Marketing Analytics with Python

One of the most difficult aspects of studying data science technical skills is understanding how these skills and concepts are applied to real-world jobs. This course is ideal for those who want to learn Python and pandas or understand the work of data scientists in marketing agencies. Learn how to convert common business questions into quantifiable results. ", "Which channel has the highest number of subscribers?" ", "Why is one channel performing poorly?" A fake marketing database can be created using data obtained from an online subscription service. This course will teach Python and Pandas basics such as groupby() and merging/slicing, groupby() and groupby(). It also covers correcting various types of data and visualizing the results with matplotlib.

Twitter hosts hundreds of millions of messages every day. It is used by people all over the world to discuss politics, entertainment and business. You can quickly access thousands to thousands of messages from this stream in just minutes. This course will show you how to analyze Twitter tweets and networks. This course also shows you how to determine the origin of tweets. It will use datasets about tech companies, data science hashtags and the 2018 State of the Union speech. This will enable you to make informed political and business decisions. It will identify the importance of topics and the geographic reach and diversity of discussion networks.

What do Amazon's product recommendations and Netflix movie recommendations have in common? Both use Market Basket Analysis, which is a powerful tool that transforms large amounts customer transaction data and viewing data into simple rules for recommending products. This course will show you how to perform Market Basket Analysis using the Apriori algorithm, standard and custom metrics, association rules, and pruning. Interactive exercises will allow you to strengthen your skills and make recommendations for small grocery stores and libraries, ebook sellers, novelty gift shops, and movie streaming service providers. Hidden insights will help you make better recommendations to your customers.

The major advancement in machine learning is the rise of machine learning. This almost sounds like the "rise and rise of machines". Statistics have revolutionized marketing. Machine learning can optimize customer journeys to maximize customer satisfaction and lifetime value. This course will give you the foundation tools to improve your company's marketing strategy. You will learn how to predict customer lifetime value and predict customer turnover using various methods. You will use customer data from a telecom company to predict churn, construct a recency-frequency-monetary dataset from an online retailer for customer lifetime value prediction, and build customer segments from product purchase data from a grocery shop.

The most successful companies are those that can anticipate their customers' needs. Data analysts are crucial to unlocking deep insights and segmenting customers in order to better serve them. This course will show you how to segment customers and conduct behavioral analysis using anonymous customer transactions obtained from an online retailer. You will first do a cohort analysis to understand customer trends. Next, you'll learn how to create customer segments that are easy to understand. The segments you create for machine learning will be prepared. You can strengthen your segments with k-means clustering in just a few lines. This course will show you how to segment customers and use customer behavior analytics.

Churn is when customers end a business relationship or stop doing business with a company. This problem affects all industries, including telecommunications, cable TV and SaaS. Companies that can accurately predict churn are better equipped to keep customers happy and to stay ahead of their competition. This course will provide you with the guidance and tools to create customer churn models. You will learn how to analyze and visualize your data, create models, and communicate useful, actionable insights to stakeholders. This course will teach you how to use scikit-learn and pandas libraries for data analysis.

Companies that are able to anticipate and understand the needs of customers will be more successful. A/B testing and customer analytics are key components to using quantitative information to make business decisions that add value. This course will show you how to use Python for customer patterns and trends analysis. This course will show you how to use A/B Testing to make data-driven business decisions.