Smart Data Collective

SEPTEMBER 13, 2021

Big data has been a gamechanger for countless companies in virtually every industry. Construction analytics is a new field that was worth just over $5 billion in 2018. The construction industry is no exception. It is growing at a rate of over 19% a year and should be worth $15.21 billion by 2026.

Analytics 94

Analytics Cost-Benefit Big Data Statistics 94

Sisense

JANUARY 14, 2021

However, the methods they had at their disposal initially couldn’t keep up with their growing user base and the ever-evolving world of big data. joining the BI team at Billie in 2018. He works on reporting, analysis, and data modeling. Read on to see how this enterprising team did it.

Dashboards 85

Dashboards Testing Business Intelligence Interactive 85

Smart Data Collective

OCTOBER 8, 2021

This is according to Barclay Hedge founder and President Sol Waksman in his July 2018 statement. “Most of the hedge fund managers surveyed are leveraging advanced algorithms and human judgment to deliver smarter investment decisions.” Perform quantitative analysis. Machine learning has done a lot to help them improve financial trading.

Machine Learning 113

Machine Learning Testing Strategy Modeling 113

Alation

FEBRUARY 20, 2020

For those of us who champion the power of data, the past five years have been an incredible ride thanks to the rise of big data. And here’s the catch: in spite of our recent data-driven achievements, the evidence suggests that humans may well be in the dark ages of data.

Big Data 52

Big Data Data-driven Statistics Metrics 52

Smart Data Collective

JULY 1, 2021

Become Familiar with Ransomware Statistics. Ransomware threats have increased exponentially since 2018 and businesses fall victim to an attack approximately every 14 seconds. More importantly, AI cybersecurity tools can be trained to recognize ransomware threats and protect against them.

Technology 82

Technology Machine Learning Statistics Data-driven 82

Domino Data Lab

JULY 22, 2019

He was saying this doesn’t belong just in statistics. He also really informed a lot of the early thinking about data visualization. It involved a lot of interesting work on something new that was data management. To some extent, academia still struggles a lot with how to stick data science into some sort of discipline.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

IBM Big Data Hub

JANUARY 10, 2023

To make it easy for clients to understand how to utilize this capability within NPS, a demonstration was created that uses flight delay data for all commercial flights from United States airports that was collected by the United States Department of Transportation (Bureau of Transportation Statistics). Prerequisites for the demo.

Data Warehouse 52

Data Warehouse Cost-Benefit Statistics Data Processing 52

Domino Data Lab

AUGUST 8, 2019

to join data science teams, e.g., to support advertising, social networks, gaming, and so on—I hired more than a few. 2018 – Global reckoning about data governance, aka “Oops!…We They tend to use less machine learning, but more advanced statistical practices, since the outcomes (government policies, etc.)

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

O'Reilly on Data

MARCH 20, 2019

Distributed systems and models : For better or worse, we live in the age of big data. Many organizations are now using distributed data processing and machine learning systems. But keeping a placeholder for them when scoring new data, or when retraining future iterations of your model, may come in very handy one day.

Modeling 219

Modeling Machine Learning Predictive Modeling Consulting 219

Peter James Thomas

AUGUST 12, 2018

Let’s cover what can go wrong (note: this section is not intended to be exhaustive, links are provided to more in-depth articles where appropriate): Accuracy of Data that is captured. A number of factors can play into the accuracy of data capture. Integrity of statistical estimates based on Data. million ± £0.5

Metrics 49

Metrics Statistics Data Quality Measurement 49

datapine

NOVEMBER 19, 2019

Currently, popular approaches include statistical methods, computational intelligence, and traditional symbolic AI. Some more examples of AI applications can be found in various domains: in 2020 we will experience more AI in combination with big data in healthcare. One of the IT buzzwords you must take note of in 2020.

Technology 279

Technology Internet of Things IT IoT 279

Smart Data Collective

APRIL 14, 2020

Data showed that Data Science and Analysis, comprising 21% of data scientists, is the degree that’s most likely to get you into data science in 2020, followed by the previous leader Computer Science (18.3%) and the traditional Statistics &Mathematics (16.3%). Coding Languages.

Data Science 89

Data Science Statistics IT Big Data 89

Juice Analytics

NOVEMBER 20, 2018

These are some of our favorite books and gift ideas for the data scientist, designer or analyst in your life. Books we read in 2018. Storytelling with Data - This one already feels like a classic. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy - This is the book that keeps us grounded.

Visualization 40

Visualization Statistics Big Data Interactive 40

IBM Big Data Hub

OCTOBER 16, 2023

In addition, certain countries have privacy laws that restrict the disclosure of personal data to third parties. Data sovereignty in the EU is an evolving field. There have been calls in Europe to create a stronger European-based cloud infrastructure that can help better ensure data sovereignty within the EU’s member states.

Cost-Benefit 68

Cost-Benefit Data Governance Risk Data-driven 68

Domino Data Lab

MARCH 3, 2019

Over the past six months, Ben Lorica and I have conducted three surveys about “ABC” (AI, Big Data, Cloud) adoption in enterprise. O’Reilly Media published our analysis as free mini-books: The State of Machine Learning Adoption in the Enterprise (Aug 2018). Spark, Kafka, TensorFlow, Snowflake, etc., will not save you there.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Domino Data Lab

MAY 8, 2019

2018-06-21). They also require advanced skills in statistics, experimental design, causal inference, and so on – more than most data science teams will have. Having more data is generally better; however, there are subtle nuances. Use of influence functions goes back to the 1970s in robust statistics.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

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. These are as follows: General Data Articles. Data Visualisation. Statistics & Data Science.

Data-driven 49

Data-driven Statistics Big Data Data Science 49

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 234

Machine Learning Technology Deep Learning Data Science 234

datapine

NOVEMBER 27, 2019

The demand for real-time online data analysis tools is increasing and the arrival of the IoT (Internet of Things) is also bringing an uncountable amount of data, which will promote the statistical analysis and management at the top of the priorities list. 9) Data Automation. How can we make it happen?

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361