Smart Data Collective

NOVEMBER 18, 2020

The Bureau of Labor Statistics estimates that the number of data scientists will increase from 32,700 to 37,700 between 2019 and 2029. Previously, such problems were dealt with by specialists in mathematics and statistics. Statistics, mathematics, linear algebra. Machine learning. Where to Use Data Science?

Data mining 143

Data mining Data Science Informatics Statistics 143

Rocket-Powered Data Science

MARCH 10, 2020

In 2019, I was asked to write the Foreword for the book “ Graph Algorithms: Practical Examples in Apache Spark and Neo4j “ , by Mark Needham and Amy E. And this: perhaps the most powerful node in a graph model for real-world use cases might be “context”. How does one express “context” in a data model?

Metadata 250

Metadata Machine Learning ROI Prescriptive Analytics 250

Business Over Broadway

MAY 22, 2019

Remember that digital transformation is about transforming your business and operating models with technology. Also, loyalty leaders infuse analytics into CX programs, including machine learning, data science and data integration. IoT Artificial Intelligence. So, become data literate. AI and Marketing (slide): [link].

Machine Learning 40

Machine Learning Technology Digital Transformation Data Science 40

CIO Business Intelligence

MARCH 15, 2022

Synthetic data is artificially generated information that can be used in place of real historic data to train AI models when actual data sets are lacking in quality, volume, or variety. Many models will decide that the most successful policy will be to label every transaction as non-fraudulent.”. Synthetic data use cases.

Strategy 145

Strategy Testing Modeling Sales 145

Insight

MAY 14, 2020

Image source: [link] Motivation Beginning around December 2019, patients with severe respiratory infections began arriving at hospitals in Wuhan, kicking off a race to identify the cause of the illness. Data Pipeline Input to the model is a FASTA file containing sequences of 100 base pairs.

Deep Learning 98

Deep Learning Statistics Modeling Machine Learning 98

Smart Data Collective

MARCH 23, 2020

With the help of data mining and machine learning, it is now possible to find the connections between seemingly disparate pieces of information. According to the SensorTower statistics , in 2019, a simple arcade game Stack Ball reached 100 million installs and only continued to grow. In 2019, this number increased to 2.4

Big Data 103

Big Data Recreation/Entertainment Data mining Optimization 103

Cloudera

DECEMBER 3, 2021

In 2019, the Gradient institute published a white paper outlining the practical challenges for Ethical AI. For example, training an interview screening model using education data often contains gender information. As discussed in this article , model design can also be a source of bias too. Model Drift. Governance.

Uncertainty 102

Uncertainty Measurement Metrics Risk 102

DataRobot

AUGUST 5, 2021

In 2019, Utah struck a deal with Banjo, a threat detection firm selling AI services to process live traffic feeds, dispatch logs, and other data. After making their first estimate, all participants were shown rental price predictions from a machine learning model. Is there too much hype about AI or too much doomsaying?

Uncertainty 98

Uncertainty Sales Risk Statistics 98

IBM Big Data Hub

AUGUST 9, 2023

Why phishing simulations are important Recent statistics show phishing threats continue to rise. Since 2019, the number of phishing attacks has grown by 150% percent per year— with the Anti-Phishing Working Group (APWG) reporting an all-time high for phishing in 2022 , logging more than 4.7 million phishing sites.

Testing 82

Testing Measurement Behavioral Analytics Reporting 82

FineReport

MAY 6, 2020

Machine learning. Machine learning murni menekankan kepada kemampuan prediktif dan pengimplimentasian algoritma tetapi statistik menekankan kepada tingkat penginterpretasiannya. Metode kuantitatif: analisis axis waktu, model probabilitas, optimalisasi. Prinsip database: model data, desain database.

Data mining 98

Data mining Data Warehouse Machine Learning Big Data 98

KDnuggets

AUGUST 21, 2019

Understanding Cancer using Machine Learning; Statistical Modelling vs Machine Learning; Is Kaggle Learn a "Faster Data Science Education?"; This week's news: Become More Marketable as a Data Scientist; Command Line Basics Every Data Scientist Should Know; Chatbots with Keras!; and much more!

Data Science 48

Data Science Marketing Statistics Machine Learning 48

Domino Data Lab

JANUARY 24, 2019

Before we get too far into 2019, I wanted to take a brief moment to reflect on some of the changes we’ve seen in the market. These solutions help data analysts build models by automating tasks in data science, including training models, selecting algorithms, and creating features. Reflections. Jupyter) or IDEs (e.g.,

Data Science 65

Data Science Marketing Data-driven Statistics 65

Smart Data Collective

AUGUST 8, 2019

Unfortunately, predictive analytics and machine learning technology is a double-edged sword for cybersecurity. Black Hat Hackers Exploit Machine Learning to Avoid Detection. This is largely because of their knowledge of machine learning. Big data is the lynchpin of new advances in cybersecurity.

Big Data 86

Big Data Data Processing Machine Learning Predictive Analytics 86

O'Reilly on Data

MARCH 20, 2019

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks.

Modeling 222

Modeling Machine Learning Predictive Modeling Consulting 222

Insight

APRIL 10, 2020

Insight is a community of over 3,000 data scientists, data engineers, machine learning engineers, consensus, DevOps, and security engineers who have completed an Insight Fellows Program and are now members of our professional network. In 2019, over 1,000 Insight Fellows completed one of our 7 programs across 6 locations & remote.

Uncertainty 74

Uncertainty Statistics Machine Learning Publishing 74

FineReport

DECEMBER 19, 2019

If you want to learn more about self-service BI tools, you can take a look at this review: 5 Most Popular Business Intelligence (BI) Tools in 2019 , to understand your own needs and then choose the tool that is right for you. Of course, other BI tools such as Power BI and Qlikview also have their own advantages. From Google.

Knowledge Discovery 98

Knowledge Discovery Visualization Experimentation Reporting 98

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

by TAMAN NARAYAN & SEN ZHAO A data scientist is often in possession of domain knowledge which she cannot easily apply to the structure of the model. On the one hand, basic statistical models (e.g. On the other hand, sophisticated machine learning models are flexible in their form but not easy to control.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

O'Reilly on Data

FEBRUARY 18, 2020

This year’s growth in Python usage was buoyed by its increasing popularity among data scientists and machine learning (ML) and artificial intelligence (AI) engineers. Growth is still strong for such a large topic, but usage slowed in 2018 (+13%) and cooled significantly in 2019, growing by just 7%.

Data-driven 335

Data-driven Software Statistics Marketing 335

Sisense

DECEMBER 3, 2019

The merger of Periscope Data in May 2019 brings robust functionality for cloud data experts to work with their data as needed while supporting a wide breadth of users and use cases. With a single click, an ensemble of univariate forecast models run against your data. Unleash the power of advanced analytics. Talk to your data.

Data Warehouse 90

Data Warehouse Analytics Digital Transformation Forecasting 90

The Unofficial Google Data Science Blog

APRIL 23, 2024

If $Y$ at that point is (statistically and practically) significantly better than our current operating point, and that point is deemed acceptable, we update the system parameters to this better value. Figure 2: Spreading measurements out makes estimates of model (slope of line) more accurate. And sometimes even if it is not[1].)

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Machine learning model interpretability.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

AUGUST 8, 2019

Meanwhile, many organizations also struggle with “late in the pipeline issues” on model deployment in production and related compliance. Sci Foo 2019. Latest in machine learning research centers rapidly expanding in Africa? For example, meeting Carole Goble was one of the top highlights of Sci Foo 2019 for me.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

datapine

NOVEMBER 19, 2019

While we’ve seen traces of this in 2019, it’s in 2020 that computer vision will make a significant mark in both the consumer and business world. Already in our shortlist of tech buzzwords 2019, artificial intelligence is on the front scene for next year again. Artificial Intelligence (AI). Connected Retail.

Technology 279

Technology Internet of Things IT IoT 279

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

In conferences and research publications, there is a lot of excitement these days about machine learning methods and forecast automation that can scale across many time series. Nor can we learn prediction intervals across a large set of parallel time series, since we are trying to generate intervals for a single global time series.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

Corinium

APRIL 25, 2019

For example auto insurance companies offering to capture real-time driving statistics from policy-holders’ cars to encourage and reward safe driving. This is a much more proactive and scalable model. What are you most looking forward to about CDAOI Insurance 2019? The 2nd biggest mistake, I’ll call it “silo mentality”.

Insurance 150

Insurance Risk IoT Cost-Benefit 150

Domino Data Lab

MARCH 3, 2019

Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise. O’Reilly Media published our analysis as free mini-books: The State of Machine Learning Adoption in the Enterprise (Aug 2018). What’s been the impact of using ML models on culture and organization?

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Smart Data Collective

OCTOBER 14, 2019

Ludwig is a tool that allows people to build data-based deep learning models to make predictions. Ludwig is a TensorFlow-based toolbox that aims to allow people to use machine learning during their data work without having extensive prior knowledge. Here are some open-source options to consider. Apache Drill.

Data Science 69

Data Science Machine Learning Big Data Interactive 69

Insight

FEBRUARY 19, 2020

Using variability in machine learning predictions as a proxy for risk can help studio executives and producers decide whether or not to green light a film project Photo by Kyle Smith on Unsplash Originally posted on Toward Data Science. In 2019, Netflix alone released 371 new TV shows and movies.

Risk 67

Risk ROI Modeling Metrics 67

Sisense

JUNE 30, 2020

The primary objective of data visualization is to clearly communicate what the data says, help explain trends and statistics, and show patterns that would otherwise be impossible to see. When BI and analytics users want to see analytics results, and learn from them quickly, they rely on data visualizations.

Visualization 93

Visualization Analytics Dashboards Data-driven 93

O'Reilly on Data

MAY 21, 2019

Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms. I will highlight the results of a recent survey on machine learning adoption, and along the way describe recent trends in data and machine learning (ML) within companies.

Machine Learning 238

Machine Learning Technology Deep Learning Data Science 238

Sisense

DECEMBER 11, 2019

It’s why Sisense, having merged with Periscope Data in May 2019, chose to host this event in Tel Aviv. And he demonstrated how the Periscope Data platform overcomes the challenges of huge data volumes that can’t be easily modeled by traditional BI. What VCs want from startups. Kyle Dempsey, Senior Sales Engineer, Sisense.

Data Lake 80

Data Lake Big Data Sales Data-driven 80

Smart Data Collective

JUNE 26, 2019

Get Rid of Blind Spots in Statistical Models With Machine Learning. Data-related blind spots could also exist in your statistical models. It also found that applying machine learning in this way can prevent the model’s accuracy from degrading over time.

ROI 102

ROI Statistics Machine Learning Measurement 102

datapine

NOVEMBER 27, 2019

2019 was a particularly major year for the business intelligence industry. While we work on programs to avoid such inconvenience , AI and machine learning are revolutionizing the way we interact with our analytics and data management while increment in security measures must be taken into account.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

O'Reilly on Data

JANUARY 25, 2021

Year-over-year (YOY) growth compares January through September 2020 with the same months of 2019. Let’s look at the data, starting at the highest level: O’Reilly online learning itself. O’Reilly Online Learning. Usage of O’Reilly online learning grew steadily in 2020, with 24% growth since 2019.

Machine Learning 363

Machine Learning Software Testing Technology 363

datapine

DECEMBER 4, 2019

Without a doubt, it’s a big technological advancement, and one of the big statistics buzzwords, but the extent to which it is believed to be already applied is vastly exaggerated. The accuracy of the predictions depends on the data used to create the model. The commercial use of predictive analytics is a relatively new thing.

Business Intelligence 212

Business Intelligence Prescriptive Analytics Analytics Predictive Analytics 212

Andrew White

JANUARY 11, 2021

Where does the Data Architect role fits in the Operational Model ? Assuming a data architect helps model and guide and assist D&A then they play a key role. The work of data science is more tied to machine learning and so AI and those projects do not focus only on analysis but also automation. Try some gamification?

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52

Jet Global

MAY 1, 2023

These licensing terms are critical: Perpetual license vs subscription: Subscription is a pay-as-you-go model that provides flexibility as you evaluate a vendor. Pricing model: The pricing scale is dependent on several factors. It is organized to create a top-down model that is used for analysis and reporting.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52