CompTIA Data+: Data Acquisition & Cleansing
CompTIA
| Intermediate
- 20 videos | 2h 35m 2s
- Includes Assessment
- Earns a Badge
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
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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
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57sIn this video, we will discover the key concepts covered in this course. FREE ACCESS
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9m 6sAfter 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 Techniques8m 59sUpon 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 3sAfter 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 38sUpon 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 Tools11m 46sAfter 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 Load7m 12sIn this video, find out how to perform a delta load. FREE ACCESS
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8. Data Acquisition Application Programming Interfaces (APIs)15m 6sUpon 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 API6m 6sDiscover how to perform data acquisition with an API. FREE ACCESS
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10. The Need for Clean Data7m 26sAfter completing this video, you will be able to identify reasons for cleaning datasets. FREE ACCESS
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11. Data Cleansing11m 58sUpon 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 Data4m 59sFind out how to clean duplicate data in datasets. FREE ACCESS
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13. Data Redundancy7m 27sAfter 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 Values7m 4sUpon 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 Data5m 31sAfter 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 Data6m 48sUpon 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. Outliers7m 50sAfter 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 Outliers5m 42sLearn how to find and remove outliers in datasets. FREE ACCESS
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19. Data Type Validation4m 41sUpon 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 Summary44sIn this video, we will summarize the key concepts covered in this course. FREE ACCESS
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