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DATA SCIENCE BOOT CAMP - DAY 1/25

This being the first day, we will begin with a smooth introduction. You are supposed to do research on your own to ensure you can answer the following questions even in your sleep:

  1. Can you clarify the difference between data science, data analysis, data engineering, and analytical engineering?

  2. Can you clearly identify the different roles of a data analyst, data scientist, data engineer, and analytical engineer?

  3. Use the resources below to understand Python and SQL Basics for Data Science.

In Python Make sure you understand:

(i). Python's syntax, including indentation, variables, and data types (integers, floats, strings, lists, tuples, dictionaries). Learn how to declare and manipulate variables, perform arithmetic operations, and work with strings.

(ii. Control flow structures like if statements, loops (for and while), and conditional expressions (ternary operators). Practice using these structures to control program execution and make decisions.

Read: Python 101! Introduction to Python: https://dev.to/grayhat/python-101-introduction-to-python-3kg5

Reference: https://www.w3schools.com/python/

In SQL Make sure you understand:

With SELECT, you can retrieve the data you need, with INSERT, you can add new information, and with UPDATE and DELETE, you can control and modify your data. Creating new tables with CREATE TABLE, altering them with ALTER TABLE, or even wiping them out with DROP TABLE gives you the flexibility to structure and restructure your data storage.

The WHERE clause allows you to filter precisely the rows you want, and ORDER BY lets you sort your results for easy analysis. Lastly, GROUP BY empowers you to group and summarize data, a crucial skill for advanced data manipulation.

Read : 10 Fundamental SQL Commands for Beginners: https://dev.to/grayhat/mastering-the-essentials-10-fundamental-sql-commands-for-beginners-1ig4

Refrence: https://www.w3schools.com/sql/

You can start writing the week 1 article and working on the project.

  • Article: Data Science for Beginners: 2023 - 2024 Complete Roadmap

  • Project - Option 1: Let’s say you’re a Product Data Scientist at Instagram. How would you measure the success of the Instagram TV product?

  • Project - Option 2: 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? What steps would you take to create a machine learning model that can predict if someone's going to leave or not?