5 Steps to Profit from Mistakes - DSBoost #43
Get Ready for the interview with these:
(Thread is here or click the image)
These posts from Nick are awesome.
You will find Python, Machine Learning, and general Data Science interview questions.
Here is another one:
The simple path to success in ML:
That’s indeed a really simple path. It involves only 7 ‘baby’ steps. If you walk through these you will have a great intro to the field.
But people usually misunderstand ‘simple’. It doesn’t mean easy.
Now we make it a little easier for you by providing some links to use.
Google collab intro
The Seaborn official documentation is awesome, check it out here.
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Some tips to handle mistakes
This Reddit post discusses concerns about making mistakes at work, specifically from the perspective of a Data Analyst/Scientist. The original poster (u/JLane1996) expresses worry about their lack of formal statistical or data scientist training, fearing flaws in coding or misinterpretation of results. Here we summarise the top responses from various users who offer advice and reassurance:
Emphasis on Learning from Mistakes: Users point out that all data scientists make mistakes, but good ones learn from them. They suggest staying organized with learning, perhaps by maintaining a document for notes and finding a mentor for guidance.
Importance of Process and Review: Several comments underline the importance of a structured process and regular review with peers or management. Process-focused approaches are recommended over focusing solely on individual skills, as processes can help identify and improve weak points.
Suggestions for Further Education: Some responses advise working through statistics textbooks or courses to fill knowledge gaps, highlighting the importance of continuous education in fields like econometrics and graduate-level statistics.
Acknowledging and Addressing Imposter Syndrome: Many users identify the OP's feelings as imposter syndrome, noting that it's common among professionals who are new to their field. They advise accepting and learning from mistakes, and emphasize that no one is expected to be perfect, especially in a complex field like data science.
Practical Tips and Reassurances: Practical tips like writing out steps for analyses, implementing peer reviews, and focusing on the final results of work are also given. The consensus is that mistakes are a natural part of the learning process and can be mitigated through careful procedures and continuous learning.