Predictive Modeling: Predictive Analytics & Exploratory Data Analysis

Predictive Analytics    |    Intermediate
  • 9 videos | 40m 36s
  • Includes Assessment
  • Earns a Badge
Rating 4.1 of 50 users Rating 4.1 of 50 users (50)
Explore the machine learning predictive analytics, exploratory data analytics, and different types of data sets and variables in this 9-video course. Discover how to implement predictive models and manage missing values and outliers by using Python frameworks. Key concepts covered in this course include predictive analytics, a branch of advanced analytics, and its process flow, and learning how analytical base tables can be used to build and score analytical models. Next, you will discover business problems that can be resolved by using predictive modeling; how to build predictive models with the Python framework; and learn the essential features of exploratory data analysis. Then learn about data sets, collections of data corresponding to the content of a single database or a single statistical data matrix, and then learn the variables of the different types of data sets including univariate, bivariate, and multivariate data and analytical approaches that can be implemented with them. Finally, you will learn about methods that can be used to manage missing values and outliers in data sets.

WHAT YOU WILL LEARN

  • Define the predictive analytics and describe its process flow
    Describe analytical base table and how it can be used to build and score analytical models
    Identify the business problems that can be resolved using predictive modeling
    Build predictive models using the python framework
  • List essential features of exploratory data analysis
    Describe univariate, bivariate, and multivariate data and analytical approaches that can be implemented with them
    Specify methods that can be used to manage missing values and outliers in datasets
    List applications of predictive analytics, describe analytical base tables, list predictive models, and specify variable selection methods

IN THIS COURSE

  • 1m 30s
  • 8m 23s
    In this video, you will learn how to define predictive analytics and describe its process flow. FREE ACCESS
  • Locked
    3.  Analytical Base Table
    4m 45s
    After completing this video, you will be able to describe an analytical base table and how it can be used to build and score analytical models. FREE ACCESS
  • Locked
    4.  Business Problems and Predictive Modeling
    7m 15s
    To identify the business problems that can be resolved using predictive modeling, consult resources that explain the process. FREE ACCESS
  • Locked
    5.  Predictive Modeling with Python
    3m 27s
    In this video, you will learn how to build predictive models using the Python framework. FREE ACCESS
  • Locked
    6.  Exploratory Data Analysis
    3m 42s
    After completing this video, you will be able to list essential features of exploratory data analysis. FREE ACCESS
  • Locked
    7.  Dataset and Variables Types
    5m 56s
    Upon completion of this video, you will be able to describe univariate, bivariate, and multivariate data and the analytical approaches that can be implemented with them. FREE ACCESS
  • Locked
    8.  Missing Values and Outlier Management
    3m 47s
    After completing this video, you will be able to specify methods for managing missing values and outliers in datasets. FREE ACCESS
  • Locked
    9.  Exercise: Predictive Modeling with Python
    1m 52s
    Upon completion of this video, you will be able to list applications of predictive analytics, describe analytical base tables, list predictive models, and specify variable selection methods. FREE ACCESS

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