Data Science For EveryOne Mentorship and BootCamp Program

Week 1:

• Question 1). Imagine you're working with Sprint, one of the biggest telecom companies in the USA. They're really keen on figuring out how many customers might decide to leave them in the coming months. Luckily, they've got a bunch of past data about when customers have left before, as well as info about who these customers are, what they've bought, and other things like that.

So, if you were in charge of predicting customer churn how would you go about using machine learning to make a good guess about which customers might leave?

• Article: Data Science Roadmap

Assignment Submission:

• Dataset
• Notebook - Github
• Notebook - Google Colab
• Article

Week 2:

• Question 1). Read through this case study and solve it

• Article: exploratory-data-analysis-using-data-visualization-techniques

Assignment Submission:

• Article
• Notebook - Github
• Notebook - Google Colab
• Dataset

Week 4:

• Question 1). Using the Craiglist Vehicles Dataset, create a Time-Series model using the approach outlined below:

  • Start by addressing missing values in the dataset. You can handle this by filling in missing values with the median for numerical columns and the mode for categorical columns.
  • Ensure that the data types of the columns are appropriate. Specifically, make sure to convert the 'posting_date' column to a datetime data type. Utilize the 'posting_date' column to create a datetime index for the dataset. This will facilitate the analysis of temporal patterns.
  • With clean data, explore it using various visualizations and statistical analysis techniques. This step is crucial for understanding temporal patterns, identifying seasonal trends, and analyzing demand-supply dynamics by region and vehicle type.
  • Build the time-series chart.

• Article: The Complete Guide to Time Series Models

Assignment Submission:

• Article
• Notebook - Github
• Notebook - Google Colab
• Dataset

Week 5:

• Question 2). Project 5.

In week 4, we performed time series modelling on the Craigslist vehicles dataset, which is available on Kaggle at https://www.kaggle.com/datasets/mbaabuharun/craigslist-vehicles. This project builds on that work. You will need to download the dataset, copy the data using SQL to a local PostgreSQL database, move the data from your local database to Snowflake, perform data transformation with DBT (data build tool), and use your preferred data visualization tool to create a report and dashboard.

• Article: Data Engineering for Beginners a Step by Step Guide

Assignment Submission:

• Article
• Repo - Github
• Dataset