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.
Given a set of incomplete data, consider a set of starting parameters. Expectation step (E – step): Using the observed available data of the dataset, estimate (guess) the values of the missing data. Maximization step (M – step): Complete data generated after the expectation (E) step is used in order to update the parameters. Repeat step 2 and step 3 until convergence.
The Expectation-Maximization (EM) algorithm is a way to find maximum-likelihood estimates for model parameters when your data is incomplete, has missing data points, or has unobserved (hidden) latent variables. It is an iterative way to approximate the maximum likelihood function.
Pivotal Data Labs Helps Predict Viewer Behavior and Build a Better TV Show
Media companies have long been interested in viewer habits and preferences. In the past, they were forced to rely on traditional sources of data to determine this information, which would then be used to help formulate the next series of TV shows, many of which would ultimately “flop.” However, the rise of unstructured data has given media companies and TV executives the means to dig deeper and make better decisions.
In the past, they were forced to rely on traditional sources of data to determine this information, - Quite a point
Data Science Allows Fashion Industry to Gain a More Complete Understanding of Consumers
The fashion industry is pretty visible in terms of high-profile events held in cities like New York or Milan. Fashion designers drape models with their latest creations, and then those models stroll down the catwalk while photographers turn night to noon with flashes. However, while that might be the public image of the industry, fashion companies face significant challenges. Rather than leading consumers with new designs and styles, these companies still must understand their audience, what drives them, and what consumer behaviors affect their success. Data science is being used to do just that.
Data Science Allows Fashion Industry to Gain a More Complete Understanding of Consumers article nicely explained.