Data Mining and Decision Making: Data Preparation & Predictive Analytics

Data Science 2021    |    Intermediate
  • 12 videos | 41m 6s
  • Includes Assessment
  • Earns a Badge
Likes 13 Likes 13
Data preparation transforms raw data into datasets with stable structures suitable for predictive analytics. This course shows you how to produce clean datasets with valid data to ensure accurate insights for sound business decision-making. Examine the role data sources, systems, and storage play in descriptive analytics. Explore best practices used for data preparation, including data collection, validation, and cleaning. Additionally, investigate some more advanced data exploration and visualization techniques, including the use of different chart types, summary statistics, and feature engineering. Upon completing this course, you'll know how to gather, store, and analyze data to make reliable predictions and smart business decisions.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    recognize the primary industrial and commercial data sources around us and use this knowledge to select a suitable data source for your business processes
    define key characteristics and requirements for a reliable data collection pipeline
    describe the purpose of the data validation process and name the major steps involved in it
    outline several ways to clean a dataset and describe why data cleaning is necessary
    specify how summary statistics can be used to explore and prepare a dataset and define what's meant by measures of frequency and central tendency
  • specify how summary statistics can be used to explore and prepare a dataset and describe measures of dispersion and statistics
    identify how data visualization done correctly can become a key business driver
    name advanced visualization techniques and describe their use cases
    outline how feature generation can be used to facilitate business decision-making
    outline how feature reduction can be useful when producing business analytics
    summarize the key concepts covered in this course

IN THIS COURSE

  • 1m 13s
  • 7m 20s
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    3.  Data Collection Skills and Methods
    5m 31s
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    4.  Data Validation Best Practices
    3m 9s
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    5.  Data Cleaning Techniques
    2m 59s
    In this video, you'll learn how to outline several ways to clean a dataset and describe why data cleaning is necessary. You'll learn that data cleansing removes static columns and low variance columns. The video outlines this and also provides the third and last important aspect of handling missing data points. FREE ACCESS
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    6.  Data Exploration: Summary Statistics
    2m 54s
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    7.  Data Exploration: Summary Statistics II
    4m 19s
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    8.  Data Exploration: The Power of Visualization
    3m 47s
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    9.  Data Exploration: Advanced Visualization
    2m 55s
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    10.  Feature Engineering: Feature Generation
    3m 10s
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    11.  Feature Engineering: Feature Reduction
    3m 3s
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    12.  Course Summary
    47s

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