Forget Data Science; Put Data In Order First
Or
Is Your Course Data Inclusive, Time To Ask?
“Data is food for AI”, not my words. Andrew Ng, one of the pioneers in the field of artificial intelligence, has stated this. Unfortunately, in today’s world, the discussion is mainly around the plate, while the food is missing. So let me come straight to the point — artificial intelligence, machine learning, and data science courses are selling like hot cakes in the market.
From national colleges and international universities to online platforms like Coursera, Udemy, edX, and other online platforms all offer artificial intelligence courses both online and offline. Most often, the talk revolves around coding or other data science skills, not the data itself. Yet, according to a poll of data scientists by Anaconda, data scientists spend roughly 45%, i.e., almost half of their time on data preparation tasks, including importing and cleaning data.
It surprises me many a time how many people jump right into Data Science without the right foundation for analyzing and presenting data. One must find anomalies in the data, missing values, aggregations, preprocessing, transformations, and more. Hence, this calls for a change, not just in the attitude but in the curriculum itself, to include “data” as a minor or major in the undergraduate programmes. This entails a question — why is data so important in the first place?
Data itself is an asset
Once out of college, any student needs to extract the very basic thing for the companies — produce an output from the AI system. This desired output can only be derived by comprehending the inputs. The input here is nothing but data. There is no prize in guessing that the output will be distorted if the inputs are inaccurate in any manner, and the conclusions will lead the project in the wrong direction. Training data quantity and quality are frequently the most important factors in a model’s performance. Once the training data angle is taken care of, the rest often follows.
Data gives experience: Similar to humans, AI programs get better with practice. Data offers the instances required to train prediction and classification models. Image recognition is a good case in point. The availability of data through ImageNet changed the course of advancement in image understanding and enabled computers to perform at a level comparable to humans.
Data helps you tell a compelling story: The best approach to using data to generate new knowledge, new choices, or new actions is through data storytelling. It is the process of using objective data and verbal communication to create a compelling story supported by facts. It makes use of data visualization techniques such as charts and pictures to get the audience interested in what the data means. Large datasets are analyzed and filtered to reveal insights and new or different ways to understand the data, which are then used to generate data-driven tales. They are designed for a particular audience and the environment in which they are used.
A strong data foundation is a core: Any AI solution must have a solid database as its foundation. Unfortunately, it is difficult and labor-intensive to locate and integrate enterprise data dispersed across hybrid and multi-cloud systems, explore and prepare the data to extract the most useful information from it, and manage and regulate the company data.
Furthermore, consider, for instance, supervised learning as one of the dominant fields and a significant training model today, whether in standard machine learning or deep learning. Here also, more and more labelled data is required to increase the model’s efficacy, particularly in deep learning. As a result, the issue of AI bias could pop up if the training data for artificial intelligence is not sufficiently diversified and objective.
Let me conclude
The most significant result of the rush towards digitization for the majority of businesses was a sharp increase in the types and amounts of data they started to gather. That data leads to a new analytics revolution that’s remaking today’s economies. As a result, data is termed the new currency. The question of how and where to spend this currency might vary from one company to another, but one fact remains — data sits at the heart of whatever they do.
The reality today is that organizations need personnel with the appropriate data skills to manage, interpret, and preserve data. Therefore, mastering data skills are among the most important for job seekers and career builders to master.
