2. Dealing With Missing Values - Predictive Modeling and Analytics

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2. Dealing With Missing Values - Predictive Modeling and Analytics
Advanced Business Analytics Specialization
Welcome to the second course in the Data Analytics for Business specialization!

This course will introduce you to some of the most widely used predictive modeling techniques and their core principles. By taking this course, you will form a solid foundation of predictive analytics, which refers to tools and techniques for building statistical or machine learning models to make predictions based on data. You will learn how to carry out exploratory data analysis to gain insights and prepare data for predictive modeling, an essential skill valued in the business.

You’ll also learn how to summarize and visualize datasets using plots so that you can present your results in a compelling and meaningful way. We will use a practical predictive modeling software, XLMiner, which is a popular Excel plug-in. This course is designed for anyone who is interested in using data to gain insights and make better business decisions. The techniques discussed are applied in all functional areas within business organizations including accounting, finance, human resource management, marketing, operations, and strategic planning.

The expected prerequisites for this course include a prior working knowledge of Excel, introductory level algebra, and basic statistics.
Regression Analysis, Data Cleansing, Predictive Modelling, Exploratory Data Analysis
this course teach you about the technical of using tools for predictive modeling. very useful for you who want to learn the fundamental of analytics.,It has been very exciting and an eye-opening for me. I am getting into the world of data analytics gradually. Thanks for this great opportunity.
At the end of this module students will be able to: 1. Carry out exploratory data analysis to gain insights and prepare data for predictive modeling 2. Summarize and visualize datasets using appropriate tools 3. Identify modeling techniques for prediction of continuous and discrete outcomes. 4. Explore datasets using Excel 5. Explain and perform several common data preprocessing steps 6. Choose appropriate graphs to explore and display datasets
2. Dealing With Missing Values - Predictive Modeling and Analytics
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