CIO Business Intelligence

JUNE 14, 2023

Getting the technology right can be challenging but building the right team with the right skills to undertake data initiatives can be even harder — a challenge reflected in the rising demand for big data and analytics skills and certifications. The number of data analytics certs is expanding rapidly.

Big Data 126

Big Data Data Analytics Analytics Predictive Modeling 126

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

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

O'Reilly on Data

JULY 28, 2020

Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. There’s a substantial literature about ethics, data, and AI, so rather than repeat that discussion, we’ll leave you with a few resources.

Marketing 362

Marketing Experimentation Metrics Testing 362

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.

Statistics 131

Statistics Measurement Testing Predictive Modeling 131

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

IBM Big Data Hub

NOVEMBER 29, 2023

They are already identifying and exploring several real-life use cases for synthetic data, such as: Generating synthetic tabular data to increase sample size and edge cases. You can combine this data with real datasets to improve AI model training and predictive accuracy.

Metrics 88

Metrics Machine Learning Risk Statistics 88

Data Science 101

JANUARY 15, 2020

Researchers/ scientists perform experiments to validate their hypothesis/ statements or to test a new product. Suppose we want to test the effectiveness of a new drug against a particular disease. Reliability: It means measurements should have repeatable results. For eg: you measure the blood pressure of a person.

Experimentation 96

Experimentation Statistics Measurement Testing 96

Smarten

MAY 29, 2023

World-renowned technology analysis firm Gartner defines the role this way, ‘A citizen data scientist is a person who creates or generates models that leverage predictive or prescriptive analytics, but whose primary job function is outside of the field of statistics and analytics. ‘If

Prescriptive Analytics 52

Prescriptive Analytics Predictive Modeling Digital Transformation Business Objectives 52

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 129

Big Data ROI Measurement Risk 129

Smarten

DECEMBER 3, 2023

‘Augmented analytics is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms. What is self-service analytics? We should probably explain before we move on.

Key Performance Indicator 52

Key Performance Indicator Analytics IT Predictive Analytics 52

bridgei2i

JANUARY 25, 2022

The Basic Guide to Data Science at the Workplace. The Basic Guide to Data Science at the Workplace. In this podcast episode, AI enthusiasts Anirudh and Janci talk about how sincere curiosity and passion for learning can help anyone understand data science and become an active contributor. Subscribe Now.

Data Science 52

Data Science Machine Learning Data-driven Statistics 52

The Unofficial Google Data Science Blog

JANUARY 16, 2018

We present data from Google Cloud Platform (GCP) as an example of how we use A/B testing when users are connected. Experimentation on networks A/B testing is a standard method of measuring the effect of changes by randomizing samples into different treatment groups. This could create confusion.

Testing 58

Testing Experimentation Measurement Modeling 58

O'Reilly on Data

MAY 18, 2020

And last is the probabilistic nature of statistics and machine learning (ML). Most AI models decay overtime: This phenomenon, known more widely as model decay , refers to the declining quality of AI system results over time, as patterns in new data drift away from patterns learned in training data. How Material Is the Threat?

Risk 359

Risk Modeling Data Processing Software 359

Smarten

JUNE 17, 2022

After completion of the testing procedure, the certificate is provided to show that all requirements were met. As a certified CERT-IN service and product provider, Smarten adds additional security assurances to its already rich foundation of security measures and methodologies to support clients, partners and stakeholders.

Analytics 67

Analytics IT Business Intelligence Predictive Modeling 67

O'Reilly on Data

DECEMBER 12, 2019

In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. It’s a very simple and powerful idea: simulate data that you find interesting and see what a model predicts for that data. 6] Debugging may focus on a variety of failure modes (i.e.,

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

datapine

AUGUST 29, 2022

Best for : the new intern who has no idea what data science even means. An excerpt from a rave review : “I would definitely recommend this book to everyone interested in learning about data from scratch and would say it is the finest resource available among all other Big Data Analytics books.”.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

CIO Business Intelligence

APRIL 14, 2022

Low code and no code options are making it easier for anyone to click a few buttons and produce a report or download a spreadsheet loaded with data, all without setting up an endless series of meetings with the developers. The companies also emphasize their connections to good artificial intelligence algorithms and data science options.

Enterprise 129

Enterprise Marketing Measurement Reporting 129

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

Data Science and Beyond

JANUARY 7, 2019

Bootstrap sampling techniques are very appealing, as they don’t require knowing much about statistics and opaque formulas. Instead, all one needs to do is resample the given data many times, and calculate the desired statistics. However, seeming large isn’t enough. Don’t use 1,000.

Statistics 77

Statistics Uncertainty Testing Modeling 77

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. Pearson, however, was a vocal supporter of eugenics.

Statistics 106

Statistics Interactive Data Science Testing 106

Smarten

APRIL 12, 2023

About Smarten The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist.

Snapshot 52

Snapshot Key Performance Indicator KPI Business Intelligence 52

datapine

APRIL 16, 2019

By implementing the right reporting tools and understanding how to analyze as well as to measure your data accurately, you will be able to make the kind of data driven decisions that will drive your business forward. What Is Data Driven Decision Making? Quantitative data analysis focuses on numbers and statistics.

Data-driven 287

Data-driven Dashboards Visualization Cost-Benefit 287

Rocket-Powered Data Science

SEPTEMBER 18, 2018

In “big data language”, we are talking about one of the 3 V’s of big data: big data Variety! lack of appetite; or other pains) and may order other medical tests (blood pressure checks, or other lab results).

Big Data 103

Big Data Data Science Measurement Data Collection 103

Sisense

JANUARY 21, 2020

So how do we get totally different results when breaking the data down by gender? This is an example of Simpon’s paradox , a statistical phenomenon in which a trend that is present when data is put into groups reverses or disappears when the data is combined. It’s time to introduce a new statistical term.

Testing 104

Testing Risk Data-driven Statistics 104

CIO Business Intelligence

AUGUST 24, 2023

Many enterprises are deploying AI in lower-risk use cases first, says Kjell Carlsson, head of data science strategy and evangelism at Domino Data Lab. Retraining, deployment, operations, testing—a lot of these features just aren’t available yet.” How was this data obtained? AI is a black box.

Uncertainty 97

Uncertainty Risk Testing Enterprise 97

Insight

AUGUST 5, 2020

Speech recordings are time-series data, so these features were computed across several overlapping time windows capturing the temporal changes in their values. Summary statistics (i.e. To prevent data-leakage issues, actors in the training dataset did not reappear in the test datasets. the Mel-frequency cepstrum).

Predictive Modeling 98

Predictive Modeling Statistics Modeling Measurement 98

Domino Data Lab

JULY 1, 2020

Being able to share your work aids knowledge transfer and collaboration which in turn builds more success in data science practices. It also has some neat features around showing you exact spots in your code where violations have been detected, plus it can compute statistics on different violation types.

Testing 116

Testing Data Science Machine Learning Data-driven 116

The Unofficial Google Data Science Blog

JULY 22, 2020

For example, imagine a fantasy football site is considering displaying advanced player statistics. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. We offer two examples where this may be the case.

Experimentation 95

Experimentation Statistics Testing Strategy 95

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

Jen Stirrup

JUNE 16, 2020

Convincing Your HiPPo: Business Statistics for AI and Data Science ( Wednesday 15th July at 12.30pm EST, 5.30pm BST ). To be successful and confident for working AI and Data Science, it’s important to have a grasp of statistics. Data Visualisation Sessions. Register for the DP100 Exam Cram.

Statistics 64

Statistics Visualization Machine Learning Business Intelligence 64

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

Data Science and Beyond

OCTOBER 15, 2017

Do you know what data science is? Do you understand what data scientists do? Just so we’re on the same page, what is data science? Here are my thoughts from 2014 on defining data science as the intersection of software engineering and statistics , and a more recent post on defining data science in 2018.

Data Science 71

Data Science Deep Learning Machine Learning Data-driven 71

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

Domino Data Lab

OCTOBER 2, 2019

This article covers causal relationships and includes a chapter excerpt from the book Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications by Andrew Kelleher and Adam Kelleher. The case that might be familiar to you is an AB test. A complementary Domino project is available. .

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

AWS Big Data

NOVEMBER 8, 2023

This post was co-written with Rajiv Arora, Director of Data Science Platform at Gilead Life Sciences. Gilead Sciences, Inc. Amazon Redshift Serverless is a fully managed cloud data warehouse that allows you to seamlessly create your data warehouse with no infrastructure management required.

Data Lake 74

Data Lake Data Warehouse Cost-Benefit Optimization 74

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. Later during verification, an i-Vector extracted from a test utterance (about 15 second long) is compared against the enrolled i-Vector via a cosine similarity score.

Modeling 64

Modeling Testing Machine Learning Measurement 64