Introduction – Top Myths about Data Science
Data science is one of the most trending and demanding skill set as companies are using it as a powerful tool to enhance, modify their strategies to be competitive in the market. Most of the companies are already into AI and data science, while many of them are trying to adopt it. There are lot of myths and misconceptions which is holding them back to invest in this field.
This blog is about the top misconceptions or myths about data science, which is not only making companies to be on the backseat but also holding back the prospect data scientists to master this skill. Let us see few of the myths about data science.
Difficult to find data scientists
The top myth that the organizations have is, there is lack of skilled data scientists available, which is also highlighted in most of the articles, blogs, etc. Most of the companies are targeting the real good data scientists who have mastered the skills of mathematics, programming and domain expertise. Very few resources have master’s degree or Ph.D in all the fields, that’s where they feel there is shortage.
There are lot of organizations which are trying to bridge this gap, we at Netzwerk academy focus on all these expertise and train students to gain knowledge in the field of mathematics, programming and various domain related projects. Apart from the theoretical concepts which are covered, the focus is also on the practical implementation which is the most important factor to make a student industry ready. The companies instead of looking for the degree in these fields can get most from the trained students.
Regulated companies can’t get into data science
While data is very confidential and any information leakage will lead to huge financial loss as well as ethical loss, most of the companies who have regulatory law are thinking to get into data science. Apart from the personal protected information, there are several other data points which can be used and are not restricted. Industries such as health, pharmacy, education where there are strict regulatory laws with respect to data are investing into AI and data science, are benefitted without leaking any protected data.
Learning tools is enough to be a data scientist
Many people believe that if they master either python or R, they will master data science. Although these are the tools to work with, data scientists are lot more than just these tools. Deeper understanding of all the techniques used, how the algorithm works, the mathematics behind it, hyperparameter tuning, etc are much needed. Along with the technical concepts, one should also acquire non-technical skills such as ability to breakdown problem statement, domain knowledge, excellent communication skills. Thus, data science is not just about learning a programming language, its combination of various skills with programming being one of them.
Complex models outperform simple models
Although the models such as neural networks in deep learning have evolved and proven to be very efficient to solve complex problems like image classification, NLP, etc that does not make the older machine learning models obsolete.
Complex models are not necessarily the best models when the problem to be solved is relatively very simple. In fact, the simpler models such as linear regression, decision trees etc outperform the complicated neural networks in solving simple problems. Complex models need large amounts of data, if the data is relatively smaller then they tend to under perform. The complicated structure is difficult to understand and explain, when it comes to tuning the hyper parameter, also it becomes difficult to understand them.
Data science is about building models to predict future
To be able to design a model which can predict the outcome is a cool thing, most of the people think that building model is the task in data science. To be precise, it has set of tasks to perform and its not just about building predictive models.
Data science involves the following set of tasks which tells us its way more than just building a model
- Problem statement breakdown
- Data collection
- Data reshaping
- Data cleaning
- EDA(Exploratory data analysis)
- Choice of model
- Validate the model
- Test the model
- Present the results using visualization
It’s not just a direct step to build a model, instead involves various disciplined task. This was all about top myths about data science. If you want to read what Data Science is, click here.