Problem description:
You are an analyst at Megaline, a federal mobile operator. Clients are offered two tariff plans: "Smart" and "Ultra". To adjust the advertising budget, the commercial department wants to understand which tariff brings in more money. You have to make a preliminary analysis of tariffs on a small sample of customers. At your disposal are the data of 500 Megaline users: who they are, where they are from, what tariff they use, how many calls and messages each sent in 2018. It is necessary to analyze the behavior of customers and draw a conclusion - which tariff is better.
Used libraries:
pandas, pyplot, numpy, scipy
Problem description:
You work in the Streamchik online store, which sells computer games all over the world. Historical game sales data, user and expert ratings, genres and platforms (such as Xbox or PlayStation) are available from open sources. Here is the data up to 2016. Let's say it's December 2016 and you're planning a campaign for 2017.
The goal of the project: to identify patterns that determine the success of the game for planning advertising campaigns for the next year
Used libraries:
pandas, pyplot, numpy, scipy
Problem description:
You are an analyst for a large online store. Together with the marketing department, you have prepared a list of hypotheses for increasing revenue.
To do:
- prioritize hypotheses
- run an A/B test
- analyze the results.
Used libraries:
pandas, pyplot, numpy, scipy, seaborn