Smart Data Collective

JUNE 8, 2023

many of our articles have centered around the role that data analytics and artificial intelligence has played in the financial sector. In this article, we will explore the crucial role that data collection plays in analyzing football performance, unlocking valuable insights that can shape the future of the sport.

Data Analytics 83

Data Analytics Analytics Data Collection Statistics 83

Smart Data Collective

DECEMBER 17, 2021

This article will focus on the different stages of this process: Types of Data, Data Collection, and Data Analysis. The data collection process is an ongoing process that starts with setting goals, defining success metrics , identifying what data needs to be collected, and how it will be gathered. Data is a constant in today’s world.

Marketing Analytics 95

Marketing Analytics Marketing Data-driven Analytics 95

DataRobot Blog

JULY 28, 2022

Rapid advances in machine learning in recent years have begun to lower the technical hurdles to implementing AI, and various companies have begun to actively use machine learning. The accuracy of machine learning models is highly dependent on the quality of the training data. Sensor Data Analysis Examples.

Data Science 72

Data Science Machine Learning Statistics Modeling 72

Smart Data Collective

JULY 20, 2022

Here are some statistics on the importance of AI in marketing : 48% of marketers feel AI makes a greater difference than anything else in affecting their relationship with customers 51% of e-commerce companies use AI to improve the customer experience 64% of B2B marketers use AI to guide their strategy. You can use AI to generate new content.

Marketing 139

Marketing B2B Strategy Statistics 139

Business Over Broadway

MAY 22, 2019

Below is a list of topics, answers and articles in support of a recent Tweet Chat in which I was the guest. Not only do you get to share your knowledge and insights to a crowd of interested professionals, you also get to learn from those professionals. The chat (#CXChat) was on customer experience and emerging technologies.

Machine Learning 40

Machine Learning Technology Digital Transformation Data Science 40

Ontotext

JUNE 22, 2023

Recent statistics shed light on the realities in the world of current drug development: out of about 10,000 compounds that undergo clinical research, only 1 emerges successfully as an approved drug. The current process involves costly wet lab experiments, which are often performed multiple times to achieve statistically significant results.

Metrics 52

Metrics Statistics Visualization Metadata 52

Cloudera

DECEMBER 3, 2021

As discussed in this article , model design can also be a source of bias too. There are a number of metrics that can be used to measure the performance of a system; they include accuracy, precision and F-score to name only three. We have already covered a few of those applications in this article so far. Model Drift. Regulation.

Uncertainty 102

Uncertainty Measurement Metrics Risk 102

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. The Bureau of Labor Statistics also states that in 2015, the annual median salary for BI analysts was $81,320.

Business Intelligence 239

Business Intelligence Statistics Visualization Data-driven 239

FineReport

DECEMBER 9, 2021

Untracked metrics: data you will not track. This will be a subset of the metrics you track. Data Scientist: A person who uses analytics, statistical tools, and machine learning to extract actionable value from big data. The chosen example in this article is the wonderful application of FineReport.

Strategy 98

Strategy Business Intelligence KPI Key Performance Indicator 98

Sisense

APRIL 10, 2020

R is a tool built by statisticians mainly for mathematics, statistics, research, and data analysis. We’ll actually do this later in this article. These support a wide array of uses, such as data analysis, manipulation, visualizations, and machine learning (ML) modeling. R: Analytics powerhouse. R libraries.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

IBM Big Data Hub

AUGUST 7, 2023

This article, part of the IBM and Pfizer’s series on the application of AI techniques to improve clinical trial performance, focuses on enrollment and real-time forecasting. AI can also empower trial managers and executives with the data to make strategic decisions.

Optimization 61

Optimization Forecasting Data-driven Strategy 61

Analytics Vidhya

DECEMBER 16, 2020

This article was published as a part of the Data Science Blogathon. Introduction There are so many performance evaluation measures when it comes to. The post Decluttering the performance measures of classification models appeared first on Analytics Vidhya.

Measurement 293

Measurement Modeling Data Science Publishing 293

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. Next, we can explore our data by calculating some descriptive statistics for our measures. To view the statistics, click on the Statistics Node and hit run.

Statistics 78

Statistics Measurement Descriptive Analytics Machine Learning 78

Domino Data Lab

JULY 28, 2021

Data science is a field at the convergence of statistics, computer science and business. In this article, take a deep dive into data science and how Domino’s Enterprise MLOps platform allows you to scale data science in your business. In fact, deep learning was first described theoretically in 1943.

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

datapine

JANUARY 6, 2022

For a more in-depth review of scales of measurement, read our article on data analysis questions. More often than not, it involves the use of statistical modeling such as standard deviation, mean and median. Let’s quickly review the most common statistical terms: Mean: a mean represents a numerical average for a set of responses.

Visualization 246

Visualization Dashboards Cost-Benefit Measurement 246

The Unofficial Google Data Science Blog

DECEMBER 14, 2021

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. Among these, only statistical uncertainty has formal recognition. leaves out.

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

Alation

APRIL 25, 2023

As firms mature their transformation efforts, applying Artificial Intelligence (AI), machine learning (ML) and Natural Language Processing (NLP) to the data is key to putting it into action quickly and effecitvely. Predictive models indicate that the machine learning market will grow at a compound annual growth rate (CAGR) of 38.8%

Data Governance 52

Data Governance Marketing Machine Learning Sales 52

Domino Data Lab

JULY 22, 2019

why data governance, in the context of machine learning is no longer a “dry topic” and how the WSJ’s “global reckoning on data governance” is potentially connected to “premiums on leveraging data science teams for novel business cases”. He was saying this doesn’t belong just in statistics. Tukey did this paper.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Alation

APRIL 25, 2023

As firms mature their transformation efforts, applying Artificial Intelligence (AI), machine learning (ML) and Natural Language Processing (NLP) to the data is key to putting it into action quickly and effecitvely. Predictive models indicate that the machine learning market will grow at a compound annual growth rate (CAGR) of 38.8%

Data Governance 40

Data Governance Marketing Machine Learning Sales 40

Sisense

SEPTEMBER 25, 2020

A data scientist must be skilled in many arts: math and statistics, computer science, and domain knowledge. Statistics and programming go hand in hand. Mastering statistical techniques and knowing how to implement them via a programming language are essential building blocks for advanced analytics. Linear regression.

Statistics 98

Statistics Predictive Modeling Modeling Machine Learning 98

datapine

SEPTEMBER 6, 2022

A content dashboard is an analytical tool that contains critical performance metrics to assess the success of all content-related initiatives. This most insightful of Google Analytics dashboards provide the perfect mix between overview metrics and more detailed insights. Let’s look at some of these metrics in more detail below.

Dashboards 179

Dashboards Reporting ROI Metrics 179

The Unofficial Google Data Science Blog

JUNE 30, 2016

Identification We now discuss formally the statistical problem of causal inference. We start by describing the problem using standard statistical notation. The field of statistical machine learning provides a solution to this problem, allowing exploration of larger spaces. we drop the $i$ index.

Statistics 44

Statistics Optimization Modeling Experimentation 44

AWS Big Data

MARCH 13, 2024

RAG is a machine learning (ML) architecture that uses external documents (like Wikipedia) to augment its knowledge and achieve state-of-the-art results on knowledge-intensive tasks. Each service implements k-nearest neighbor (k-NN) or approximate nearest neighbor (ANN) algorithms and distance metrics to calculate similarity.

Data Processing 116

Data Processing Dashboards Machine Learning Management 116

Smarten

APRIL 19, 2024

In this article, we will discuss the current state of AI in analytics, as well as the future of this burgeoning industry and how it can be applied to analytics to simplify and clarify results and to make analytics easier for businesses and business users to leverage.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

datapine

MARCH 25, 2022

While some experts try to underline that BA focuses, also, on predictive modeling and advanced statistics to evaluate what will happen in the future, BI is more focused on the present moment of data, making the decision based on current insights. But let’s see in more detail what experts say and how can we connect and differentiate the both.

Business Intelligence 264

Business Intelligence Analytics Statistics Dashboards 264

Domino Data Lab

APRIL 21, 2021

In this article, we’ll discuss the challenge organizations face around fraud detection, how machine learning can be used to identify and spot anomalies that the human eye might not catch. from sklearn import metrics. It can be implemented as either unsupervised (e.g. from imblearn.over_sampling import SMOTE.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Domino Data Lab

JUNE 23, 2019

Pete Skomoroch ’s “ Product Management for AI ”session at Rev provided a “crash course” on what product managers and leaders need to know about shipping machine learning (ML) projects and how to navigate key challenges. Be aware that machine learning often involves working on something that isn’t guaranteed to work.

Management 110

Management Machine Learning Experimentation Metrics 110

Insight

MARCH 12, 2020

In this article, I will discuss the construction of the AIgent, from data collection to model assembly. The most powerful approach for the first task is to use a ‘language model’ (LM), i.e. a statistical model of natural language. Ultimately this will mean a more streamlined, inclusive, and personal publishing industry.

Modeling 59

Modeling Metadata Publishing Sales 59

datapine

NOVEMBER 13, 2019

Fortunately, we live in a digital age rife with statistics, data, and insights that give us the power to spot potential issues and inefficiencies within the business. With so many areas to consider, deciding which KPIs to focus on while defining metric measurement periods can prove to be a challenge at the initial stages.

Reporting 158

Reporting KPI Cost-Benefit Metrics 158

Alation

JULY 19, 2022

As they attempt to put machine learning models into production, data science teams encounter many of the same hurdles that plagued data analytics teams in years past: Finding trusted, valuable data is time-consuming. Across the country, data scientists have an unemployment rate of 2% and command an average salary of nearly $100,000.

Metadata 59

Metadata Data Quality Statistics Data Science 59

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. Introduction.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

OCTOBER 9, 2019

This article covers clustering including K-means and hierarchical clustering. Clustering is a machine learning technique that enables researchers and data scientists to partition and segment data. Clustering, which plays a big role in modern machine learning, is the partitioning of data into groups. Introduction.

Statistics 100

Statistics Machine Learning Metrics Measurement 100

The Unofficial Google Data Science Blog

JULY 18, 2023

Once we’ve answered that, we will then define and use metrics to understand the quality of human-labeled data, along with a measurement framework that we call Cross-replication Reliability or xRR. We will follow the example of Janson and Olsson , and start from this generalized definition of the metric, which they call iota.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

IBM Big Data Hub

JULY 28, 2022

Using standard Watson Knowledge Catalog connectivity that’ss seamlessly integrated into IBM Cloud Pak for Data projects, data’s term assignment is powered by machine learning that isn’t constrained to a single global model, and can vary at the project level. Support for BI reporting. Public API.

Metadata 57

Metadata Data Quality Machine Learning Statistics 57

Domino Data Lab

OCTOBER 23, 2019

This article provides insight on the mindset, approach, and tools to consider when solving a real-world ML problem. As a result, there has been a recent explosion in individual statistics that try to measure a player’s impact. It covers questions to consider as well as collecting, prepping and plotting data. Introduction.

Statistics 59

Statistics Machine Learning Testing Modeling 59

Domino Data Lab

JULY 1, 2021

The model achieves relatively high accuracy and all data and code is freely available in the article. 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.

Modeling 111

Modeling Statistics Testing Metrics 111

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. Seriously, this entire article merely skims the surface of those reports. Check the end of this article for key guidance synthesized from the practices of the leaders in the field. Introduction.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49