Smart Data Collective

MARCH 28, 2022

Diagnostic data analytics: It analyses the data from the past to identify the cause of an event by using techniques like data mining, data discovery, and drill down. Descriptive data analytics: It is the foundation of reporting, addressing questions like “how many”, “where”, “when”, and “what”.

Data Analytics 84

Data Analytics Software Analytics Statistics 84

Smart Data Collective

JUNE 24, 2023

This means that anybody that is possible victim will need to leverage AI-driven cyber security measures to protect their digital assets. But can cybersecurity measures predicated on machine learning actually help stop cryptocurrency scams? In 2018, researchers used data mining and machine learning to detect Ponzi schemes in Ethereum.

Machine Learning 77

Machine Learning Broadcasting Risk Data mining 77

datapine

JANUARY 24, 2021

The questions to ask when analyzing data will be the framework, the lens, that allows you to focus on specific aspects of your business reality. Once you have your data analytics questions, you need to have some standard KPIs that you can use to measure them. As Data Dan reminded us, “did the best” is too vague to be useful.

IT 317

IT Statistics KPI Data-driven 317

Occam's Razor

AUGUST 22, 2011

Key To Your Digital Success: Web Analytics Measurement Model. Be data driven?" " Measuring Incrementality: Controlled Experiments to the Rescue! Barriers To An Effective Web Measurement Strategy [+ Solutions!]. Slay The Analytics Data Quality Dragon & Win Your HiPPO's Love! What's The Fix?

KPI 124

KPI Metrics Measurement ROI 124

FineReport

APRIL 26, 2023

Here are some key factors to keep in mind: Understanding business objectives : It is important to identify and understand the business objectives before selecting a big data tool. These objectives should be broken down into measurable analytical goals, and the chosen tool should be able to meet those goals. Top 10 Big Data Tools 1.

Big Data 52

Big Data Data Analytics Analytics Cost-Benefit 52

The Unofficial Google Data Science Blog

JANUARY 14, 2016

And an LSOS is awash in data, right? Well, it turns out that depending on what it cares to measure, an LSOS might not have enough data. The practical consequence of this is that we can’t afford to be sloppy about measuring statistical significance and confidence intervals. known, equal variances).

Experimentation 41

Experimentation Statistics Metrics Measurement 41

FineReport

DECEMBER 10, 2019

In column coordinates, column 0 represents the serial number, column 1 represents the name of the member, column 2 represents the age, column 3 represents the weight, columns 4-6 represent the measurements of the male members, and columns 7-9 represent the measurements for female members. There are many problems with these dirty data.

Data mining 98

Data mining Data Quality Statistics Measurement 98

DataKitchen

OCTOBER 11, 2021

First, you must understand the existing challenges of the data team, including the data architecture and end-to-end toolchain. Second, you must establish a definition of “done.” In DataOps, the definition of done includes more than just some working code. Definition of Done. When can you declare it done?

Testing 152

Testing Metadata Dashboards Statistics 152

datapine

MAY 29, 2019

BI architecture has emerged to meet those requirements, with data warehousing as the backbone of these processes. But first, let’s start with basic definitions. The point is to access, explore, and analyze measurable aspects of a business. Exclusive Bonus Content: Wondering about BI & DWH? c) What is the audience?

Business Intelligence 196

Business Intelligence Data Warehouse Dashboards Visualization 196

datapine

AUGUST 29, 2022

Companies, both big and small, are seeking the finest ways to leverage their data into a competitive advantage. With that in mind, we have prepared a list of the top 19 definitive data analytics and big data books, along with magazines and authentic readers’ reviews upvoted by the Goodreads community. trillion each year.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

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

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

Occam's Razor

SEPTEMBER 4, 2012

If you don't, it is time to future-proof your marketing strategy, because the world is most definitely moving to utility marketing. I'm sure you are impressed at the data mining and intent targeting efforts of TripIt. That would have been an easy thing to do based on data mining ( lame data mining!).

Marketing 144

Marketing Data mining Measurement Strategy 144

datapine

JULY 24, 2019

But first, let’s consider what we mean when we talk about business intelligence dashboards, or more specifically—the business intelligence dashboard definition. What Is The Definition Of A BI Dashboard? The basis for factual and informed decision making is real-time data analysis. 2) Know your dashboards.

Dashboards 148

Dashboards Business Intelligence Visualization KPI 148

The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

Posteriors are useful to understand the system, measure accuracy, and make better decisions. Methods like the Poisson bootstrap can help us measure the variability of $t$, but don’t give us posteriors either, particularly since good high-dimensional estimators aren’t unbiased. We don’t have a fully satisfying answer for this issue.

KDD 40

KDD Testing Machine Learning Modeling 40

The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

And since the metric average is different in each hour of day, this is a source of variation in measuring the experimental effect. Another way to build a classifier for variance reduction is to address the rare event problem directly — what if we could predict a subset of instances in which the event of interest will definitely not occur?

Experimentation 40

Experimentation Metrics Statistics Measurement 40

datapine

DECEMBER 28, 2021

Statistics are infamous for their ability and potential to exist as misleading and bad data. To get this journey started let’s look at the misleading statistics definition. Exclusive Bonus Content: Download Our Free Data Integrity Checklist. Get our free checklist on ensuring data collection and analysis integrity!

Statistics 363

Statistics Advertising Visualization Data mining 363

datapine

NOVEMBER 27, 2019

While we work on programs to avoid such inconvenience , AI and machine learning are revolutionizing the way we interact with our analytics and data management while increment in security measures must be taken into account. It’s an extension of data mining which refers only to past data.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

Alation

AUGUST 26, 2021

And types of metadata — or data about data — abound. Some high-level metadata categories in a data catalog include: Behavioral : Records who is using data, and how they are using it. Technical: Shows schema or table definitions. Business: Policies on how to handle different kinds of data appropriately.

Metadata 64

Metadata Data Governance Dashboards Software 64

Jet Global

MAY 1, 2023

Introduction Why should I read the definitive guide to embedded analytics? The Definitive Guide to Embedded Analytics is designed to answer any and all questions you have about the topic. Users Want to Help Themselves Data mining is no longer confined to the research department. It is now most definitely a need-to-have.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52