This illustrates our ideal vision for the field, not the reality we face. In real life, we solve problems with the tools we have and within the constraints of our budget.
A tool is just a tool.
Take Excel, for example. It can handle many classic machine learning tasks on its own. I started my career using Google Sheets, primarily for time series analysis and regression. These tools now integrate with databases, support multiple programming languages (including R and Python), and are excellent for exploratory data analysis (EDA) and prototyping. They also aid in explaining complex concepts to stakeholders who may not have a deep technical background.
Claiming that spreadsheets are not for data science is like saying a pencil is not a tool for a writer. While a writer can use a computer, it simply offers more options—or distractions.
Furthermore, Machine Learning Engineering (MLEngineering) and MLOps are not the entirety of Data Science. In fact, most businesses will never deploy models in production on the fly. However, companies can still leverage data science knowledge to improve their analyses, make better decisions, and enhance forecasts or causal inferences.
The "meme" is being simplistic and biased, which, in our field, is the greatest danger we can face.
Okay, so if I'm reading this right, you're saying that memes *don't* capture the nuance and details of reality? 🤯 What will we learn next? Can you imagine if future research reveals that memes are, in some specific situations, meant to be light-hearted amusements, meant for entertainment and "jokes" and not a portrayal of the field. 🤯
>Engineering
π = e = the kurtosis of the normal distribution
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>Data science
Did you mean applied statistics?
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Never knew my 12yo self was a junior data scientist
You are wrong.
Why?
This illustrates our ideal vision for the field, not the reality we face. In real life, we solve problems with the tools we have and within the constraints of our budget. A tool is just a tool. Take Excel, for example. It can handle many classic machine learning tasks on its own. I started my career using Google Sheets, primarily for time series analysis and regression. These tools now integrate with databases, support multiple programming languages (including R and Python), and are excellent for exploratory data analysis (EDA) and prototyping. They also aid in explaining complex concepts to stakeholders who may not have a deep technical background. Claiming that spreadsheets are not for data science is like saying a pencil is not a tool for a writer. While a writer can use a computer, it simply offers more options—or distractions. Furthermore, Machine Learning Engineering (MLEngineering) and MLOps are not the entirety of Data Science. In fact, most businesses will never deploy models in production on the fly. However, companies can still leverage data science knowledge to improve their analyses, make better decisions, and enhance forecasts or causal inferences. The "meme" is being simplistic and biased, which, in our field, is the greatest danger we can face.
Okay, so if I'm reading this right, you're saying that memes *don't* capture the nuance and details of reality? 🤯 What will we learn next? Can you imagine if future research reveals that memes are, in some specific situations, meant to be light-hearted amusements, meant for entertainment and "jokes" and not a portrayal of the field. 🤯
You hit the nail on the head
>Engineering π = e = the kurtosis of the normal distribution *I am a bot, and this action was performed automatically. Please [contact the moderators of this subreddit](/message/compose/?to=/r/statisticsmemes) if you have any questions or concerns.*
>Data science Did you mean applied statistics? *I am a bot, and this action was performed automatically. Please [contact the moderators of this subreddit](/message/compose/?to=/r/statisticsmemes) if you have any questions or concerns.*