I created a short hands-on course for getting started with data science

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Hi, I am a former software engineer at Amazon Alexa and I have created a short down-to-earth hands-on course to introduce you to the tools needed to do data science. You can watch the first 2 videos on Youtube (https://youtu.be/ehPkrAKVLII) or you can go directly to the course (https://skl.sh/2ZlDxnu) and access SkillShare premium for free for 2 months so no need to pay anything. Below is the description of the course for your convenience. I really hope you find it useful and would love to know your feedback --- Course Description The goal of this class is to provide you with a step by step guide on how to start doing data science and data analysis of real-life raw .csv files. Prerequisites: basic programming skills preferably in Python Concretely, you will learn: * best practices to properly setup Python for your project using Conda environments * how to install packages (Jupyter notebook & Pandas) * how to analyze a .csv file using Pandas DataFrames to extract useful insights for your business --- Course Project In this class we will use a real-life Android Play Store data (.csv) for a given search query to extract useful insights and answer 3 questions: 1. How many apps are paid? 2. How much money are they making? 3. When were these apps released?

Visit product View on Hacker NewsMay 21, 2020

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, started · Missing: supports, reddit linkedin, podcasting

88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: apps, video, month · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: apps, using · Missing: mac, agents, macos

39%39% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid, introduce · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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