Business Statistics Project Rubric.

Description

Background:

An online news portal aims to expand its business by acquiring new subscribers. Every visitor to the website takes certain actions based on their interest. The company plans to analyze these interests and wants to determine whether a new feature will be effective or not. Companies often analyze users' responses to two variants of a product to decide which of the two variants is more effective. This experimental technique is known as a/b testing that is used to determine whether a new feature attracts users based on a chosen metric.

Suppose you are hired as a Data Scientist in E-news Express. The design team of the company has created a new landing page. You have been assigned the task to decide whether the new landing page is more effective to gather new subscribers. Suppose you randomly selected 100 users and divided them equally into two groups. The old landing page is served to the first group (control group) and the new landing page is served to the second group (treatment group). Various data about the customers in both groups are collected in 'abtest.csv'. Perform the statistical analysis to answer the following questions using the collected data.

Objective:

Statistical analysis of business data. Explore the dataset and extract insights from the data. The idea is for you to get comfortable with doing statistical analysis in Python.

You are expected to perform the statistical analysis to answer the following questions:

1. Explore the dataset and extract insights using Exploratory Data Analysis.
2. Do the users spend more time on the new landing page than the old landing page?
3. Is the conversion rate (the proportion of users who visit the landing page and get converted) for the new page greater than the conversion rate for the old page?
4. Does the converted status depend on the preferred language? [Hint: Create a contingency table using the pandas.crosstab() function]
5. Is the mean time spent on the new page same for the different language users?

*Consider a significance level of 0.05 for all tests.

Data Dictionary:

1. user_id - This represents the user ID of the person visiting the website.
2. group - This represents whether the user belongs to the first group (control) or the second group (treatment).
3. landing_page - This represents whether the landing page is new or old.
4. time_spent_on_the_page - This represents the time (in minutes) spent by the user on the landing page.
5. converted - This represents whether the user gets converted to a subscriber of the news portal or not.
6. language_preferred - This represents the language chosen by the user to view the landing page.

Best Practices for Notebook :

• The notebook should be well-documented, with inline comments explaining the functionality of code and markdown cells containing comments on the observations and insights.
• The notebook should be run from start to finish in a sequential manner before submission.
• It is preferable to remove all warnings and errors before submission.
• The notebook should be submitted as an HTML file (.html) and NOT as a notebook file (.ipynb)

Scoring guide (Rubric) - E-news Express Project

Criteria	Points
Define the problem and perform an Exploratory Data Analysis - Problem definition, questions to be answered - Data background and contents - Univariate analysis - Bivariate analysis	3
Illustrate the insights based on EDA Key meaningful observations on individual variables and the relationship between variables	3
Do the users spend more time on the new landing page than the old landing page? Perform the statistical analysis and visual analysis	7
Is the conversion rate (the proportion of users who visit the landing page and get converted) for the new page greater than the conversion rate for the old page? Perform the statistical analysis and visual analysis	7
Does the converted status depend on the preferred language? Perform the statistical analysis and visual analysis	7
Is the mean time spent on the new page same for the different language users? Perform the statistical analysis and visual analysis	7
Presentation - Overall quality - Structure and flow - Crispness - Visual appeal - Key insight and recommendation based on EDA - Formulated hypothesis and inference based on the p-value	3
Notebook - Overall - Structure and flow - Well commented code	3