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
What do we offer?
- Full-Length Mock Test with unique questions in each test set
- Practice objective questions with section-wise scores
- In-depth and exhaustive explanation for every question
- Reliable exam reports to evaluate strengths and weaknesses
- Latest Questions with an updated version
- Tips & Tricks to crack the test
- Unlimited access
What are our Practice Exams?
- Practice exams have been designed by professionals and domain experts that simulate real time exam scenario.
- Practice exam questions have been created on the basis of content outlined in the official documentation.
- Each set in the practice exam contains unique questions built with the intent to provide real-time experience to the candidates as well as gain more confidence during exam preparation.
- Practice exams help to self-evaluate against the exam content and work towards building strength to clear the exam.
- You can also create your own practice exam based on your choice and preference
