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- Why did you take part in the Zindi challenge?
- Cobus: “Because my weekends are empty. Jokes, I like the subtle competition. Everyone is given the same data and has the same tools at their disposal, so technically there is no reason why one can’t achieve the same score as whoever is currently winning a competition. Whenever I am training I use this to motivate me.”
- Cobus: “Because my weekends are empty. Jokes, I like the subtle competition. Everyone is given the same data and has the same tools at their disposal, so technically there is no reason why one can’t achieve the same score as whoever is currently winning a competition. Whenever I am training I use this to motivate me.”
- How did winning the Zindi challenge contribute to your professional growth and opportunities in your career?
- Cobus: “Hopefully, it reassured my bosses that I am a capable modeller.”
- Cobus: “Hopefully, it reassured my bosses that I am a capable modeller.”
- Can you share any specific strategies or techniques you utilised during the challenge that helped you succeed?
- Cobus: “Before running any models I try to understand each column and get a feel for how these variables are related to our outcome (if at all). I usually spend a day or two just drawing simple graphs and then another couple of days playing around with basic correlations using simple OLS regressions.”
- Cobus: “Don’t be afraid to drop variables: If a variable doesn’t seem to add much statistically or doesn’t make intuitive sense don’t be afraid of getting rid of it.”
- What resources or learning materials would you recommend for aspiring data scientists who are preparing for the Zindi Challenge?
- Cobus: “I would recommend that they go through the shared codes of old competitions.”
- Cobus: “I would recommend that they go through the shared codes of old competitions.”
- What advice do you have for young individuals who want to participate in the Zindi Challenge to showcase their data science skills.
- Cobus: “Keep it simple. Draw graphs. Run regression models. Especially if the dataset is small.”
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