5 Mistakes To Avoid In Exploratory Data Analysis
It is not just leading enterprises but even mid-sized firms that are investing heavily in data science and big data projects. And that’s why executing a data science model with the correct predictions has become one of the top priorities for data science teams. In this article, we list down 5 common mistakes while exploring a data analysis and how to avoid them. 1| Choosing The Wrong Visualisation Tool It is very crucial to choose the right visualisation tool. Most of the time, data scientists fail to focus on visualising the data while concentrating more on the technical aspects of data analysis. In order to monitor the exploratory data analysis or representing the final results in an eye-catching way, it is very important to choose the right kind of visualisation of the data in the model. How To Avoid Defining the goal of visualisation should be taken into account while choosing the tool for the model. Rich visuals on the data insights can grow a strong root while analysing the data. 2| Understanding The Difference Between Correlation And Causation These are the two terms that have been mostly misunderstood by the data science enthusiasts and often used interchangeably. Thus, it can mislead a data scientist to wrong assumptions in the model and thus leading to a loss of money in the project. So understanding both the statistical terms is important to make the right conclusion. How To Avoid One simple way to avoid this misconception is to know and understand the basics in a clear way. Correlation is the measure that describes the size and direction of a relationship between two or more variables whereas on the other hand causation describes that one event is the outcome of the other event. 3| Focusing Only On Data One should keep in mind that data only isn’t enough to build an efficient data science model. It often happens that after extracting data from various sources, data science enthusiast starts implementing the findings without thinking much about how the analysis can be proved to be an advantage to the project. How To Avoid Data science enthusiasts should not forget that are also various parameters like statistical approaches that also need to be thoroughly understood to gain an efficient model. 4| Choosing The Wrong Model And Method Building a model can be easy but along with building a model, a data science enthusiast has to be ensured that the model provides the correct predictive power every time with different inputs. It is indeed crucial to re-validate the model after certain intervals to make sure that the model is working correctly. Also, the deep learning model such as Neural Network can be powerful and efficient rather than some other models of machine learning but it is not always convenient to use this model since this model is a complete black box model. It is harder to analyse the applicability as well as interpret the domain in this model. There are other statistical models which are simple and understandable as well as interpretable which one can use in their project. How To Avoid The best to avoid such mistakes is practice developing simpler models which may be easier for you to make understand to the non-technical people in an organisation along with scoring the data models with new and different data. It is efficient to apply a model according to the constraints of your model. 5| Ignoring The Probabilities It often happens because of curiosity, sometimes a data science enthusiast forgets to take into account the possibilities for a particular solution. It is not necessarily possible that A input will provide you B output, and in that case, an informed choice has to be made from not only one but various possibilities. How To Avoid Before giving any conclusion, a data science enthusiast must go through the scenario of the model and its probability without rushing for the available possibilities.
It is not just leading enterprises but even mid-sized firms that are investing heavily in data science and big data projects. And that’s why executing a data science model with the correct predictions has become one of the top priorities for data science teams. In this article, we list down 5 common mistakes while exploring […]
It is not just leading enterprises but even mid-sized firms that are investing heavily in data science and big data projects.
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["big data", "data science", "Go", "machine learning", "TPU", "AI", "neural network", "deep learning", "GAN", "R"]
["AI", "machine learning", "deep learning", "neural network", "data science", "TPU", "R", "Go", "big data", "GAN"]
A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box.