Predictive Analytics for Business (Udacity)

Predictive Analytics for Business (Udacity) digital download.

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Predictive Analytics for Business (Udacity)

Type: Digital download

Salepage: Udacity

Udacity - Predictive Analytics for Business

Udacity - Predictive Analytics for Business

Learn to apply predictive analytics and business intelligence to solve real-world business problems

What You Will Learn


Prerequisite Knowledge

Students who enroll should be familiar with algebra and descriptive statistics and have experience working with data in Excel. Working knowledge of SQL and Tableau is a plus, but not required.

Problem Solving with Advanced Analytics

In this course, we give you a framework to help you organize and plan your analytical approach. We also introduce both simple Linear Regression and Multiple Linear Regression.

Data Wrangling

Data Wrangling is at the core of all data activity. In this course you learn how to work with different data types,dirty data, and outliers. You will also learn how to reformat data and join data from different sources together.

Classification Models

Classification models are a powerful tool for business analyst. In this course, you learn more about binary and non-binary classification models and how to use them to drive business insights.

A/B Testing

Helping businesses make the best decisions is an essential part of Business Analysis. Planning and executing the analysis of an AB test allow you to provide confident recommendations. In this course, you learn how to create, execute, and analyze an AB test.

Time Series Forecasting

Time Series Forecasting is a powerful analytical tool. In this course, you learn how ETS and ARIMA models are used to forecast data and how they deal with trends and seasonality. These skills will be evaluated in the final project.

Segmentation and Clustering

Segmentation and Clustering are effective methods for finding patterns in your data. In this course, you learn how to prepare data to be clustered appropriately and interpret results.