Learn how to use Python to analyze customer churn and build a model to predict it.
Learn how to ensure clean data entry and build dynamic dashboards to display your marketing data.
Build up your pandas skills and answer marketing questions by merging, slicing, visualizing, and more!
From customer lifetime value, predicting churn to segmentation - learn and implement Machine Learning use cases for Marketing in Python.
In this course you'll learn how to use data science for several common marketing tasks.
Learn to analyze and model customer choice data in R.
Learn to build simple models of market response to increase the effectiveness of your marketing plans.
Learn to use the Census API to work with demographic and socioeconomic data.
Learn to write SQL queries to calculate key metrics that businesses use to measure performance.
Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelling visualizations.
Analyze text data in R using the tidy framework.
Extract and visualize Twitter data, perform sentiment and network analysis, and map the geolocation of your tweets.
Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data.
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
Design surveys to get actionable insights via reviewing of survey design structures and visualizing and analyzing survey results.
Learn mixture models: a convenient and formal statistical framework for probabilistic clustering and classification.
Learn efficient techniques in pandas to optimize your Python code.
Automatically generate keywords for a search engine marketing campaign using Python.
Plot Google Trends data to find the most famous Kardashian/Jenner sister. Is it Kim? Kendall? Kylie?
