{"action":"closed","issue":{"url":"https://api.github.com/repos/dnaranjo89/test_script/issues/294","repository_url":"https://api.github.com/repos/dnaranjo89/test_script","labels_url":"https://api.github.com/repos/dnaranjo89/test_script/issues/294/labels{/name}","comments_url":"https://api.github.com/repos/dnaranjo89/test_script/issues/294/comments","events_url":"https://api.github.com/repos/dnaranjo89/test_script/issues/294/events","html_url":"https://github.com/dnaranjo89/test_script/issues/294","id":314754490,"number":294,"title":"From SQL to Python: A Beginner's Guide to Making the Switch","user":{"login":"dnaranjo89","id":9324508,"avatar_url":"https://avatars3.githubusercontent.com/u/9324508?v=4","gravatar_id":"","url":"https://api.github.com/users/dnaranjo89","html_url":"https://github.com/dnaranjo89","followers_url":"https://api.github.com/users/dnaranjo89/followers","following_url":"https://api.github.com/users/dnaranjo89/following{/other_user}","gists_url":"https://api.github.com/users/dnaranjo89/gists{/gist_id}","starred_url":"https://api.github.com/users/dnaranjo89/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/dnaranjo89/subscriptions","organizations_url":"https://api.github.com/users/dnaranjo89/orgs","repos_url":"https://api.github.com/users/dnaranjo89/repos","events_url":"https://api.github.com/users/dnaranjo89/events{/privacy}","received_events_url":"https://api.github.com/users/dnaranjo89/received_events","type":"User","site_admin":false},"labels":[{"id":901520894,"url":"https://api.github.com/repos/dnaranjo89/test_script/labels/Group%204","name":"Group 4","color":"1116f4","default":false}],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-04-16T17:46:15Z","updated_at":"2018-04-16T17:56:46Z","closed_at":"2018-04-16T17:56:46Z","author_association":"OWNER","body":"# Title\nFrom SQL to Python: A Beginner's Guide to Making the Switch\n---\n# Metadata\nTags: #OOP, #SQL, #DS, #ML, #PyLadies, #Upskilling, #DataScience\nAudience Level: Beginner\n---\n# Abstract\nMany analysts want to up-skill to Python, but don't know where to start. Learn how to leverage your SQL knowledge, and how to fill common knowledge gaps that arise when moving to a \"true\" programming language.\n---\n# Description\nWe've all been there... your day job mostly involves crunching data into a SQL database, and then pulling it back out again. It's monotonous and often cumbersome- you know there's got to be a better way. Not only do you know that Python is a powerful tool for data manipulation, visualisation and analysis, you also know that it can open the door to the world of Data Science and Machine Learning. There's just one problem.. you have no idea where to start. This talk will help you map the similarities and differences between SQL and Python, allowing you to pinpoint what you don't know and giving you a base from which to start learning. You'll leave with a solid understanding of Pandas and other \"SQL-like\" Python libraries, as well as a respect for the vast Python universe that exists beyond them. \n---\n# Notes\nWhen I first started learning Python, I was confident I could master it quickly given my background in SQL. \"How much different can it be?\" I thought. I was quickly and thoroughly humbled. As someone who didn't have a computer science background, I struggled at first to wrap my head around many of the core Python concepts, and simultaneously wanted to be able to quickly learn to do everything I could already do in SQL. My frustration in making the switch, and exasperation with the lack of clear tutorials that made a swift connection to Python's usability for data analysis, became my motivation for giving this talk. As I am still fairly new to Python, I definitely don't yet have the \"curse of knowledge\" that plagues some more experienced teachers, and still have close memories of the initial knowledge gaps I overcame in the beginning of my transition to Python.\n---\n# Speaker Bio\nI'm an Analyst turned Data Scientist eager to help others make the transition and learn from my mistakes. In 2017 I quit my Senior Energy Analyst job to pursue a 3-month Bootcamp at Data Science Retreat in Berlin, Germany. Combining my Data Science skills with a background in quantitative economics and sustainable development, I now work for a green tech start up called Tempus Energy. We at Tempus use machine learning to drive demand flexibility, leading to deep decarbonisation and cost savings.\n"}}
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