Online marketing and startup growth are better if you can continuously test different ideas. The statistic comes into help when we have to perform A/B tests. The results you may achieve with the proper analysis can give your project a great boost.
Are your training and test sets comparable?
Data scientists usually split a dataset into training and test sets. Their model is trained on the former and then its performance is checked in the latter. But, if these sets are sampled wrongly, model performance may be affected by biases.
A language detection model in pure Javascript
Models are very powerful tools to be used in several web applications. The general approach is to make them accessible by using REST APIs. In this article, I’ll talk about a model that can be created in Python and then deployed in pure Javascript.
Visualize the predictive power of a numerical feature in a classification problem
Measuring the predictive power of some feature in a supervised machine learning problem is always a hard task to accomplish. Before using any correlation metrics, it’s important to visualize wether a feature is informative or not. In this article, we’re going to apply data visualization to a classification problem.
How to measure outlier probability
Outliers are a great problem for a data scientist. They are “strange points” in a dataset that must be checked in order to verify whether they are errors or real phenomena.
Why SQL is still important for data analysis
Data Science mixes different skills and there are some old skills that are still useful. One of such skills is SQL.
Do companies really need Deep Learning?
We’ve seen strong growth of Deep Learning techniques in the past few years. Thanks to technologies like Tensorflow and Keras, neural networks have become accessible by anybody in the world. But is Deep Learning really useful for you?
How my degree in Physics helped me become a better Data Scientist
I have a Master’s Degree cum laude in Theoretical Physics. When I started my journey into data science, I figured out how useful it is for this kind of job.
How to choose the bins of a histogram?
Histograms are a very useful tool when we want to give a quick sight to the shape of our data. However, we always have to choose the right number of bins.
How to calculate confidence intervals in Python
When we measure something, we always have to calculate the uncertainty of the result. confidence intervals are a very useful tool to calculate a range in which we can find the real value of the observable with a certain confidence.