Smart Data Collective

SEPTEMBER 19, 2022

Statistics show that 93% of customers will offer repeat business when they encounter a positive customer experience. To ensure their customers have a satisfactory experience, financial businesses will use big data analytics to tweak their services across various platforms to suit a customer’s needs. Better UI/UX based on A/B testing.

Big Data 130

Big Data ROI Measurement Risk 130

O'Reilly on Data

JULY 28, 2020

There’s a substantial literature about ethics, data, and AI, so rather than repeat that discussion, we’ll leave you with a few resources. Ethics and Data Science is a short book that helps developers think through data problems, and includes a checklist that team members should revisit throughout the process.

Marketing 363

Marketing Experimentation Metrics Testing 363

O'Reilly on Data

JUNE 18, 2019

There has been a significant increase in our ability to build complex AI models for predictions, classifications, and various analytics tasks, and there’s an abundance of (fairly easy-to-use) tools that allow data scientists and analysts to provision complex models within days. Error detection and automatic repairing/imputation.

Machine Learning 264

Machine Learning Data Quality Statistics Modeling 264

Smart Data Collective

OCTOBER 7, 2023

In an age dominated by AI technology, the ways in which we interact with the world around us are continuously evolving. Marco Cerliani has a great article in Towards Data Science on the benefits of AI in the people tracing field. This is going to make it a lot easier to find people in the years to come.

Technology 94

Technology Snapshot Data-driven Machine Learning 94

Domino Data Lab

OCTOBER 7, 2020

This could be lots of rows (samples) and few columns (variables) like credit card transaction data, or lots of columns (variables) and few rows (samples) like genomic sequencing in life sciences research. Each variable in a data set is a dimension with the set of variables defining the space in which the samples fall.

Statistics 145

Statistics Testing Predictive Modeling Modeling 145

Smarten

APRIL 12, 2023

SnapShot Monitoring provides powerful data analytical features that reveal trends and anomalies and allow the enterprise to map targets and adapt to changing markets with clear, prescribed actions for continuous improvement.

Snapshot 52

Snapshot Key Performance Indicator KPI Business Intelligence 52

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

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

Domino Data Lab

DECEMBER 4, 2020

This tutorial will show how easy it is to integrate and use Pumas in the Domino Data Science Platform , and we will carry out a simple non-compartmental analysis using a freely available dataset. The Domino data science platform empowers data scientists to develop and deliver models with open access to the tools they love.

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

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

Insight

APRIL 28, 2020

Originally posted on Open Data Science (ODSC). In this article, we share some data-driven advice on how to get started on the right foot with an effective and appropriate screening process. Designing a Data Science Interview Onsite interviews are indispensable, but they are time-consuming. Length: Highly Variable.

Data-driven 53

Data-driven Data Science Experimentation Measurement 53

IBM Big Data Hub

APRIL 18, 2024

Achieving these feats is accomplished through a combination of sophisticated algorithms, natural language processing (NLP) and computer science principles. LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language.

Cost-Benefit 89

Cost-Benefit Modeling Manufacturing Interactive 89

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

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. It's why estimating causal effects has been at the heart of data science. But the analytical challenges are substantial.

Advertising 47

Advertising Statistics Big Data Modeling 47

Domino Data Lab

JUNE 8, 2021

where (boldsymbol{x}{i_p}) is the normal projection of (boldsymbol{x}_i) onto (mathcal{H}), and (gamma_i) is an algebraic measure of the margin (see Duda and Hart, 1973). and they are all available within the Domino Data Science Platform. Granular computing and decision-making: Interactive and iterative approaches (pp.

Optimization 76

Optimization Machine Learning Measurement Data Science 76

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. The more data they ingest, the better they get.

Machine Learning 76

Machine Learning Data Integration Marketing Predictive Modeling 76

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

Alation

SEPTEMBER 28, 2022

DataOps strives to give customers the highest priority through quick and continuous delivery of data insights. In DataOps, data analytics performance is primarily measured through insightful analytics, and accurate data, in robust frameworks. Daily Interactions. Value working analytics. Embrace change.

Cost-Benefit 52

Cost-Benefit Data Quality Manufacturing Testing 52

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., Taking measurements at parameter settings further from control parameter settings leads to a lower variance estimate of the slope of the line relating the metric to the parameter.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

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

O'Reilly on Data

JULY 15, 2019

We should clarify that SR 11-7 also covers models that aren’t necessarily based on machine learning: "quantitative method, system, or approach that applies statistical, economic, financial, or mathematical theories, techniques, and assumptions to process input data into quantitative estimates." Sources of model risk.

Machine Learning 38

Machine Learning Management Enterprise Risk Management 38

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

The Unofficial Google Data Science Blog

DECEMBER 4, 2015

In other cases, features such as standard libraries and a language’s community of users are more important — the main reason that R is a great language for statistics is that it’s widely used by statisticians and has a huge variety of useful statistics libraries. counts as spam. var stats = table.

Statistics 54

Statistics Interactive Software Modeling 54

Domino Data Lab

NOVEMBER 17, 2020

High throughput screening technologies have been developed to measure all the molecules of interest in a sample in a single experiment (e.g., It is hoped this data will give researchers a better understanding of what they are studying. Points fall on the outer edges of the data distribution. Introduction. Antimicrobial.

Statistics 131

Statistics Measurement Testing Predictive Modeling 131

Domino Data Lab

AUGUST 12, 2021

In a previous blog , we have covered how Pandas Profiling can supercharge the data exploration required to bring our data into a predictive modelling phase. We covered the importance of Exploratory Analytics including observing the frequency of missing data and computing interactions of all features. Pandas Profiler.

Machine Learning 126

Machine Learning Reporting Statistics Visualization 126

Domino Data Lab

AUGUST 8, 2019

Paco Nathan ‘s latest monthly article covers Sci Foo as well as why data science leaders should rethink hiring and training priorities for their data science teams. In this episode I’ll cover themes from Sci Foo and important takeaways that data science teams should be tracking. Introduction.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

Insight

AUGUST 5, 2020

While most of these signals are implicitly communicated during human-to-human interaction, we do not have a method for quantifying feeling and mood through individual behavioral signals expressed on the digital platform. Summary statistics (i.e. in predictions through gender-specific models over the gender-nonspecific prediction model.

Predictive Modeling 98

Predictive Modeling Statistics Modeling Measurement 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

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

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. million.

Metrics 49

Metrics Statistics Data Quality Measurement 49

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

Cloudera

NOVEMBER 13, 2020

Data science experiment result and performance analysis, for example, calculating model lift. Exhaustive cost-based query planning depends on having up to date and reliable statistics which are expensive to generate and even harder to maintain, making their existence unrealistic in real workloads. More on this below.

Optimization 143

Optimization Metadata Statistics Cost-Benefit 143

Smart Data Collective

OCTOBER 17, 2022

In fact, Statista predicts that by 2025, the world will have produced slightly more than 180 zettabytes of data. Consider the statistics from Domo that the number of home-based workers has increased from roughly 15% 18 months ago to more than 50% now (it was close to 100% at times during the epidemic).

Big Data 104

Big Data Data-driven Recreation/Entertainment Data Governance 104

datapine

MAY 16, 2019

A sobering statistic if ever we saw one. By working with relevant key performance indicators (KPIs) and data dashboards , you’ll be able to track, monitor, and measure your most valuable business insights in a way that is clear, concise, and digestible, pulling from past, present, and predictive data.

Analytics 163

Analytics Predictive Analytics Advertising Dashboards 163

The Unofficial Google Data Science Blog

JANUARY 31, 2017

By OMKAR MURALIDHARAN, NIALL CARDIN, TODD PHILLIPS, AMIR NAJMI Given recent advances and interest in machine learning, those of us with traditional statistical training have had occasion to ponder the similarities and differences between the fields. Are there counterfactual predictions we should avoid in decision making?

Machine Learning 73

Machine Learning Modeling Statistics Predictive Modeling 73

datapine

APRIL 11, 2019

Download our 10-step checklist and see how to tell the best data story. Rather than listing facts, figures, and statistics alone, people used gripping, imaginative timelines, bestowing raw data with real context and interpretation. Create an interactive dialogue. However, do not rely just on emotions to make your point.

Dashboards 169

Dashboards Visualization Data-driven Key Performance Indicator 169

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