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Data Science for Marketing Analytics Practice Exam

Data Science for Marketing Analytics

Marketers can acquire better insights into their customers' beliefs, opinions, and attitudes. They can also screen how customers respond to marketing campaigns and whether or not they're drawing in with their business.


Skills Required

Information on Computer Science.

SQL Mastery.

Experience in Working with Marketing Tools.

Information on Programming Languages.

Information on Data Engineering Tools.

Relational abilities.

Statistical analysis 

Software such as R, SAS, SPSS, or STATA

SQL databases 

Programming skills

Information on Survey/inquiry software.

Tableau

Data mining.


Career Opportunity

Marketing analysts

Marketing Data Analyst

marketing data scientist

Data Scientist

Data Science Analyst

Marketing Effectiveness Manager

Consumer Insights and Data Science Manager

Marketing Analytics Manager

Survey Analytics Analyst

Data Science Team Manager

Progressed Analytics Manager


Table of Content

Data Preparation and Cleaning

Course Overview

Lesson Overview

Data Models and Structured Data

Pandas

Data Manipulation

Summary

Data Exploration and Visualization

Lesson Overview

Identifying the Right Attributes

Generating Targeted Insights

Visualizing Data

Summary

Unsupervised Learning: Customer Segmentation

Lesson Overview

Customer Segmentation Methods

Similarity and Data Standardization

k-means Clustering

Summary

Choosing the Best Segmentation Approach

Lesson Overview

Choosing the Number of Clusters

Different Methods of Clustering

Evaluation Clustering

Summary

Predicting Customer Revenue Using Linear Regression

Lesson Overview

Feature Engineering for Regression

Performing and Interpreting Linear Regression

Summary

Other Regression Techniques and Tools for Evaluation

Lesson Overview

Evaluating the Accuracy of a Regression Model

Using Regularization for Feature Selection

Tree Based Regression Models

Summary

Supervised Learning - Predicting Customer Churn

Lesson Overview

Understanding Logistic Regression

Creating a Data Science Pipeline

Modelling the Data

Summary

Fine-Tuning Classification Algorithms

Lesson Overview

Support Vector Machines

Decision Trees and Random Forests

Pre-processing Data and Model Evaluation

Performance Metrics

Summary

Modelling Customer Choice

Lesson Overview

Understanding Multi-class Classification

Class Imbalanced Data

Summary


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