Rocket-Powered Data Science

MARCH 19, 2021

Focus on the strategies that aim these tools, talents, and technologies on reaching business mission and goals: e.g., data strategy, analytics strategy, observability strategy ( i.e., why and where are we deploying the data-streaming sensors, and what outcomes should they achieve?).

Internet of Things 259

Internet of Things Analytics IoT Prescriptive Analytics 259

Smart Data Collective

DECEMBER 17, 2021

The process of Marketing Analytics consists of data collection, data analysis, and action plan development. Understanding your marketing data to make more informed and successful marketing strategy decisions is a systematic process. Types of Data Used in Marketing Analytics. Preparing the Data for Analysis.

Marketing Analytics 96

Marketing Analytics Marketing Data-driven Analytics 96

FineReport

MARCH 7, 2024

However, due to regulatory controls on sensitive data like phone numbers and technical challenges in cross-platform integration of Internet and mobile reporting data, our current matching rates are relatively low, reaching around 20% in ideal scenarios, excluding telecom data.

Dashboards 52

Dashboards Reporting Advertising Statistics 52

CIO Business Intelligence

JANUARY 17, 2024

The first was becoming one of the first research companies to move its panels and surveys online, reducing costs and increasing the speed and scope of data collection. According to Mohammed, the results of this digital transformation journey are measurable and impressive. js and React.js.

Digital Transformation 118

Digital Transformation Dashboards Reporting Data Science 118

Smart Data Collective

DECEMBER 9, 2021

Businesses already have a wealth of data but understanding your business will help you identify a data need – what kind of data your business needs to collect and if it collects too much or too little of certain data. Collecting too much data would be overwhelming and too little – inefficient.

Big Data 129

Big Data Data-driven Key Performance Indicator Testing 129

FineReport

NOVEMBER 30, 2020

So, how can you measure your work on a larger scale? YoY growth can eliminate factors that can skew your data by comparing your monthly figures to a larger sample and comparable period. As you grow from year to year, comparing specific months or quarters can refine your statistics and make them appear more reliable to investors.

Statistics 98

Statistics Metrics Sales Reporting 98

Smart Data Collective

MARCH 28, 2022

There are four main types of data analytics: Predictive data analytics: It is used to identify various trends, causation, and correlations. It can be further classified as statistical and predictive modeling, but the two are closely associated with each other. Improved decision-making will create more successful outcomes.

Data Analytics 86

Data Analytics Software Analytics Statistics 86

Data Science 101

JANUARY 15, 2020

Bias ( syatematic unfairness in data collection ) can be a potential problem in experiments and we need to take it into account while designing experiments. Reliability: It means measurements should have repeatable results. For eg: you measure the blood pressure of a person. Statistics Essential for Dummies by D.

Experimentation 96

Experimentation Statistics Measurement Testing 96

O'Reilly on Data

NOVEMBER 13, 2018

There are also many important considerations that go beyond optimizing a statistical or quantitative metric. As we deploy ML in many real-world contexts, optimizing statistical or business metics alone will not suffice. Classification parity means that one or more of the standard performance measures (e.g.,

Machine Learning 220

Machine Learning Risk Management Statistics 220

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. Alex Ratner on “Creating large training data sets quickly”.

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

O'Reilly on Data

JULY 28, 2020

These measures are commonly referred to as guardrail metrics , and they ensure that the product analytics aren’t giving decision-makers the wrong signal about what’s actually important to the business. When a measure becomes a target, it ceases to be a good measure ( Goodhart’s Law ). Any metric can and will be abused.

Marketing 362

Marketing Experimentation Metrics Testing 362

Rocket-Powered Data Science

JULY 7, 2019

Computer Vision: Data Mining: Data Science: Application of scientific method to discovery from data (including Statistics, Machine Learning, data visualization, exploratory data analysis, experimentation, and more). They provide more like an FAQ (Frequently Asked Questions) type of an interaction.

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

AWS Big Data

MAY 16, 2023

In this first post of the series, we show you how data collected from smart sensors is used for building automated dashboards using QuickSight to help distribution network engineers manage, maintain and troubleshoot smart sensors and perform advanced analytics to support business decision making.

Dashboards 74

Dashboards Statistics Data Collection Business Intelligence 74

BizAcuity

JULY 7, 2020

With respect to developments or changes in inbound markets, gaming data, player statistics, economic recovery speed, and more, adjust and reiterate core strategies. A crucial part of business recovery is to show and reassure people that all the precautionary measures are being taken to ensure safety. This too shall pass.

Revenue Optimization 98

Revenue Optimization Measurement Interactive Marketing 98

Smart Data Collective

MAY 4, 2023

Big data has been discussed by business leaders since the 1990s. It refers to datasets too large for normal statistical methods. Professionals have found ways to use big data to transform businesses. Furthermore, many websites have implemented anti-scraping measures to prevent bots from collecting data.

Data mining 79

Data mining Big Data Data-driven Measurement 79

CIO Business Intelligence

FEBRUARY 13, 2023

The name references the Greek letter sigma, which is a statistical symbol that represents a standard deviation. The process aims to bring data and statistics into the mesh to help objectively identify errors and defects that will impact quality. Six Sigma was trademarked by Motorola in 1993.

Management 83

Management Statistics Manufacturing Measurement 83

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

Smart Data Collective

APRIL 29, 2021

Big data has evolved from a technology buzzword into a real-world solution that helps companies and governments analyze data, extract the meaningful statistics, and apply it into their specific business needs. It’s not so much the realization that this information is collected, but what can be effectively done with it.

Big Data 96

Big Data Forecasting Unstructured Data Statistics 96

O'Reilly on Data

MARCH 31, 2020

All you need to know for now is that machine learning uses statistical techniques to give computer systems the ability to “learn” by being trained on existing data. After training, the system can make predictions (or deliver other results) based on data it hasn’t seen before. Machine learning adds uncertainty.

Management 332

Management Machine Learning Experimentation Metrics 332

FineReport

JUNE 29, 2023

Advanced levels of IoT analytics dashboards facilitate the identification of statistical trends, enabling the use of data for predictive failure analysis and extracting precise information and correlations from datasets. Generally, the primary objective of any IoT device in a connected environment is data collection.

IoT 52

IoT Dashboards Visualization Cost-Benefit 52

FineReport

JUNE 26, 2023

These reports commonly incorporate graphical elements such as charts, graphs, tables, and statistics, which complement the text-based information and offer visual representation. Status Reports These reports provide the current state of a project at a specific moment, detailing its alignment with the performance measurement baseline.

Reporting 52

Reporting Key Performance Indicator Sales Visualization 52

Smart Data Collective

AUGUST 31, 2020

At Smart Data Collective, we have talked extensively about the benefits of big data in digital marketing. We have focused a lot on using data analytics for SEO. However, there are a lot of other benefits of using big data in marketing. You shouldn’t limit yourself to using data analytics in your SEO strategy.

Data-driven 86

Data-driven Marketing Big Data Broadcasting 86

The Unofficial Google Data Science Blog

OCTOBER 31, 2016

By PATRICK RILEY For a number of years, I led the data science team for Google Search logs. We were often asked to make sense of confusing results, measure new phenomena from logged behavior, validate analyses done by others, and interpret metrics of user behavior. On the flip side, you sometimes have a small volume of data.

Metrics 107

Metrics Measurement Statistics Data Collection 107

Rocket-Powered Data Science

SEPTEMBER 18, 2018

For the modern digital organization, the proof of any inference (that drives decisions) should be in the data! Rich and diverse data collections enable more accurate and trustworthy conclusions. In “big data language”, we are talking about one of the 3 V’s of big data: big data Variety!

Big Data 103

Big Data Data Science Measurement Data Collection 103

Juice Analytics

MARCH 30, 2021

Data analyst —Data authors can’t create great art if they don’t like working with their materials. Data authors need to be comfortable with core statistical concepts and comfortable with manipulating data. Getting involved with deep data analysis can reveal the important messages and accurate ways to convey them.

Cost-Benefit 131

Cost-Benefit Statistics Metrics Dashboards 131

Domino Data Lab

AUGUST 12, 2021

Taking a closer look at the data you will notice that some columns have questions marks ? For this dataset that is the way the data collection denotes missing data. Let’s look at some examples of the data in the dataset: masses.iloc[[20, 456, 512],:]. Severity is made out of integers. Pandas Profiler.

Machine Learning 126

Machine Learning Reporting Statistics Visualization 126

IBM Big Data Hub

AUGUST 7, 2023

AI algorithms have the potential to surpass traditional statistical approaches for analyzing comprehensive recruitment data and accurately forecasting enrollment rates. A mitigation plan facilitates trial continuity by providing contingency measures and alternative strategies.

Optimization 58

Optimization Forecasting Data-driven Strategy 58

datapine

FEBRUARY 9, 2024

4) How to Select Your KPIs 5) Avoid These KPI Mistakes 6) How To Choose A KPI Management Solution 7) KPI Management Examples Fact: 100% of statistics strategically placed at the top of blog posts are a direct result of people studying the dynamics of Key Performance Indicators, or KPIs. 3) What Are KPI Best Practices? What happens next?

KPI 221

KPI Management Key Performance Indicator Measurement 221

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. measure the subjects’ ability to trust the models’ results. training data”) show the tangible outcomes.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Alation

APRIL 25, 2023

But whatever your industry, perfecting your processes for making important decisions about how to handle data is crucial. Whether you deal in customer contact information, website traffic statistics, sales data, or some other type of valuable information, you’ll need to put a framework of policies in place to manage your data seamlessly.

Data Governance 52

Data Governance Marketing Machine Learning Sales 52

Domino Data Lab

OCTOBER 2, 2019

As data science work is experimental and probabilistic in nature, data scientists are often faced with making inferences. You’ll measure this effect by looking at a quantity called the average treatment effect (ATE). What you really want to measure is the difference in outcomes. Let’s continue with this example.

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

Alation

APRIL 25, 2023

But whatever your industry, perfecting your processes for making important decisions about how to handle data is crucial. Whether you deal in customer contact information, website traffic statistics, sales data, or some other type of valuable information, you’ll need to put a framework of policies in place to manage your data seamlessly.

Data Governance 40

Data Governance Marketing Machine Learning Sales 40

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

1]" Statistics, as a discipline, was largely developed in a small data world. Data was expensive to gather, and therefore decisions to collect data were generally well-considered. As computing and storage have made data collection cheaper and easier, we now gather data without this underlying motivation.

Experimentation 61

Experimentation Testing Statistics Metrics 61

Depict Data Studio

APRIL 16, 2019

Long ago, I had majored in psychology so I could take as many research methods and statistics courses as possible. And learning how we learn–the courses on cognition, memory, perception, and brain biology–are critical in my everyday work as a data visualization designer and speaker. Increase the Inclusivity of Your Writing.

Visualization 56

Visualization Reporting Statistics Dashboards 56

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 56

Data Science Machine Learning Cost-Benefit Deep Learning 56

Sisense

OCTOBER 15, 2020

Every data professional knows that ensuring data quality is vital to producing usable query results. Streaming data can be extra challenging in this regard, as it tends to be “dirty,” with new fields that are added without warning and frequent mistakes in the data collection process.

Dashboards 98

Dashboards IoT Optimization Internet of Things 98

Insight

MARCH 12, 2020

In this article, I will discuss the construction of the AIgent, from data collection to model assembly. Data Collection The AIgent leverages book synopses and book metadata. The latter is any type of external data that has been attached to a book? Instead, I built the AIgent. features) and metadata (i.e.

Modeling 59

Modeling Metadata Publishing Sales 59