, you will be able to list various data integration tools and their uses. FREE ACCESS
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    7.  Performing a Delta Load
    7m 12s
    In this video, find out how to perform a delta load. FREE ACCESS
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    8.  Data Acquisition Application Programming Interfaces (APIs)
    15m 6s
    Upon completion of this video, you will be able to describe various data acquisition APIs, the purpose of APIs, and different types of APIs. FREE ACCESS
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    9.  Performing Data Acquisition with an API
    6m 6s
    Discover how to perform data acquisition with an API. FREE ACCESS
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    10.  The Need for Clean Data
    7m 26s
    After completing this video, you will be able to identify reasons for cleaning datasets. FREE ACCESS
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    11.  Data Cleansing
    11m 58s
    Upon completion of this video, you will be able to describe the role and characteristics of and common reasons for data cleansing . FREE ACCESS
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    12.  Cleaning Duplicate Data
    4m 59s
    Find out how to clean duplicate data in datasets. FREE ACCESS
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    13.  Data Redundancy
    7m 27s
    After completing this video, you will be able to outline the characteristics of data redundancy in datasets and identify common reasons for addressing redundancy. FREE ACCESS
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    14.  Missing Values
    7m 4s
    Upon completion of this video, you will be able to outline the key aspects of missing values in datasets and common reasons for addressing missing values. FREE ACCESS
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    15.  Characteristics of Bad Data
    5m 31s
    After completing this video, you will be able to identify characteristics of bad data and common reasons for addressing bad data. FREE ACCESS
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    16.  Non-Parametric Data
    6m 48s
    Upon completion of this video, you will be able to describe characteristics of non-parametric data in datasets and common reasons for addressing non-parametric data. FREE ACCESS
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    17.  Outliers
    7m 50s
    After completing this video, you will be able to outline characteristics of outliers in datasets and common reasons for addressing outliers. FREE ACCESS
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    18.  Finding and Removing Outliers
    5m 42s
    Learn how to find and remove outliers in datasets. FREE ACCESS
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    19.  Data Type Validation
    4m 41s
    Upon completion of this video, you will be able to outline key characteristics of data type validation and reasons for performing data type validation. FREE ACCESS
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    20.  Course Summary
    44s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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