Data Analytics for Marketing
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Data Analytics for Marketing
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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
- Extract, Transform, Load
- Data-Driven Marketing
- Regression Analysis
- Time Series Analysis and Forecasting
- Customer Insights
- Marketing Analytics
- Anomaly Detection
- Statistical Modeling
- Dashboard Creation
- Forecasting
- Marketing Strategies
- Descriptive Analytics
- A/B Testing
- Predictive Modeling
- Marketing Effectiveness
- Data Presentation
- Statistical Analysis
- Customer Analysis
- Statistical Methods
Tools you'll learn
Details to know
March 2026
13 assignments
See how employees at top companies are mastering in-demand skills
There are 13 modules in this course
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.
Through hands-on examples, you’ll learn how to analyze marketing data, apply appropriate models, and interpret results to improve campaign performance and customer understanding. The course helps you move from raw data to actionable outcomes with confidence. What sets this course apart is its balance between theory and real-world application. It combines statistical reasoning with practical Python workflows to solve common marketing analytics problems. This course is ideal for marketing-focused data analysts and data scientists with prior experience in Python, basic statistics, and data analysis who want to deepen their analytical impact.
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
2 videos•Total 2 minutes
- Course Overview•1 minute
- What Is Marketing Analytics? - Overview Video•1 minute
6 readings•Total 60 minutes
- Introduction•10 minutes
- Exploring Different Types of Analytics•10 minutes
- Diagnostic Analytics•10 minutes
- Walking Through the Maze of Tools and Techniques•10 minutes
- Why Python?•10 minutes
- The Importance of Data Engineering and Tracking•10 minutes
1 assignment•Total 10 minutes
- Foundations of Marketing Analytics•10 minutes
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
1 video•Total 1 minute
- Extracting and Exploring Data with Singer and pandas - Overview Video•1 minute
6 readings•Total 70 minutes
- Introduction•5 minutes
- What is Singer?•20 minutes
- Summarizing Data and EDA•20 minutes
- Measures of Central Tendency•10 minutes
- Measures of Variability•5 minutes
- Dealing with Common Data Issues•10 minutes
1 assignment•Total 10 minutes
- Data Analysis and Preparation Fundamentals•10 minutes
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
1 video•Total 1 minute
- Design Principles and Presenting Results with Streamlit - Overview Video•1 minute
6 readings•Total 80 minutes
- Introduction•20 minutes
- Thinking About How to Best Present Data•10 minutes
- Thinking a bit about Processing Information•10 minutes
- Generating Effective Filters, Dimensions, and Metrics•10 minutes
- Getting Your Data Into Streamlit and Generating a Basic Dashboard•10 minutes
- Loading the Data and Creating Metrics•20 minutes
1 assignment•Total 10 minutes
- Effective Dashboard Development and Data Presentation•10 minutes
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
1 video•Total 1 minute
- Econometrics and Causal Inference with Statsmodels and PyMC - Overview Video•1 minute
6 readings•Total 105 minutes
- Introduction•10 minutes
- Exploring Different Types of Regression Models•5 minutes
- How to Do a Linear Regression•30 minutes
- What Is Logistic Regression•20 minutes
- What Is Causal Inference•10 minutes
- A More Practical Application•30 minutes
1 assignment•Total 10 minutes
- Causal Inference and Econometric Analysis•10 minutes
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
1 video•Total 1 minute
- Forecasting with Prophet, ARIMA, and Other Models Using StatsForecast - Overview Video•1 minute
10 readings•Total 110 minutes
- Introduction•10 minutes
- What to Forecast•10 minutes
- What Types of Patterns Are Present•10 minutes
- STL Decomposition•10 minutes
- STL Features•20 minutes
- Basics of Time Series Forecasting•10 minutes
- Adjusting for Bias•10 minutes
- Information Criteria Metrics•10 minutes
- ETS Models in Python•10 minutes
- The Prophet Model•10 minutes
1 assignment•Total 10 minutes
- Time Series Forecasting Fundamentals•10 minutes
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
1 video•Total 1 minute
- Anomaly Detection with StatsForecast and PyMC - Overview Video•1 minute
4 readings•Total 50 minutes
- Introduction•20 minutes
- Advantages and Limitations•10 minutes
- Forecasting as an Anomaly Detection Tool•10 minutes
- Using Rates of Arrival to Identify Change Points•10 minutes
1 assignment•Total 10 minutes
- Anomaly Detection in Time Series Analysis•10 minutes
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
1 video•Total 1 minute
- Customer Insights Segmentation and RFM - Overview Video•1 minute
9 readings•Total 105 minutes
- Introduction•10 minutes
- Delving Deeper Into What Segmentation Is•10 minutes
- K-Means Clustering•20 minutes
- Targeting the Right Segment with the Right Marketing Efforts•10 minutes
- But What Exactly Is a Decision Tree and How Does It Fit Into a Forest?•5 minutes
- Linear versus Quadratic Discriminant Analysis•10 minutes
- Exploring RFM•10 minutes
- The Data•20 minutes
- Profitability Evaluation•10 minutes
1 assignment•Total 10 minutes
- Customer Insights and RFM Analysis•10 minutes
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
1 video•Total 1 minute
- Customer Lifetime Value with PyMC Marketing - Overview Video•1 minute
5 readings•Total 85 minutes
- Introduction•10 minutes
- What's Wrong with the CLV Formula•5 minutes
- Beyond the CLV Formula•10 minutes
- Implementing the BTYD Model Using PyMC Marketing•30 minutes
- Predicting the Expected Number of Purchases for a New Customer•30 minutes
1 assignment•Total 10 minutes
- Customer Lifetime Value and Statistical Modeling•10 minutes
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
1 video•Total 1 minute
- Customer Survey Analysis - Overview Video•1 minute
7 readings•Total 70 minutes
- Introduction•10 minutes
- Asking Questions•10 minutes
- Reliability and Validity•10 minutes
- Standard Error of Measurement•10 minutes
- How to Do Sampling•10 minutes
- Response Rate•10 minutes
- Customer Loyalty and NPS Methodology•10 minutes
1 assignment•Total 10 minutes
- Customer Survey Analysis Fundamentals•10 minutes
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
1 video•Total 1 minute
- Conjoint Analysis with pandas and Statsmodels - Overview Video•1 minute
4 readings•Total 50 minutes
- Introduction•10 minutes
- Conducting Conjoint Analysis in Python•10 minutes
- Fitting a Choice Model•10 minutes
- Marginal Utility and Willingness to Pay•20 minutes
1 assignment•Total 10 minutes
- Conjoint Analysis and Regression Modeling•10 minutes
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
1 video•Total 1 minute
- Multi-Touch Digital Attribution - Overview Video•1 minute
5 readings•Total 80 minutes
- Introduction•10 minutes
- Linear Attribution•10 minutes
- Algorithmic Attribution Models•10 minutes
- Implementing a Shapley Value Example•30 minutes
- Fractribution•20 minutes
1 assignment•Total 10 minutes
- Exploring Attribution Models and Their Mechanics•10 minutes
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
1 video•Total 1 minute
- Media Mix Modeling with PyMC Marketing - Overview Video•1 minute
8 readings•Total 115 minutes
- Introduction•10 minutes
- Steps Toward Implementing MMM•5 minutes
- Data Granularity•10 minutes
- How to Measure the Adstock Effect•10 minutes
- Saturation and Diminishing Returns•10 minutes
- Selecting a Model•30 minutes
- Modeling•10 minutes
- Model Results•30 minutes
1 assignment•Total 10 minutes
- Media Mix Modeling Fundamentals•10 minutes
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
1 video•Total 1 minute
- Running Experiments with PyMC - Overview Video•1 minute
10 readings•Total 100 minutes
- Introduction•10 minutes
- Looking at an Example•10 minutes
- False Positive Risk•10 minutes
- Surprising Results Require Strong Evidence Lower P-Values•10 minutes
- Delving Deeper into Some Pitfalls•10 minutes
- Statistical Power•10 minutes
- Uniform Prior•10 minutes
- Experimentation•10 minutes
- Observational Studies•10 minutes
- Quasi-experiments•10 minutes
1 assignment•Total 10 minutes
- Experimental Analysis and Statistical Techniques•10 minutes
Instructor
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University of Illinois Urbana-Champaign
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University of Colorado System
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