Lesson List

Program Overview

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.

Predicting Diamond Prices

The Analytical Problem Solving Framework

Selecting an Analytical Methodology

Linear Regression

Practice Project

Predicting Catalog Demand

Understanding Data

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.

Data Issues

Data Formatting

Data Blending

Practice Project

Create an Analytical Dataset

Selecting Predictor Variables

Select Location of a New Petstore

Intro to Data Visualization

Design

Data Visualizations in Tableau

Making Dashboards Stories in Tableau

Visualizing Movie Data

Classification Problems

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.

Binary Classification Models

Non-Binary Classification Models

Predicting Default Risk

AB Testing Fundamentals

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.

Randomized Design Tests

Matched Pair Design Tests

Matched Pair Practice

AB Test a New Menu Launch

Fundamentals of 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.

ETS Models

ARIMA Models

Analyzing and Visualizing Results

Forecast Video Game Sales

Segmentation Fundamentals

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.

Preparing Data for Clustering

Variable Reduction

Clustering Models

Validating and Applying Clusters

Segmenting the Countries of the World

Combining Predictive Techniques

Go to course home
01. Program Hosts – Course Overview

Overview

The course will take you through two key analytical concepts to help you understand any business situation and help you choose the correct techniques to analyze your data.

  1. Cross Industry Standard Process for Data Mining (CRISP-DM)
  2. Predictive Methodology Map

crisp explanation

CRISP-DM

This framework was originally developed by data miners in order to generalize the common approaches to defining and analyzing a problem. In this course, we will call CRISP-DM the “Problem Solving Framework”.

The framework is made up of 6 steps:

  1. Business Issue Understanding
  2. Data Understanding
  3. Data Preparation
  4. Analysis/Modeling
  5. Validation
  6. Presentation/Visualization

crisp

 

This framework is based upon the CRISP-DM framework: Cross Industry Standard Process for Data Mining

method map explain

Methodology Map

The methodology map is a guide to determine the appropriate analytical technique(s) to solve a particular business question or problem.

The map outlines two main scenarios for a business problem:

  1. Data analysis
  2. Predictive analysis

Data analysis refers to the more standard approaches of blending together data and reporting on trends and statistics and helps answer business questions that involve understanding more about the dataset such as “On average, how many people order coffee and a donut per transaction in my store in any given week?”

Predictive analysis will help businesses predict future behavior based on existing data such as “Given the average coffee order, how much coffee can I expect to sell next week if I were to add a new brand of coffee?”

It’s highly suggested you download and print this map to help you figure out what kind of analytical techniques you should use given any business problem you may work on in your career.

method map

Analyst Methodology Map

lr

Linear Regression

You will then learn how to create linear regression models to help you predict numerical data such as sales. You’ll dive deep into these concepts:

  1. Linear relationship
  2. Multiple-R squared and p-values
  3. Significant coefficients
  4. Modeling categorical variables

project

Feel free to skip around this course if you’re already familiar with some of these topics already. You do not have to go through every video in this course.

To help you gauge what techniques you need for the project, the project will focus on linear regressions and categorical variables. If you’re already familiar with these techniques, feel free to skip ahead and start working on the project.

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