1. Math and data analysis are often seen as synonymous, but the math used in data analysis is practical and easily applicable.
2. To become a data analyst, it's important to learn Excel, SQL, and Python, with Excel being a powerful tool for conducting simple data analyses.
3. Refreshing your memory of algebra and statistics is necessary, but you can get away with knowing just the basics. The focus should be on studying data analysis and visualization through hands-on projects.
The article titled "The Data Analyst Learning Roadmap for People Who Hate Math" by Madison Hunter provides a roadmap for individuals who want to become data analysts but are intimidated by the math involved. The author argues that while some math is involved in data analysis, it is not as scary as people think and can actually be fun. However, the article has several potential biases and missing points of consideration.
One-sided reporting: The article presents only one perspective on learning data analysis, which is focused on using Excel, SQL, and Python. While these are important tools for data analysis, there are other programming languages such as R that are widely used in the industry. The author dismisses R as difficult to learn without providing any evidence or counterarguments.
Unsupported claims: The author claims that Excel might be the only tool needed to become a data analyst and suggests that individuals should focus on learning Excel before moving on to other tools. However, this claim is unsupported and ignores the fact that many companies use more advanced tools such as Tableau or Power BI for data visualization.
Missing evidence: The article suggests that individuals can get away with knowing only algebra and statistics to become a data analyst. However, there is no evidence provided to support this claim or explain why these two areas of math are sufficient.
Promotional content: The article promotes Khan Academy's lecture series without providing any alternative resources or acknowledging potential limitations of using online courses for learning complex topics such as statistics.
Partiality: The author presents their personal experience of self-teaching themselves data analysis without acknowledging that everyone's learning journey may be different. Additionally, the author assumes that all individuals interested in becoming data analysts hate math, which may not be true for everyone.
Possible risks not noted: The article does not mention potential risks associated with relying solely on Excel for data analysis or using cheat sheets instead of memorizing functions in SQL.
In conclusion, while the article provides a useful roadmap for individuals interested in becoming data analysts, it has several potential biases and missing points of consideration. It is important for individuals to do their own research and consider multiple perspectives before embarking on a learning journey in data analysis.