CIO Business Intelligence

MAY 28, 2025

For businesses, this requires coordination from legal, compliance, data science, product teams and beyond, to work together to assess AI use cases, map applicable regulations and implement proactive governance measures. In other words, those doing business globally are going through the same growing pains as GDPR back in 2018.

Risk

Analytics Vidhya

NOVEMBER 1, 2022

This article was published as a part of the Data Science Blogathon. Source: Canva|Arxiv Introduction In 2018 GoogleAI researchers developed Bidirectional Encoder Representations from Transformers (BERT) for various NLP tasks.

Data Science

Analytics Vidhya

NOVEMBER 3, 2022

This article was published as a part of the Data Science Blogathon. Source: Canva Introduction In 2018, GoogleAI researchers released the BERT model. It was a fantastic work that brought a revolution in the NLP domain. However, the BERT model did have some drawbacks i.e. it was bulky and hence a little slow.

Modeling

Analytics Vidhya

DECEMBER 30, 2021

This article was published as a part of the Data Science Blogathon Introduction In 2018, a powerful Transformer-based machine learning model, namely, BERT was developed by Jacob Devlin and his colleagues from Google for NLP applications.

Machine Learning

Analytics Vidhya

JULY 29, 2022

This article was published as a part of the Data Science Blogathon. According to the IDC report, the world will spend $160 billion on cloud services and infrastructure in 2018. Introduction Cloud computing is one of the fastest-growing IT technologies today. By 2025, 83% of enterprise workloads will be in the cloud.

Data Science

Analytics Vidhya

OCTOBER 28, 2022

This article was published as a part of the Data Science Blogathon. Source: Canva Introduction In 2018 the researchers of OpenAI presented a framework for achieving strong natural language understanding (NLU) with a single task-agnostic model through generative pre-training and discriminative fine-tuning.

Data Science

Analytics Vidhya

OCTOBER 19, 2022

This article was published as a part of the Data Science Blogathon. Source: Canva Introduction In 2018, Google AI researchers came up with BERT, which revolutionized the NLP domain.

Modeling

O'Reilly on Data

SEPTEMBER 12, 2018

Watch highlights from expert talks covering data science, machine learning, algorithmic accountability, and more. People from across the data world are coming together in New York for the Strata Data Conference. The future of data warehousing. Von Neumann to deep learning: Data revolutionizing the future.

IoT

O'Reilly on Data

FEBRUARY 18, 2020

Growth is still strong for such a large topic, but usage slowed in 2018 (+13%) and cooled significantly in 2019, growing by just 7%. Within the data topic, however, ML+AI has gone from 22% of all usage to 26%. In 2019, as in 2018, Python was the most popular language on O’Reilly online learning. Security is surging.

Data-driven

Analytics Vidhya

OCTOBER 27, 2022

This article was published as a part of the Data Science Blogathon. Source: Canva Introduction In 2018 Google AI released a self-supervised learning model […]. The post A Gentle Introduction to RoBERTa appeared first on Analytics Vidhya.

Data Science

O'Reilly on Data

AUGUST 24, 2018

Watch keynotes covering Jupyter's role in business, data science, higher education, open source, journalism, and other domains, from JupyterCon in New York 2018. Jupyter trends in 2018. Paco Nathan shares a few unexpected things that emerged in Jupyter in 2018. Watch " Jupyter trends in 2018.".

Machine Learning

datapine

AUGUST 14, 2019

Data science has become an extremely rewarding career choice for people interested in extracting, manipulating, and generating insights out of large volumes of data. To fully leverage the power of data science, scientists often need to obtain skills in databases, statistical programming tools, and data visualizations.

Data Science

Data Science and Beyond

JULY 22, 2018

I got my first data science job in 2012, the year Harvard Business Review announced data scientist to be the sexiest job of the 21st century. Two years later, I published a post on my then-favourite definition of data science , as the intersection between software engineering and statistics. But what does it mean?

Data Science

O'Reilly on Data

JANUARY 29, 2019

In 2018, we decided to run a follow-up survey to determine whether companies’ machine learning (ML) and AI initiatives are sustainable—the results of which are in our recently published report, “ Evolving Data Infrastructure.”. Data scientists and data engineers are in demand.

Deep Learning

Data Science 101

APRIL 26, 2019

While it is not one of the popular programming languages for data science, The Go Programming Language (aka Golang) has surfaced for me a few times in the past few years as an option for data science. I decided to do some searching and find some conclusions about whether golang is a good choice for data science.

Data Science

IBM Big Data Hub

JANUARY 8, 2019

Data science was one of the hot topics of 2018, and it’s likely to dominate again in 2019. We've asked five key data science influencers to take a look back at 2018 and look ahead at what's to come in 2019.

Data Science

Business Over Broadway

MARCH 8, 2021

Results of a survey of data professionals show that about 1 out of 5 are women. Ways of improving gender diversity in the field of data science are offered. How does gender diversity look in the data science world? Annual Salaries of Data Professionals from the US. Click image to enlarge. Salary Differences.

Data Science

Analytics Vidhya

APRIL 29, 2024

Introduction In 2018, when we were contemplating whether AI would take over our jobs or not, OpenAI put us on the edge of believing that. Our way of working has completely changed after the inception of OpenAI’s ChatGPT in 2022. But is it a threat or a boon?

Software

O'Reilly on Data

FEBRUARY 4, 2019

Temporal data and time-series. Automation in data science and big data. At the 2018 Strata Data London, data privacy and GDPR were big topics. In fact, our 2018 conference happened the same week GDPR came online. Deep Learning. Text and Language processing and analysis.

Machine Learning

Smart Data Collective

DECEMBER 3, 2021

Are you looking to get a job in big data? The Bureau of Labor Statistics reports that there were over 31,000 people working in this field back in 2018. However, it is not easy to get a career in big data. Prepare for Machine Learning Interview Questions for Your Data Science Job. That could be a wise career move.

Machine Learning

Data Science 101

JUNE 3, 2019

Looking for a few academic data science papers to study? Here are a few I have found interesting. The are not all from the past 12 months, but I am including them anyhow.

Data Science

Business Over Broadway

DECEMBER 20, 2018

The importance of data science and machine learning continues to grow in business and beyond. I did my part this year to spread interest in data science to more people. Below are my top 10 blog posts of 2018: Favorite Data Science Blogs, Podcasts and Newsletters. Click image to enlarge.

Machine Learning

Data Science 101

MARCH 6, 2019

University can be a great way to learn data science. Luckily, a few of them are willing to share data science, machine learning and deep learning materials online for everyone. However, many universities are very expensive, difficult to get admitted, or not geographically feasible.

Data Science

IBM Big Data Hub

MARCH 21, 2018

Think 2018 is in full swing. And we’re thrilled to take part in conversations about data science, machine learning, AI and much more. We’re inspired hearing from leaders across industries using analytics to transform their business. Here are some highlights from Wednesday at Think.

Machine Learning

Business Over Broadway

JUNE 29, 2020

The practice of data science, including work in machine learning and artificial intelligence, requires the use of analytics tools, technologies and programming languages. A recent survey of nearly 20,000 data professionals by Kaggle revealed that Python, SQL and R continue to be the most popular programming languages.

Machine Learning

O'Reilly on Data

MAY 21, 2019

For example, in a July 2018 survey that drew more than 11,000 respondents, we found strong engagement among companies: 51% stated they already had machine learning models in production. With all the hype around AI, it can be tempting to jump into use cases involving data types with which you aren’t familiar. Source: O'Reilly.

Machine Learning

Rocket-Powered Data Science

DECEMBER 16, 2018

In a related post we discussed the Cold Start Problem in Data Science — how do you start to build a model when you have either no training data or no clear choice of model parameters. Workshop on Meta-Learning (MetaLearn 2018).

Machine Learning

Business Over Broadway

JANUARY 13, 2019

The practice of data science requires the use of analytics tools, technologies and programming languages to help data professionals extract insights and value from data. A recent survey of nearly 24,000 data professionals by Kaggle revealed that Python, SQL and R are the most popular programming languages.

Machine Learning

Business Over Broadway

JANUARY 20, 2019

Analysis of usage patterns of 16 data science programming languages by over 18,000 data professionals showed that programming languages can be grouped into a smaller set (specifically, 5 groupings). Therefore, I chose a 5-factor solution to explain the relationships among the 16 data science tools.

Machine Learning

Domino Data Lab

JANUARY 24, 2019

In 2018 we saw the “data science platform” market rapidly crystallize into three distinct product segments. Over the last couple years, it would be hard to blame anyone for being overwhelmed looking at the data science platform market landscape. Proprietary (often GUI-driven) data science platforms.

Data Science

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

by THOMAS OLAVSON Thomas leads a team at Google called "Operations Data Science" that helps Google scale its infrastructure capacity optimally. It required investments from our data science team to re-think our statistical forecasting approach to make it easier to compare against customer forecasts. 3] Hyndman, R.J., &

Forecasting

Sanjeev Mohan

MARCH 18, 2018

Ladies and Gentlemen, we have a new buzz word for 2018: Operationalization ! I attended this year’s edition of Strata Data show last week in San Jose. Most companies now want to be known as Data Science companies. The competition is intensifying across the entire stack of Big Data.

Big Data

Domino Data Lab

MAY 15, 2019

This Domino Data Science Field Note covers Pete Skomoroch ’s recent Strata London talk. I was fortunate to see an early iteration of Pete Skomoroch ’s ML product management presentation in November 2018. Pete Skomoroch, San Francisco, November 2018. It focuses on his ML product management insights and lessons learned.

Machine Learning

Dataiku

AUGUST 20, 2020

This blog post was originally published in October 2018 on Data From the Trenches. TL;DR : As data science evolves as a discipline, we think that it’s time to have a more systematic approach to feature engineering.

Data Science

Rocket-Powered Data Science

OCTOBER 16, 2020

The survey also had forward-looking questions, as well as historical comparisons and trends from results of similar “state of AI in the enterprise” surveys in 2018 and 2019. 2) The percentage who are concerned about other countries being more advanced than the U.S. in AI technology and implementation increased from 70% to 82%.

Strategy

Peter James Thomas

APRIL 9, 2019

Despite starting to write this piece on 18 th December 2018, I have somehow sneaked into the second quarter before getting round to completing it. Anyway, 2018 was a record-breaking year for peterjamesthomas.com. Given the advent of the Maths & Science section, there are now seven categories into which I have split articles.

Data-driven

Domino Data Lab

JULY 2, 2019

In other words, using metadata about data science work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated. The approach they’ve used applies to other popular data science APIs such as NumPy , Tensorflow , and so on.

Metadata