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. The above example (clustering) is taken from unsupervised machine learning (where there are no labels on the training data).

Machine Learning 203

Machine Learning Metrics Optimization Data Science 203

Domino Data Lab

JULY 22, 2019

Paco Nathan presented, “Data Science, Past & Future” , at Rev. At Rev’s “ Data Science, Past & Future” , Paco Nathan covered contextual insight into some common impactful themes over the decades that also provided a “lens” help data scientists, researchers, and leaders consider the future.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

O'Reilly on Data

JULY 28, 2020

The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded. It sounds simplistic to state that AI product managers should develop and ship products that improve metrics the business cares about. Agreeing on metrics.

Marketing 363

Marketing Experimentation Metrics Testing 363

DataRobot

FEBRUARY 10, 2021

Even if all the code runs and the model seems to be spitting out reasonable answers, it’s possible for a model to encode fundamental data science mistakes that invalidate its results. As a data scientist, one of my passions is to reproduce research papers as a learning exercise. See the source for this graphic.).

Modeling 98

Modeling Deep Learning Machine Learning Testing 98

Smart Data Collective

NOVEMBER 26, 2021

Data analytics refers to the systematic computational analysis of statistics or data. Data analytics make up the relevant key performance indicators ( KPIs ) or metrics necessary for a business to create various sales and marketing strategies. When companies use data analytics, the processing of data is lightning fast.

Marketing 111

Marketing Data Analytics Key Performance Indicator Analytics 111

Juice Analytics

FEBRUARY 10, 2020

If a data culture was something you could purchase, the companies answering these surveys would have done so. Most large organizations are investing heavily in data science, AI, data infrastructure, master data management, and analytical tools ( we can save you money there ). Where to next? Modeling behavior.

Data-driven 135

Data-driven Metrics Dashboards Data Science 135

Data Science 101

OCTOBER 31, 2019

This data alone does not make any sense unless it’s identified to be related in some pattern. Data mining is the process of discovering these patterns among the data and is therefore also known as Knowledge Discovery from Data (KDD). The choice of these metrics depends on the nature of the problem.

Data mining 77

Data mining KDD Data Science Forecasting 77

O'Reilly on Data

NOVEMBER 13, 2018

Below are the top search topics on our training platform: Beyond “search,” note that we’re seeing strong growth in consumption of content related to ML across all formats—books, posts, video, and training. Data Platforms. There are also many important considerations that go beyond optimizing a statistical or quantitative metric.

Machine Learning 224

Machine Learning Risk Management Statistics 224

Domino Data Lab

JUNE 2, 2019

Co-chair Paco Nathan provides highlights of Rev 2 , a data science leaders summit. We held Rev 2 May 23-24 in NYC, as the place where “data science leaders and their teams come to learn from each other.” If you lead a data science team/org, DM me and I’ll send you an invite to data-head.slack.com ”.

Data-driven 110

Data-driven Data Science Machine Learning Modeling 110

Sisense

APRIL 10, 2020

One of the most-asked questions from aspiring data scientists is: “What is the best language for data science? People looking into data science languages are usually confused about which language they should learn first: R or Python. NLP can be used on written text or speech data. R or Python?”.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

Cloudera

MAY 2, 2019

The theme that I’ve heard emerge is that big data and data science are domains in which most of us were never trained in school. Read Hilary’s book on this topic: Ethics and Data Science. Violeta spoke about the importance of metrics and KPIs. Call to action.

Big Data 93

Big Data Digital Transformation Data Science Data-driven 93

Insight

MARCH 12, 2020

There was only one problem: literary agents, the gatekeepers of the publishing industry, kept rejecting the book?—?often With breaking this bottleneck in mind, I’ve used my time as an Insight Data Science Fellow to build the AIgent, a web-based neural net to connect writers to representation. often without even looking at it.

Modeling 59

Modeling Metadata Publishing Sales 59

Domino Data Lab

AUGUST 22, 2019

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. Utilizing NLP helps researchers and data scientists complete core tasks faster. and 2.6) [ in the book]. Introduction.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

Domino Data Lab

FEBRUARY 4, 2019

In Paco Nathan ‘s latest column, he explores the role of curiosity in data science work as well as Rev 2 , an upcoming summit for data science leaders. Welcome back to our monthly series about data science. and dig into details about where science meets rhetoric in data science.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. back to the structure of the dataset. Let’s look through some antidotes. Ergo, less interpretable.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

datapine

OCTOBER 21, 2020

Summing up the product of all this work, the data science team developed a web-based user interface that forecasts patient loads and helps in planning resource allocation by utilizing online data visualization that reaches the goal of improving the overall patients’ care. 2) Electronic Health Records (EHRs).

Big Data 364

Big Data Data Analytics Analytics Cost-Benefit 364

datapine

MARCH 31, 2022

To help you with your studies, you can start here with a list of the best SQL books that will help you take your skills to the next level. Data Analysis : Most BI skills and intelligence analyst-related skills are about using data to make better decisions. A background in (or a firm grasp of) data warehousing and mining.

Business Intelligence 239

Business Intelligence Statistics Visualization Data-driven 239

Domino Data Lab

AUGUST 26, 2019

This article provides an excerpt of “Tuning Hyperparameters and Pipelines” from the book, Machine Learning with Python for Everyone by Mark E. Data scientists, machine learning (ML) researchers, and business stakeholders have a high-stakes investment in the predictive accuracy of models. in the book. ]. Introduction.

Testing 79

Testing Modeling Machine Learning Metrics 79

The Unofficial Google Data Science Blog

AUGUST 27, 2019

Of course, any mistakes by the reviewers would propagate to the accuracy of the metrics, and the metrics calculation should take into account human errors. If we could separate bad videos from good videos perfectly, we could simply calculate the metrics directly without sampling. The missing verdicts create two problems.

Metrics 98

Metrics Statistics Uncertainty Optimization 98

Domino Data Lab

AUGUST 29, 2019

This article provides an excerpt “Deep Reinforcement Learning” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. In Chapter 4 [ in the book ], we introduced the paradigm of reinforcement learning (as distinct from supervised and unsupervised learning), in which an agent (e.g., Defining the agent.

Optimization 111

Optimization Modeling Deep Learning Metrics 111

Sisense

JANUARY 21, 2020

One of the simplest ways to start exploring your data is to aggregate the metrics you are interested in by their relevant dimensions. In general, it is not possible to give a rule of thumb about when data should be partitioned or combined. As an example, I’ll present a case from The Book of Why by Judea Pearl.

Testing 104

Testing Risk Data-driven Statistics 104

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. Welcome back to our monthly series about data science! This month, the theme is not specifically about conference summaries; rather, it’s about a set of follow-up surveys from Strata Data attendees.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

O'Reilly on Data

JANUARY 25, 2021

The data includes all usage of our platform, not just content that O’Reilly has published, and certainly not just books. More traditional modes also saw increases: usage of books increased by 11%, while videos were up 24%. AI, Machine Learning, and Data. What about data science? Web Development.

Machine Learning 363

Machine Learning Software Testing Technology 363

DataKitchen

NOVEMBER 18, 2021

Imagine a data pipeline error or data problem that impacts critical analytics. Most organizations find out about these errors from their customers, such as a VP of Sales who notices that the bookings report is millions of dollars off. This oversight triggers an all-hands-on-deck emergency response.

Testing 246

Testing Data Governance Measurement Software 246

Jet Global

MAY 9, 2024

Data pipelines are designed to automate the flow of data, enabling efficient and reliable data movement for various purposes, such as data analytics, reporting, or integration with other systems. It also includes data validation and quality checks to ensure the accuracy and integrity of the data being processed.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

Domino Data Lab

DECEMBER 2, 2020

When we use the standard Euclidean distance metric, sometimes called the l 2 metric, our neighborhoods display this spherical shape. I am using points in 2D space for simplicity, but these distance metrics can be generalized to higher-dimensional spaces. Why does this happen? Conclusion.

Metrics 116

Metrics KDD Testing Machine Learning 116

The Unofficial Google Data Science Blog

JANUARY 14, 2016

by AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of data science. In this post we explore how and why we can be “ data-rich but information-poor ”. There are many reasons for the recent explosion of data and the resulting rise of data science.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

DataKitchen

MAY 10, 2021

And the worst part – data errors take the fun out of data science. Remember your first data science courses? You probably imagined your career would be about helping drive insights with data instead of having to sit in endless meetings discussing analytics errors and painstaking corrective actions.

Testing 214

Testing Manufacturing Data Quality Statistics 214

Domino Data Lab

OCTOBER 9, 2019

As Domino seeks to support the acceleration of data science work, including core tasks, Domino reached out to Addison-Wesley Professional (AWP) Pearson for the appropriate permissions to except “Clustering” from the book, R for Everyone: Advanced Analytics and Graphics, Second Edition. > wineUrl <- '[link].

Statistics 100

Statistics Machine Learning Metrics Measurement 100

Jen Stirrup

SEPTEMBER 4, 2021

It may come as a surprise to you that the history of statistics and data science is fraught with racism, sexism, class, race, colonialism and anti-Semitism. Even if you do not have much of a data science background, you may be aware of some of the issues. It’s not just about the numbers, KPIs and metrics.

Statistics 106

Statistics Interactive Data Science Testing 106

Peter James Thomas

AUGUST 12, 2018

There is also (as referenced by Neil in his comments above) the delayed booking of transactions in order to – with the nicest possible description – smooth revenues. Again see Using BI to drive improvements in data quality for further details. Pertinence and fidelity of metrics developed from Data.

Metrics 49

Metrics Statistics Data Quality Measurement 49

Cloudera

APRIL 4, 2019

They then printed all of the photos and created a book of ‘adventures’ the young child’s stuffed animal experienced during the evening and delivered the book to him the next morning. For example, contrary to normal industry accepted call duration metrics, Zappos does not hurry its customers just to be within a call metric.

Insurance 54

Insurance IT Interactive Cost-Benefit 54

Sisense

SEPTEMBER 3, 2019

Think of it like something that houses the metrics used to power daily, weekly, or monthly business KPIs. roll-ups of many rows of data). This results in less joins between the metric data in fact tables, and the dimensions. These combine the dimensions and fact tables and pre-aggregate the metrics to simplify reporting.

Data Warehouse 78

Data Warehouse OLAP Statistics Cost-Benefit 78

Domino Data Lab

OCTOBER 23, 2019

As Domino is committed to supporting data scientists and accelerating research, we reached out to Addison-Wesley Professional (AWP) Pearson for the appropriate permissions to excerpt “Predicting Social-Media Influence in the NBA” from the book, Pragmatic AI: An Introduction to Cloud-Based Machine Learning by Noah Gift. In Figure 6.1,

Statistics 59

Statistics Machine Learning Testing Modeling 59

Andrew White

JANUARY 9, 2022

This research does not tell you where to do the work; it is meant to provide the questions to ask in order to work out where to target the work, spanning reporting/analytics (classic), advanced analytics and data science (lab), data management and infrastructure, and D&A governance. The last item should be updated in 2022.

Analytics 64

Analytics Measurement Modeling Data-driven 64

O'Reilly on Data

OCTOBER 11, 2022

We can see what books and courses our customers are using, and for how long. We know if customers only read the first chapter of some book, and can think about what how to improve it. The data available to our retail business is much more limited. As a result, your relationship to many important financial metrics changes.

Data Integration 300

Data Integration Insurance Dashboards Modeling 300

The Unofficial Google Data Science Blog

JUNE 30, 2016

Under lack of domain-specific scientific knowledge supporting the use of a parametric model, these data-adaptive methods have a better chance of consistently estimating the propensity score. The algorithms in the plot are ordered according to the sum of the respective metrics weighted by the square root of the sample size considered.

Statistics 44

Statistics Optimization Modeling Experimentation 44