Learn Data Science in 2025
๐ญ. ๐๐ฝ๐ฝ๐น๐ ๐ฃ๐ฎ๐ฟ๐ฒ๐๐ผ'๐ ๐๐ฎ๐ ๐๐ผ ๐๐ฒ๐ฎ๐ฟ๐ป ๐๐๐๐ ๐๐ป๐ผ๐๐ด๐ต ๐
Pareto's Law states that "that 80% of consequences come from 20% of the causes".
This law should serve as a guiding framework for the volume of content you need to know to be proficient in data science.
Often rookies make the mistake of overspending their time learning algorithms that are rarely applied in production. Learning about advanced algorithms such as XLNet, Bayesian SVD++, and BiLSTMs, are cool to learn.
But, in reality, you will rarely apply such algorithms in production (unless your job demands research and application of state-of-the-art algos).
For most ML applications in production - especially in the MVP phase, simple algos like logistic regression, K-Means, random forest, and XGBoost provide the biggest bang for the buck because of their simplicity in training, interpretation and productionization.
So, invest more time learning topics that provide immediate value now, not a year later.
๐ฎ. ๐๐ถ๐ป๐ฑ ๐ฎ ๐ ๐ฒ๐ป๐๐ผ๐ฟ โก
Thereโs a Japanese proverb that says โBetter than a thousand days of diligent study is one day with a great teacher.โ This proverb directly applies to learning data science quickly.
Mentors can teach you about how to build a model in production and how to manage stakeholders - stuff that you donโt often read about in courses and books.
So, find a mentor who can teach you practical knowledge in data science.
๐ฏ. ๐๐ฒ๐น๐ถ๐ฏ๐ฒ๐ฟ๐ฎ๐๐ฒ ๐ฃ๐ฟ๐ฎ๐ฐ๐๐ถ๐ฐ๐ฒ โ๏ธ
If you are serious about growing your excelling in data science, you have to put in the time to nurture your knowledge. This means that you need to spend less time watching mindless videos on TikTok and spend more time reading books and watching video lectures.
Join for more: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING ๐๐
๐ญ. ๐๐ฝ๐ฝ๐น๐ ๐ฃ๐ฎ๐ฟ๐ฒ๐๐ผ'๐ ๐๐ฎ๐ ๐๐ผ ๐๐ฒ๐ฎ๐ฟ๐ป ๐๐๐๐ ๐๐ป๐ผ๐๐ด๐ต ๐
Pareto's Law states that "that 80% of consequences come from 20% of the causes".
This law should serve as a guiding framework for the volume of content you need to know to be proficient in data science.
Often rookies make the mistake of overspending their time learning algorithms that are rarely applied in production. Learning about advanced algorithms such as XLNet, Bayesian SVD++, and BiLSTMs, are cool to learn.
But, in reality, you will rarely apply such algorithms in production (unless your job demands research and application of state-of-the-art algos).
For most ML applications in production - especially in the MVP phase, simple algos like logistic regression, K-Means, random forest, and XGBoost provide the biggest bang for the buck because of their simplicity in training, interpretation and productionization.
So, invest more time learning topics that provide immediate value now, not a year later.
๐ฎ. ๐๐ถ๐ป๐ฑ ๐ฎ ๐ ๐ฒ๐ป๐๐ผ๐ฟ โก
Thereโs a Japanese proverb that says โBetter than a thousand days of diligent study is one day with a great teacher.โ This proverb directly applies to learning data science quickly.
Mentors can teach you about how to build a model in production and how to manage stakeholders - stuff that you donโt often read about in courses and books.
So, find a mentor who can teach you practical knowledge in data science.
๐ฏ. ๐๐ฒ๐น๐ถ๐ฏ๐ฒ๐ฟ๐ฎ๐๐ฒ ๐ฃ๐ฟ๐ฎ๐ฐ๐๐ถ๐ฐ๐ฒ โ๏ธ
If you are serious about growing your excelling in data science, you have to put in the time to nurture your knowledge. This means that you need to spend less time watching mindless videos on TikTok and spend more time reading books and watching video lectures.
Join for more: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING ๐๐
โค6๐1๐คฉ1
The New Data Scientist of 2025:
- Business background
- Self-educated
- Knows enough SQL
- Knows enough Python
- Knows enough machine learning
- Uses Microsoft Excel
- Uses AI to be productive
- Title isn't "Data Scientist"
- Business background
- Self-educated
- Knows enough SQL
- Knows enough Python
- Knows enough machine learning
- Uses Microsoft Excel
- Uses AI to be productive
- Title isn't "Data Scientist"
๐10๐คฉ1