Smart Data Collective

NOVEMBER 26, 2021

With the “big data” or insurmountable, high-volume amount of information, data analytics plays a crucial role in many business aspects, including revenue marketing. Data analytics refers to the systematic computational analysis of statistics or data. It lays a core foundation necessary for business planning.

Marketing 111

Marketing Data Analytics Key Performance Indicator Analytics 111

datapine

MARCH 31, 2022

According to the US Bureau of Labor Statistics, demand for qualified business intelligence analysts and managers is expected to soar to 14% by 2026, with the overall need for data professionals to climb to 28% by the same year. A background in (or a firm grasp of) data warehousing and mining.

Business Intelligence 239

Business Intelligence Statistics Visualization Data-driven 239

Smarten

APRIL 12, 2023

Smarten CEO, Kartik Patel says, ‘Smarten SnapShot supports the evolving role of Citizen Data Scientists with interactive tools that allow a business user to gather information, establish metrics and key performance indicators.’

Snapshot 52

Snapshot Key Performance Indicator KPI Business Intelligence 52

The Unofficial Google Data Science Blog

OCTOBER 31, 2016

By PATRICK RILEY For a number of years, I led the data science team for Google Search logs. We were often asked to make sense of confusing results, measure new phenomena from logged behavior, validate analyses done by others, and interpret metrics of user behavior. Something like histograms, CDFs, Q-Q plots, etc.

Metrics 107

Metrics Measurement Statistics Data Collection 107

Business Over Broadway

MARCH 13, 2019

IBM is using the power of its Watson Studio platform to extend the power of AI to people who fall outside the realm of data science, machine learning and AI experts. IBM Watson Studio is an end-to-end analytics solution to help you gain insights from your data. To view the statistics, click on the Statistics Node and hit run.

Statistics 78

Statistics Measurement Descriptive Analytics Machine Learning 78

Domino Data Lab

DECEMBER 4, 2020

Approximating the region under the graph of as a series of trapezoids and calculating the sum of their area (in the case of non-uniformly distributed data points) is given by. Having calculated AUC/AUMC, we can further derive a number of useful metrics like: Total clearance of the drug from plasma. Mean residence time. pain_df.TIME.==

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

Sisense

SEPTEMBER 3, 2020

Data scientists usually build models for data-driven decisions asking challenging questions that only complex calculations can try to answer and creating new solutions where necessary. Programming and statistics are two fundamental technical skills for data analysts, as well as data wrangling and data visualization.

Statistics 97

Statistics Metrics Visualization Finance 97

The Unofficial Google Data Science Blog

DECEMBER 14, 2021

by AMIR NAJMI & MUKUND SUNDARARAJAN Data science is about decision making under uncertainty. Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature.

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

O'Reilly on Data

DECEMBER 12, 2019

Residuals are a numeric measurement of model errors, essentially the difference between the model’s prediction and the known true outcome. There are several known attacks against machine learning models that can lead to altered, harmful model outcomes or to exposure of sensitive training data. [8] Residual analysis.

Machine Learning 198

Machine Learning Modeling Testing Risk Management 198

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. Because individual observations have so little information, statistical significance remains important to assess. We must therefore maintain statistical rigor in quantifying experimental uncertainty.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

IBM Big Data Hub

AUGUST 7, 2023

Therefore, IBM observes that more clients tend to consult AI leaders to help establish governance and enhance AI and data science capabilities, an operating model in the form of co-delivery partnerships. A mitigation plan facilitates trial continuity by providing contingency measures and alternative strategies.

Optimization 55

Optimization Forecasting Data-driven Strategy 55

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. So how do we get totally different results when breaking the data down by gender? But when the data is combined, our conclusion reverses and it seems like ginger is preferable.

Testing 104

Testing Risk Data-driven Statistics 104

The Unofficial Google Data Science Blog

JULY 18, 2023

E ven after we account for disagreement, human ratings may not measure exactly what we want to measure. Overview Human-labeled data is ubiquitous in business and science, and platforms for obtaining data from people have become increasingly common. And for thousands of years, measurement was as simple as this.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

The Unofficial Google Data Science Blog

SEPTEMBER 23, 2015

by KAY BRODERSEN For the first time in the history of statistics, recent innovations in big data might allow us to estimate fine-grained causal effects, automatically and at scale. How can we establish that there is a causal link between our idea and the outcome metric we care about? Is a randomized experiment possible?

Advertising 47

Advertising Statistics Big Data Modeling 47

The Unofficial Google Data Science Blog

APRIL 23, 2024

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Insight

MARCH 12, 2020

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. The most powerful approach for the first task is to use a ‘language model’ (LM), i.e. a statistical model of natural language. features) and metadata (i.e.

Modeling 59

Modeling Metadata Publishing Sales 59

Business Over Broadway

SEPTEMBER 27, 2018

In this paper, I show you how marketers can improve their customer retention efforts by 1) integrating disparate data silos and 2) employing machine learning predictive analytics. Your marketing strategy is only as good as your ability to deliver measurable results. underspecified) due to omitted metrics.

Machine Learning 76

Machine Learning Data Integration Marketing Predictive Modeling 76

CIO Business Intelligence

MAY 27, 2024

CISOs are increasingly anxious because while they realize the ax will fall on them when the inevitable breach occurs, securing boardroom support for heavy investment in preventative measures, like training, is challenging in a world where revenue is demanded for each dollar spent. “The Here are some key strategies: 1.

Cost-Benefit 103

Cost-Benefit Statistics ROI Measurement 103

Domino Data Lab

JULY 17, 2021

The likelihood function measures the goodness of fit of a statistical model to a sample of data for given values of the unknown parameters. The test set is used to evaluate model performance metrics. The post Building a Speaker Recognition Model appeared first on Data Science Blog by Domino. 2] [link]. [3]

Modeling 64

Modeling Testing Machine Learning Measurement 64

The Unofficial Google Data Science Blog

JANUARY 16, 2018

Experimentation on networks A/B testing is a standard method of measuring the effect of changes by randomizing samples into different treatment groups. With A/B testing, we can validate various hypotheses and measure the impact of our product changes, allowing us to make better decisions. This could create confusion.

Testing 58

Testing Experimentation Measurement Modeling 58

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

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. With those stakes and the long forecast horizon, we do not rely on a single statistical model based on historical trends. Our team does a lot of forecasting.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

The Unofficial Google Data Science Blog

JUNE 30, 2016

A naïve comparison of the exposed and unexposed groups would produce an overly optimistic measurement of the effect of the ad, since the exposed group has a higher baseline likelihood of purchasing a pickup truck. Identification We now discuss formally the statistical problem of causal inference. we drop the $i$ index.

Statistics 44

Statistics Optimization Modeling Experimentation 44

Peter James Thomas

AUGUST 12, 2018

This piece was prompted by both Olaf’s question and a recent article by my friend Neil Raden on his Silicon Angle blog, Performance management: Can you really manage what you measure? Data may be perfectly valid, but still not represent reality. It is hard to account for such tweaking in measurement systems.

Metrics 49

Metrics Statistics Data Quality Measurement 49

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

DataRobot Blog

APRIL 4, 2022

MMM stands for Marketing Mix Model and it is one of the oldest and most well-established techniques to measure the sales impact of marketing activity statistically. Data Requirements. As with any type of statistical model, data is key and GIGO (“Garbage In, Garbage Out”) principle definitely applies. What is MMM?

Machine Learning 52

Machine Learning Sales Measurement ROI 52

datapine

AUGUST 29, 2022

Whether you are a complete novice or a seasoned BI professional, you will find here some books on data analytics that will help you cultivate your understanding of this essential field. Before we delve deeper into the best books for data analytics, here are three big data insights to put their relevance and importance into perspective.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

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.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

OCTOBER 9, 2019

Clustering is a machine learning technique that enables researchers and data scientists to partition and segment data. Segmenting data into appropriate groups is a core task when conducting exploratory analysis. It divides the observations into discrete groups based on some distance metric. > wineUrl <- '[link].

Statistics 100

Statistics Machine Learning Metrics Measurement 100

Andrew White

JANUARY 9, 2022

First, how we measure emissions and carbon footprint is about data design and policy. In other words, D&A plays a key role in the foundational measuring angle. – We did some early work a few years ago that look at the career path of a CDO – see from 2016 Build Your Career Path to the Chief Data Officer Role.

Analytics 64

Analytics Measurement Modeling Data-driven 64

Smart Data Collective

JANUARY 31, 2022

a website design company, both quantitative and qualitative data should be utilized during a website design or redesign. Quantitative data provides the statistics of an event on a website in number, while qualitative data highlights a number of reasons the event might take place. Elements of data-driven web design.

Data-driven 102

Data-driven Big Data Statistics Optimization 102

The Unofficial Google Data Science Blog

MAY 31, 2016

It is important that we can measure the effect of these offline conversions as well. Panel studies make it possible to measure user behavior along with the exposure to ads and other online elements. Wouldn't it be great if we didn't require individual data to estimate an aggregate effect? days or weeks).

Advertising 42

Advertising Testing Sales Statistics 42

Domino Data Lab

JUNE 23, 2019

Companies with successful ML projects are often companies that already have an experimental culture in place as well as analytics that enable them to learn from data. Ensure that product managers work on projects that matter to the business and/or are aligned to strategic company metrics. That’s another pattern.

Management 110

Management Machine Learning Experimentation Metrics 110

O'Reilly on Data

OCTOBER 11, 2022

Over the past decade, one problem with data science and its successors has been the assumption that all you need is data, and lots of it; analyzing that data will lead you to new products, new processes, new strategies: just follow the data and let it transform your business. Decide where data fits in.

Data Integration 300

Data Integration Insurance Dashboards Modeling 300

Domino Data Lab

JULY 1, 2021

statistical model-based techniques – Using Machine Learning we can streamline and simplify the process of building NER models, because this approach does not need a predefined exhaustive set of naming rules. The process of statistical learning can automatically extract said rules from a training dataset. The CRF model.

Modeling 111

Modeling Statistics Testing Metrics 111

Domino Data Lab

OCTOBER 23, 2019

As a result, there has been a recent explosion in individual statistics that try to measure a player’s impact. Eighty percent of this problem is collecting the data and then transforming the data. The other 20 percent is ML- and data science–related tasks like finding the right model, doing EDA, and feature engineering.

Statistics 59

Statistics Machine Learning Testing Modeling 59

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. Hollywood is a $10 billion-a-year industry, and movies range from huge hits to box office bombs.

Risk 67

Risk ROI Modeling Metrics 67