Adman Analytics
Thoughts on marketing analytics
recent posts
- A Structured SQL Framework for Marketing Analytics: Building Scalable Query Architecture
- Did Google Just Take the Lead in AI? Yup.
- Practical Data Visualization with Seaborn: Analyzing Superstore Sales Data
- Introduction to Seaborn: Beautiful Statistical Visualizations in Python
- Google Search “Preferred Sources”
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Category: Python
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In our previous post, we explored the fundamentals of seaborn. Now, let’s put those concepts into practice with real-world data. We’ll use the Tableau Superstore dataset—a rich sales dataset containing 9,994 transaction records with information about customers, products, sales, and profits across different regions of the United States. This dataset provides an excellent playground for…
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Data visualization is a cornerstone of data analysis, transforming raw numbers into insights that leap off the page. While matplotlib laid the foundation for plotting in Python, seaborn builds upon it to create a more intuitive, aesthetically pleasing approach to statistical graphics. If you’ve ever struggled with matplotlib’s verbose syntax or wished your plots looked…
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AI is good at pulling the Hard Skills from job postings, and comparing that to what’s in your resume. After a long job search, you may wonder what Hard Skills you need to focus on. Here’s a python script that helped me out. I started with a CSV that was copy and pasted from a…
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SQL (Structured Query Language) is the universal language of data, and a non-negotiable skill for any data professional.
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Building a Marketing Mix Model: A Data-Driven Approach to Attribution
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If you’ve ever needed to extract data from a website, you’ve probably encountered Beautiful Soup. This Python library has become the go-to tool for parsing HTML and XML documents, making web scraping accessible even to those just starting their coding journey. What Makes Beautiful Soup Special? Beautiful Soup transforms messy, real-world HTML into a navigable…
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In natural language processing, consistency is crucial for accurate analysis. Fix words like “can’t,” “won’t,” “it’s,” and “we’re” (they can wreak havoc on machine learning models) with the contractions library. Contractions provides an elegant solution to this problem, automatically expanding contracted forms into their full equivalents for cleaner, more consistent text analysis.
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Raw text from the internet, social media, customer reviews, or scraped content often comes loaded with noise – HTML tags, emojis, URLs, special characters, and inconsistent formatting that can derail your analysis before it even begins. Enter clean-text: a specialized Python library designed to tackle these messy text challenges with surgical precision.
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If you’re new to Natural Language Processing (NLP) or need a quick, reliable tool for text analysis, TextBlob should be at the top of your list.
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Tutorial to use Python for time series analysis