Smart Data Collective

FEBRUARY 9, 2022

A growing number of traders are using increasingly sophisticated data mining and machine learning tools to develop a competitive edge. For instance, they display trend lines, pivot points, low volatility and other metrics in distinct colors. Analytics technology has become an invaluable aspect of modern financial trading.

Visualization 133

Visualization Optimization Analytics Testing 133

Rocket-Powered Data Science

DECEMBER 16, 2018

So, you start by assuming a value for k and making random assumptions about the cluster means, and then iterate until you find the optimal set of clusters, based upon some evaluation metric. The above example (clustering) is taken from unsupervised machine learning (where there are no labels on the training data). What data do we have?

Machine Learning 203

Machine Learning Metrics Optimization Data Science 203

BizAcuity

MAY 16, 2022

Fast food companies like Domino’s, McDonald’s and KFC collect massive amounts of data which includes customer data and other key business metrics for their own operations. Also, it is using customer data that they experiment and roll out new products every month. Data mining.

Business Intelligence 52

Business Intelligence Data mining Visualization Data Lake 52

Decision Management Solutions

JUNE 4, 2021

Like many enterprises, you’ve likely made a hefty investment in analytic technology—from interactive dashboards and advanced visualization tools to data mining, predictive analytics, machine learning (ML), and artificial intelligence (AI). Focusing on decision-making changes everything.

Analytics Technologies 52

Analytics Technologies Technology Modeling Key Performance Indicator 52

BizAcuity

APRIL 19, 2019

Several data mining and machine learning techniques are being developed that are able to foresee and predict high-risk players by tracking their actions while he or she is still engaged in gaming. We used similar metrics for bets and wins and pre-computed and aggregated them.

Machine Learning 75

Machine Learning Metrics Data mining Business Analytics 75

Smart Data Collective

OCTOBER 14, 2019

That, along with data mining can help if the developer wants to work with supply chains, for example. They can use predictive, descriptive and prescriptive analytics to help CSCOs turn metrics into insights for better decision-making. These can help a developer find a career in the data science field. Machine Learning.

Big Data 75

Big Data Software Data mining Prescriptive Analytics 75

IBM Big Data Hub

JANUARY 10, 2023

Process mining tools automate and generate dashboards which illustrate an ‘at a glance’ view of adoption rates. They also allow you to quantify business value based on improvements and allows you to assign and track key metrics with business objectives. Making it stick: Driving continuous change.

Management 52

Management Data-driven Data mining Business Objectives 52

Smart Data Collective

JUNE 16, 2021

The good news is that data analytics technology can help with employee training. Companies use data analytics software to see how employees are progressing across various metrics. Data analytics can also help with compliance. High cost of development and acquisition. Cybersecurity.

Data Analytics 89

Data Analytics Analytics Cost-Benefit Data mining 89

CIO Business Intelligence

APRIL 19, 2022

In any market segment you care to look at, you will find that the market front-runners will be those that have an exceptionally good data mining capability. Data that is accurate and easily accessible gives organisations the traction they need to target customers and deliver information or engagement without delay.

Digital Transformation 78

Digital Transformation Data mining Strategy Insurance 78

IBM Big Data Hub

SEPTEMBER 19, 2023

The data science lifecycle Data science is iterative, meaning data scientists form hypotheses and experiment to see if a desired outcome can be achieved using available data. The dedicated data analyst Virtually any stakeholder of any discipline can analyze data.

Data Science 87

Data Science Data Analytics Prescriptive Analytics Analytics 87

Smart Data Collective

OCTOBER 20, 2020

Some of the benefits of using data technology in conjunction with your GTM approach include: More easily defining a plan for your marketing and sales teams to follow. Defining the metrics and goals to measure the success of your business strategy. Identifying the needs, behaviors, and characteristics of your customers.

Marketing 107

Marketing Data Analytics Analytics Big Data 107

FineReport

APRIL 9, 2024

Furthermore, the implementation of healthcare data mining techniques allows organizations to uncover hidden patterns and correlations within their datasets. The integration of clinical data analysis solutions empowers organizations to conduct ongoing assessments of care delivery, treatment effectiveness, and patient satisfaction.

Digital Transformation 52

Digital Transformation Business Intelligence Data-driven Visualization 52

IBM Big Data Hub

JULY 6, 2023

Other challenges include communicating results to non-technical stakeholders, ensuring data security, enabling efficient collaboration between data scientists and data engineers, and determining appropriate key performance indicator (KPI) metrics.

Machine Learning 62

Machine Learning Data Science Statistics Deep Learning 62

Smart Data Collective

OCTOBER 28, 2019

Hadoop data mining tools have made these monitoring tools more reliable over the last few years. For instance, your APM tools might include: Insights into basic server metrics like memory or CPU. Application framework metrics like JMX and performance counters. Application log data and error information.

Big Data 59

Big Data Data mining Metrics Digital Transformation 59

IBM Big Data Hub

AUGUST 28, 2023

One of the best ways to take advantage of social media data is to implement text-mining programs that streamline the process. What is text mining? Validation and iteration It’s essential to make sure your mining results are accurate and reliable, so in the penultimate stage, you should validate the results.

Data mining 60

Data mining Machine Learning Deep Learning Marketing 60

datapine

OCTOBER 10, 2019

And if you’re looking to make a dashboard to outline return on marketing investment (ROMI), your visualizations, design, and metrics would be very different from those featured in a marketing channel engagement report. With better access to data than ever before, and improved data mining tools, we’re able to recover A LOT of types of data.

Dashboards 146

Dashboards Visualization Sales Metrics 146

Rocket-Powered Data Science

DECEMBER 15, 2018

If we cannot know that ( i.e., because it truly is unsupervised learning), then we would like to know at least that our final model is optimal (in some way) in explaining the data. In those intermediate steps it serves as an evaluation (or validation) metric. This challenge is known as the cold-start problem !

Machine Learning 171

Machine Learning Optimization Metrics Modeling 171

BizAcuity

APRIL 28, 2019

With Big Data, it is possible to acquire and segregate data with laser sharp focus with respect to one singular debtor. By clubbing various techniques like data mining, machine learning, artificial intelligence and statistical modelling, it makes predictions about events in the future.

Predictive Analytics 98

Predictive Analytics Analytics Big Data Data mining 98

Smart Data Collective

FEBRUARY 14, 2021

Do Your Research with Data Mining. This is one of the most important reasons that big data is important in SaaS. There are a lot of metrics that need to be tracked with data analytics tools. This means you will need to work hard to get your product on virtual shelves if you want to make some money.

Data-driven 86

Data-driven Big Data Deep Learning Software 86

Occam's Razor

AUGUST 22, 2011

" ~ Web Metrics: "What is a KPI? " + Standard Metrics Revisited Series. "Engagement" Is Not A Metric, It's An Excuse. Data Mining And Predictive Analytics On Web Data Works? Web Analytics Data Sampling 411. Overview & Importance of Qualitative Metrics.

KPI 124

KPI Metrics Measurement ROI 124

Cloudera

MAY 15, 2019

American Water leverages NiFi to track metrics against a simulated truck, showing the initial values in capturing this type of data. Walmart will be sharing about how its construction of a Finance stream in its data lake helped reduce and eliminate efforts on data mining and cleansing.

Enterprise 47

Enterprise Data Lake Data mining IoT 47

Sisense

APRIL 10, 2020

These libraries are used for data collection, analysis, data mining, visualizations, and ML modeling. Using XG-Boost to model the text data resulted in an almost identical score for Python and R. There are many performance metrics to evaluate performance of Machine Learning models.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

datapine

MAY 14, 2019

This interdisciplinary field of scientific methods, processes, and systems helps people extract knowledge or insights from data in a host of forms, either structured or unstructured, similar to data mining.

Data Science 350

Data Science Machine Learning Data-driven Big Data 350

Sisense

AUGUST 11, 2019

BI lets you apply chosen metrics to potentially huge, unstructured datasets, and covers querying, data mining , online analytical processing ( OLAP ), and reporting as well as business performance monitoring, predictive and prescriptive analytics. But on the whole, BI is more concerned with the whats and the hows than the whys.

Business Intelligence 75

Business Intelligence Business Analytics Analytics OLAP 75

IBM Big Data Hub

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects.

Data Science 60

Data Science Machine Learning Cost-Benefit Deep Learning 60

The Unofficial Google Data Science Blog

JANUARY 14, 2016

The LSOS may do this by exposing a random group of users to the new design and compare them to a control group, and then analyze the effect on important user engagement metrics, such as bounce rate, time to first action, or number of experiences deemed positive. In addition to a suitable metric, we must also choose our experimental unit.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

The Unofficial Google Data Science Blog

OCTOBER 7, 2015

Nevertheless, A/B testing has challenges and blind spots, such as: the difficulty of identifying suitable metrics that give "works well" a measurable meaning. Henne, Dan Sommerfield, Overall Evaluation Criterion , Proceedings 13th Conference on Knowledge Discovery and Data Mining, 2007. 2] Ron Kohavi, Randal M.

Modeling 61

Modeling Experimentation Knowledge Discovery Testing 61

Occam's Razor

SEPTEMBER 15, 2014

They will need two different implementations, it is quite likely that you will end up with two sets of metrics (more people focused for mobile apps, more visit focused for sites). Mobile content consumption, behavior along key metrics (time, bounces etc.) If you have ecommerce you will see key metrics related to money making.

Metrics 141

Metrics Reporting Analytics Marketing 141

datapine

MARCH 31, 2022

S/He is responsible for providing cost-effective solutions to achieve business objectives, comparing operational progress against project development while assisting in planning budgets, forecasts, timelines, and developing reports on performance metrics. SAS BI: SAS can be considered the “mother” of all BI tools.

Business Intelligence 239

Business Intelligence Statistics Visualization Data-driven 239

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

Occam's Razor

NOVEMBER 13, 2009

Chapter 3 The Awesome World of Clickstream Analysis: Metrics. The second half shows exactly how to pick the best metrics for your org and, my absolute favorite (Page 64), how to diagnose the root cause of a metrics performance. No metrics, data pukes, guidance on creating every more reports. A good thing.

Analytics 71

Analytics Metrics Measurement Testing 71

CIO Business Intelligence

JANUARY 20, 2023

The potential use cases for BI extend beyond the typical business performance metrics of improved sales and reduced costs. BI focuses on descriptive analytics, data collection, data storage, knowledge management, and data analysis to evaluate past business data and better understand currently known information.

Business Intelligence 144

Business Intelligence Dashboards Data mining OLAP 144

datapine

MARCH 25, 2019

What Is A Data Analysis Method? Data analysis method focuses on strategic approaches to taking raw data, mining for insights that are relevant to the business’s primary goals, and drilling down into this information to transform metrics, facts, and figures into initiatives that benefit improvement.

Key Performance Indicator 217

Key Performance Indicator Statistics Visualization Big Data 217

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

IBM Big Data Hub

APRIL 15, 2024

Virtualization layer abstraction and developer benefits Advantage: The virtualization layer in the data platform acts as an abstraction layer. They can focus on designing the core logic of their models without getting bogged down in data management complexities. Data virtualization streamlines these intricate systems crucially.

Analytics 74

Analytics Optimization Data-driven Metrics 74

DataKitchen

OCTOBER 11, 2021

I also record all these metrics and analyze them to build a DataOps report to measure the progress in collaboration, keep track of production error rates, and keep track of the data error rates, test coverage and SLAs (service level agreements) – figure 8. . Priyanjna Sharma is a Senior DataOps Implementation Engineer at DataKitchen.

Testing 157

Testing Metadata Dashboards Statistics 157

Domino Data Lab

MAY 20, 2021

Further, imbalanced data exacerbates problems arising from the curse of dimensionality often found in such biological data. def get_neigbours(M, k): nn = NearestNeighbors(n_neighbors=k+1, metric="euclidean").fit(M) Data mining for direct marketing: Problems and solutions. return synthetic. link] Ling, C.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136