This course focuses on building practical marketing analytics skills using Python and statistical methods that are essential in today’s data-driven business environment. It emphasizes turning complex datasets into meaningful insights that support strategic marketing decisions.
Data Analytics for Marketing
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Understand the core statistical models used in marketing analytics
Apply the right tools and models to specific analytical questions
Conduct causal inference and statistical modeling using Python
Skills you'll gain
- Anomaly Detection
- Data Presentation
- Market Research
- Statistical Methods
- Regression Analysis
- Customer Insights
- Marketing Effectiveness
- Data Transformation
- Time Series Analysis and Forecasting
- Marketing Strategies
- Forecasting
- Statistical Analysis
- Analytics
- Digital Advertising
- Marketing Analytics
- Customer Analysis
- A/B Testing
- Predictive Analytics
- Exploratory Data Analysis
Tools you'll learn
Details to know
Shareable certificate
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Assessments
13 assignments
Taught in English
Recently updated!
March 2026
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There are 13 modules in this course
In this section, we cover marketing analytics fundamentals, including descriptive and diagnostic analytics, and their role in decision-making.
What's included
2 videos6 readings1 assignment
In this section, we explore ETL processes using Singer and pandas for data extraction and exploratory data analysis. Key concepts include descriptive statistics, data issues, and practical data cleaning techniques.
What's included
1 video6 readings1 assignment
In this section, we explore Streamlit dashboard design, focusing on effective metrics, dimensions, and layout principles for clear data presentation and user-centered visualization.
What's included
1 video6 readings1 assignment
In this section, we explore linear and logistic regression models to analyze causal relationships and interpret coefficients for data-driven decision-making in marketing analytics.
What's included
1 video6 readings1 assignment
In this section, we explore forecasting techniques like Prophet and ARIMA for marketing KPIs, focusing on model selection, performance evaluation, and practical applications in time series analysis.
What's included
1 video10 readings1 assignment
In this section, we explore anomaly detection using STL decomposition, S-H-ESD, and PyMC for Bayesian change point detection, emphasizing practical applications and technical accuracy.
What's included
1 video4 readings1 assignment
In this section, we explore customer segmentation and RFM analysis to identify high-value customers and optimize marketing strategies using Python for data-driven decision-making.
What's included
1 video9 readings1 assignment
In this section, we explore CLV fundamentals, challenges in its formula, and implement the BTYD model with PyMC Marketing to predict customer value and purchase frequency accurately.
What's included
1 video5 readings1 assignment
In this section, we explore customer survey design, reliability, validity, sampling methods, and NPS limitations to improve data accuracy and customer insights.
What's included
1 video7 readings1 assignment
In this section, we explain conjoint analysis and how to use it to understand customer preferences and decision-making.
What's included
1 video4 readings1 assignment
In this section, we explore heuristic and algorithmic attribution models to evaluate marketing touchpoints and optimize spend. Key concepts include Shapley values, marginal contributions, and Python implementation for conversion path analysis.
What's included
1 video5 readings1 assignment
In this section, we explore media mix modeling (MMM) to assess marketing effectiveness using Python. Key concepts include data collection, adstock effects, and synthetic data applications for limited data scenarios.
What's included
1 video8 readings1 assignment
In this section, we explore designing and evaluating experiments using A/A testing, p-values, and statistical power to ensure reliable results in marketing and data analysis.
What's included
1 video10 readings1 assignment
Instructor
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