Understanding Buzzwords in Data Science
Have you come across Data Science jargon and buzzwords such as Artificial intelligence (AI); Machine Learning (ML); Deep Learning (DL); Neural Network (NN) etc.. want to know more about them and how each of them differ? If so then checkout this article, which will give you a brief idea what these are.
Good job explaining the ''buzzwords''. It is lack of common interpretation of the Data Science terminology that often leads to misunderstandings among the scholars.
Piecewise hazard model for under-five child mortality
The application of piecewise hazard model in mortality data becomes more useful in survival methods. This method is used to find the number and location cut points and to estimate the hazard model. The objective of this paper is to illustrate the piecewise constant hazard model and to find significant factors for under-five child mortality.
Data set for content-based package selection in the telecom
Dear team, I am working on a research based on content-based package selection in the telecom industry. So I want to get a data set based on customer usage of their package. I mean internet usage, voice usage etc. Can you please give me a support. Anyone have that type of a data set, can you please share with me.
I have a Data Cloud Lab and Github too, Ready to contribute someone like you. please send me your resume at firstname.lastname@example.org. I will find out your solution. Thank you
Explaining the fundamentals of the P-value
Many times, I have seen data science professionals who know very well what is p-values but they struggle to explain it. I have also seen many professionals who don’t want to get into technical details but want to know more and understand why it is so important.
GULP: A DEVOPS Based Tool for Web Applications
Gulp is used by software development teams as the streaming build system with which a number of tasks can be programmed and automated in an effective way.
This article is very informative and the steps which are been soon can be easily approcable .A lit more effective and efficient way the author tries to summarise the step here.
Data Science and Econometrics
What is the correlation between Data Science and Econometrics.I have been thinking to find out if Data Science can stand alone without Econometrics. Will it rather be wise to say Econometrics forms a better modelling blocks on which Data Science thrive. I will like to know the correlation between this two fields. Thanks
The Role of Data Science in Big Data Analytics
As the volumes of data increase each day, organisations are begining on Data Science and Data Analytics to make sense of it. This article is a basic introduction to those new to the field
Looking for Data Science Projects 2020
Hello All, Can anyone know latest data science project name in 2020? I want to learn about all the project information during this lockdown.
1. Sentiment Analysis2. Recognizing the face news3. Detection of the Parkinson disease4. Recognizing the speech emotions5. Age and Gender Detection6. OLA data analysis7. Credit Card Fraud Detection8. Recommended Movie9. Customer segmentation10. Classifying Breast CancerHere are the top 10 data science project which are listed one of my friend, want to know more about these visit here: https://hackr.io/blog/data-science-projects
Data Scientist's Role & Ethical Challenge
The job of the data scientist and the ethical challenges they face
What ethical issues are involved with Big Data? But, overexposed or not, the Big Data revolution raises a bunch of ethical issues related to privacy, confidentiality, transparency and identity.
The art of Learning to Learn by using the Power of Meta-Learning in AI
Machine learning is a fast-paced area and many types of research are ongoing. One of the fastest growths can be seen in the area of Meta-learning. As it is becoming more popular and more meta-learning techniques are being developed, it is important to understand this area of data science. Meta-learning caught my attention somewhere back in 2018
Very nice article Abhishek. Particularly, Three main steps to create a meta-learning model are too good.