AI Academy

Marketing Analytics Foundation

Instructor: Anke Audenaert Duration: Approx. 11 hours
Objective 1 How data and its measurement inform a marketing action
Objective 2 Basic principles of marketing
Objective 3 How data is collected and the regulations around its collection
Application Programming Interface (API)
Data Collection
Web Analytics
Digital Marketing
Marketing Analytics
Data-Driven Decision-Making
Personally Identifiable Information
Facebook
Google Analytics
Marketing
Data Integration
Information Privacy
Analytics
Advertising

Introduction to Data Analytics

Instructor: Anke Audenaert Duration: 2 weeks at 10 hours a week
Objective 1 Apply the data analysis process OSEMN to marketing data
Objective 2 Compare and contrast various data formats and their applications across different scenarios
Objective 3 Identify data gaps and articulate the strengths and weaknesses of collected data
Data Collection
Data Quality
Data Validation
Analytics
Data analysis
Data Visualization
Key Performance Indicators (KPIs)
Business Metrics
Generative AI
Data Manipulation
Data Modeling
Data Storytelling
Data Cleansing
Exploratory Data Analysis

Data Analysis with Spreadsheets and SQL

Instructor: Brandon Larkin Duration: 3 weeks at 10 hours a week
Objective 1 Clean data with spreadsheets and use common spreadsheet formulas to calculate summary statistics
Objective 2 Write foundational SQL statements and queries to extract data in spreadsheets
Objective 3 Create charts in Google Sheets and use Tableau to visualize data and use dashboards to create data visualizations
Data Visualization Software
Tableau Software
Statistical Analysis
Correlation Analysis
Data Manipulation
Data-Driven Decision-Making
Data analysis
Descriptive Statistics
Data Storytelling
Pivot Tables And Charts
SQL
Google Sheets
Dashboard
Data Cleansing
Spreadsheet Software
Exploratory Data Analysis

Python Data Analytics

Instructor: Victor Geislinger Duration: 2 weeks at 10 hours a week
Objective 1 Sort, query, and structure data in Pandas, the Python library
Objective 2 Describe how to model and interpret data using Python
Objective 3 Create basic data visualizations with Python libraries
Exploratory Data Analysis
Matplotlib
Pandas (Python Package)
Data Manipulation
Data Visualization Software
Data Cleansing
Data analysis
Python Programming
Data Processing
Programming Principles
Jupyter
Data Modeling
Scripting

Statistics Foundations

Instructor: Brandi Robinson Duration: 2 weeks at 10 hours a week
Objective 1 The basic principles of descriptive and inferential statistics
Objective 2 Use statistical analyses to make data-driven decisions
Objective 3 How to formulate and test hypotheses and take action based on the outcome
Spreadsheet Software
Statistics
Sampling (Statistics)
Analytics
Tableau Software
Marketing Analytics
Quantitative Research
Descriptive Statistics
Descriptive Analytics
Data Analysis Software
Time Series Analysis and Forecasting
Data analysis
Probability & Statistics
Bayesian Statistics
Statistical Modeling
Statistical Methods
Statistical Hypothesis Testing
Statistical Analysis
Data Modeling
Statistical Inference

Data Analytics Methods for Marketing

Instructor: Anke Audenaert Duration: Approx. 12 hours
Objective 1 How to plan and forecast your marketing efforts across different channels
Objective 2 How to use marketing mix modeling and attribution to optimize your efforts
Objective 3 How to evaluate and optimize your sales funnel
Sales Pipelines
Marketing Strategies
Regression Analysis
Unsupervised Learning
Target Audience
Customer Analysis
Advertising Campaigns
Marketing Planning
Return On Investment
Marketing Analytics
Forecasting
Marketing Effectiveness
Marketing
Marketing Channel
A/B Testing
Key Performance Indicators (KPIs)

Marketing Analytics with Meta

Instructor: Anke Audenaert Duration: 1 week at 10 hours a week
Objective 1 How to optimize your marketing campaign by evaluating campaign results
Objective 2 How to measure advertising effectiveness with Meta’s testing tools
Objective 3 How to implement a full advertising analysis from the hypothesis formulation to presenting results and recommendations
Marketing Strategies
A/B Testing
Marketing Effectiveness
Target Audience
Advertising Campaigns
Digital Advertising
Facebook
Paid media
Social Media Campaigns
Key Performance Indicators (KPIs)
Data analysis
Marketing Analytics
Presentations
Campaign Management
Advertising
Data-Driven Decision-Making
Brand Awareness
Marketing

Meta Marketing Science Certification Exam

Instructor: Anke Audenaert Duration: 1 hour to complete Recommended experience
Objective 1 Complete the Meta Marketing Science Certification Exam
Marketing Analytics
Professional Networking
Interviewing Skills
Digital Marketing
Registration
Statistical Analysis
Data analysis