5 BIGGEST MISTAKES DONE BY BEGINNER DATA SCIENTISTS

Did you know that deleting columns in a dataset is one of the BIGGEST MISTAKES made by the beginner data scientists.

 Are YOU one of those?

By the end of this blog you are going to know 5 BIGGEST MISTAKES made by beginner data scientists.




To become a data scientist, you must keep in my to avoid these mistakes at ALL COST

1. DELETING COLUMNS - 
If you find any null value in the column, and then go for dropping the column. That's one of the BIGGEST MISTAKE. You should consider if that null value can be replaced with some value that does not change the bias of that column. Generally these values are central tendencies. That are mean, median and mode.

2. FORGET ENCODING-
If you think that your model is fitting successfully without encoding, so you should skip it. That your that's a mistake. Encoding helps the model to establish greater spectrum for the column and having the column represented as numerical helps the model to have a comparable outcome.

3. STATIC PARAMETER - 
If you are using your algorithms as it is for creating models, then you need to consider tuning the parameters. It can help you a lot to find the best model and with higher accuracy.

4. JUMPING INTO NEURAL NETWORK - 
If you skip all the algorithms to jump to neural network, you are making a mistake. You should consider other algorithms too as they are are also powerful. And given the RESOURCES for making the model, other algorithms can perform far better than neural network.

5. AVOIDING ANALYSIS - 
If you are avoiding analysis of the dataset and directly jumping it into your model, Then you are making BIG MISTAKE. Exploratory Data Analysis plays a huge in helping us familiar with data, and provide insight for what data we should use for model creation.

If want you to start your journey in data science by downloading your FREE copy of roadmap to Data Science from link below.


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