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Motivating Hypothetical: DataSciencester Explained in Fewer than 140 Characters

Motivating Hypothetical: DataSciencester Explained in Fewer than 140 Characters

meenati biswal 2000 27-Sep-2019

You’ve just been hired to lead the data science efforts at DataScien‐ center, the social network for data scientists. Despite being for data scientists, DataSciencester has never actually invested in building its data science course practice. 

In fairness, DataSciencester has never really invested in building its product either.

That will be your job! Throughout the book, we’ll be learning about data science concepts by solving problems that you encounter at work. Sometimes we’ll look at data explicitly supplied by users, sometimes we’ll look at data generated through their interactions with the site, and sometimes we’ll even look at data from experiments that we’ll design. 

And because DataSciencester has a strong “not-invented-here” mentality, we’ll be building our tools from scratch. In the end, you’ll have a pretty solid understanding of the fundamentals of data science. And you’ll be ready to apply your skills at a company with a less shaky premise, or to any other problems that happen to interest you. Welcome aboard, and good luck! (You’re allowed to wear jeans on Fridays, and the bathroom is down the hall on the right.) 

Finding Key Connectors It’s your first day on the job at DataSciencester, and the VP of Networking is full of questions about your users. Until now he’s had no one to ask, so he’s very excited to have you aboard. Learn more Data Science Certification

In particular, he wants you to identify who the “key connectors” are among data scientists. To this end, he gives you a dump of the entire DataSciencester network. (In real life, people don’t typically hand you the data you need. 

What does this data dump look like? It consists of a list of users, each represented by a dict that contains for each user his or her id (which is a number) and name (which, in one of the great cosmic coincidences, rhymes with the user’s id): 

 users = [ 

{ "id": 0, "name": "Hero" },

 { "id": 1, "name": "Dunn" }, 

{ "id": 2, "name": "Sue" },

 { "id": 3, "name": "Chi" },

 { "id": 4, "name": "Thor" }, 

{ "id": 5, "name": "Clive" }, 

{ "id": 6, "name": "Hicks" }, 

{ "id": 7, "name": "Devin" },

 { "id": 8, "name": "Kate" },

 { "id": 9, "name": "Klein" }


He also gives you the “friendship” data, represented as a list of pairs of IDs: 

friendships = [(0, 1), (0, 2), (1, 2), (1, 3), (2, 3), (3, 4),

 (4, 5), (5, 6), (5, 7), (6, 8), (7, 8), (8, 9)] 

For example, the tuple (0, 1) indicates that the data scientist with id 0 (Hero) and the data scientist with id 1 (Dunn) are friends. 

Updated 27-Sep-2019
Meenati biswal loves pursuing excellence through writing and has a passion for technology. She currently working form is a global training company that provides e-learning and professional certification training.

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