Data analytics for marketing a practical guide to analyzing marketing data using Python
Most marketing professionals are familiar with various sources of customer data that promise insights for success. There are extensive sources of data, from customer surveys to digital marketing data. Moreover, there is an increasing variety of tools and techniques to shape data, from small to big d...
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| Format: | eBook |
| Language: | English |
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Birmingham, UK
Packt Publishing Ltd.
2024
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| Edition: | 1st edition |
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| Online Access: | |
| Collection: | O'Reilly - Collection details see MPG.ReNa |
Table of Contents:
- Getting your data into Streamlit and generating a basic dashboard
- Starting out with Streamlit
- Creating a marketing data dashboard with Streamlit
- Summary
- Further reading
- Chapter 4: Econometrics and Causal Inference with Statsmodels and PyMC
- Technical requirements
- What is a linear regression?
- What is a model?
- What are the assumptions of a linear regression?
- Exploring different types of regression models
- What we can do when the assumptions break down
- How to do a linear regression
- What is logistic regression?
- Objectives of logistic regression models
- Includes bibliographical references and index
- The importance of data engineering and tracking
- Don't moonlight as a data engineer
- Web tracking is hard, and it is becoming harder
- Summary
- References
- Chapter 2: Extracting and Exploring Data with Singer and pandas
- Technical requirements
- What is ETL, and why should you care?
- Data pipelines
- What is Singer?
- Summarizing data and EDA
- Primer on descriptive statistics
- Percentiles, quantiles, and distributions
- Measures of central tendency
- Measures of variability
- Dealing with common data issues
- Bill Gates walks into a bar
- Odds of an event
- What is causal inference?
- Correlation, causation, and key drivers
- A more practical application
- A small detour through the backdoor
- Watch out for colliders
- Summary
- Further reading
- Part 2: Planning Ahead
- Chapter 5: Forecasting with Prophet, ARIMA, and Other Models Using StatsForecast
- Technical requirements
- What is forecasting?
- Why forecasting is important
- Types of times series data
- Exploratory data analysis
- What to forecast
- Weekly, daily, and sub-daily data
- Time series of counts
- Prediction intervals for aggregates
- Missing values and data imputation
- Digging deeper into variable transformations
- Data standardization or scaling
- Power transformations
- Summary
- Further reading
- Chapter 3: Design Principles and Presenting Results with Streamlit
- Technical requirements
- Types of dashboards and their design
- Understanding the design concepts of a dashboard
- Thinking about how to best present data
- Thinking a bit about processing information
- Generating effective filters, dimensions, and metrics
- Filters
- Dimensions
- Metrics
- Cover
- Title Page
- Copyright
- Dedication
- Contributors
- Table of Contents
- Preface
- Part 1: Fundamentals of Analytics
- Chapter 1: What is Marketing Analytics?
- What is analytics?
- An overview of marketing analytics
- Why should we bother with marketing analytics?
- Exploring different types of analytics
- Descriptive analytics
- Diagnostic analytics
- Predictive analytics
- Prescriptive analytics
- Walking through the maze of tools and techniques
- Beyond simple pivot tables
- Why Python?
- Modern challenges in the world of privacy-centric marketing