Getting started with data science can be a confusing journey, especially if the person is not from the STEM field. In this article, I explore and define the essential aspects of data science you need to get started correctly. This article will mainly tackle the technical skills required for a data scientist. To become a data scientist, you need to be familiar with programming, statistics, and machine learning. This article will outline the steps you can take to become a data scientist and the important libraries you need to know.

Whenever there is any mention of ethics in the context of AI, the topic of bias & fairness often follows. Similarly, whenever there is any mention of training and testing machine learning models, the trade-off between bias & variance features heavily. But do these two mentions of bias refer to the same thing? In order for machines to learn these patterns, especially in "supervised learning", they go through a training process whereby an algorithm extracts patterns from a training dataset, typically in an iterative manner. It then tests its predictions on an unseen (out-of-sample) test dataset to validate if the patterns it had learnt from the training dataset are valid. Bias: The action of supporting or opposing a particular person or thing in an unfair way, because of allowing personal opinions to influence your judgment.

Data science is one of India's rapidly growing and in-demand industries, with far-reaching applications in almost every domain. Not just the leading technology giants in India but medium and small-scale companies are also betting on data science to revolutionize how business operations are performed. Data science is the field where large datasets are collected, analyzed, and interpreted in a way that assists in making critical business decisions in a better manner. Data scientists are the experts in the data science community, having considerable knowledge and experience in utilizing scientific techniques to gather and interpret monstrous data to be used for a specific purpose or project. Data science is a perfect field for anyone with a passion for data and numbers and a keen interest in mathematics and technology.

In response to growing recognition of the social impact of new AI-based technologies, major AI and ML conferences and journals now encourage or require papers to include ethics impact statements and undergo ethics reviews. This move has sparked heated debate concerning the role of ethics in AI research, at times devolving into name-calling and threats of "cancellation." We diagnose this conflict as one between atomist and holist ideologies. Among other things, atomists believe facts are and should be kept separate from values, while holists believe facts and values are and should be inextricable from one another. With the goal of reducing disciplinary polarization, we draw on numerous philosophical and historical sources to describe each ideology's core beliefs and assumptions. Finally, we call on atomists and holists within the ever-expanding data science community to exhibit greater empathy during ethical disagreements and propose four targeted strategies to ensure AI research benefits society.

While people may be thinking that AI is still in a research and development stage, they don't actually realize that this isn't true for a lot of cases. In this article, I am going to demonstrate how AI actually helped many organizations to fight the Covid, which I consider to be "indirect help". On another hand, I will also show that it directly helped to develop the actual Covid vaccine. If you haven't checked out my recent blog post on Stanford's Covid mRNA vaccine degradation prediction competition on Kaggle where tons of people from the data science community were actually working on improving the vaccine models, check this out: Although IBM didn't actually come up with the final vaccine model, they were heavily working on it. The IBM team is using a computational model of the spike (S-protein) of SARS-CoV-2 to model its interaction with the human ACE2 receptor .

The fundamentals of Data Science include computer science, statistics and math. It's very easy to get caught up in the latest and greatest, most powerful algorithms -- convolutional neural nets, reinforcement learning etc. As an ML/health researcher and algorithm developer, I often employ these techniques. However, something I have seen rife in the data science community after having trained 10 years as an electrical engineer is that if all you have is a hammer, everything looks like a nail. Suffice it to say that while many of these exciting algorithms have immense applicability, too often the statistical underpinnings of the data science community are overlooked.

In Machine Learning, we use data and algorithms to build intelligent systems. If you are new to machine learning, you need to work on beginner-level machine learning projects to understand how to use machine learning algorithms on datasets to solve problems. So if you're looking for project ideas as a machine learning beginner, this article is for you. In this article, I'll introduce you to some of the best machine learning project ideas for beginners. Cryptocurrency price prediction is the problem of regression analysis and time series analysis.

The 7 fastest-growing data science trends for 2022 and beyond are listed below. We'll also discuss how these developments will affect data scientists' jobs and daily lives. These are the main trends to keep an eye on, whether you're actively participating in the data science community or simply concerned about personal data privacy. Since 2017, searches for "deep fake" have surged by 900 percent. When public personalities are deeply fabricated and the media learns about it, interest surges.

To help grow and strengthen the data science community, we are offering several ways to attend the leading applied data science virtual conference, ODSC APAC for free. Check out below how you can join us for talks by some of the leading experts in data science, AI, machine learning and more for free. At ODSC, diversity, inclusion, and democratization of data science are core values. At each conference we strive to create an inclusive experience, ensuring unrepresented groups feel welcome in the data science community. As part of this initiative, we are offering a limited amount of scholarship passes for free and discounted passes.

There are many beginners in the field of data science since when the requirement of data scientists boosted in this pandemic. Most of the time, they have questions like where can I find datasets for machine learning/ deep learning projects? Where can I get free datasets for data science? So here I am writing a piece of useful information for every beginner from the very basic. I hope this article will be helpful to beginners as well as advanced data science professionals who were not familiar with these resources earlier.