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. Statistics Essential for Dummies by D. Rumsey Statistical Reasoning Course by Stanford Ligunita Introduction to the Practice of Statistics by D. REFERENCES.

Experimentation 96

Experimentation Statistics Measurement Testing 96

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

Occam's Razor

FEBRUARY 22, 2010

These toolbars also collect limited information about the browsing behavior of the customers who use them, including the pages visited, the search terms used, perhaps even time spent on each page, and so forth. Typically, data collected is anonymous and not personally identifiable information (PII). 6: Self-reported Data.

Metrics 124

Metrics Advertising Reporting Software 124

erwin

APRIL 21, 2020

As illustrated above, a data catalog is essential to business users because it synthesizes all the details about an organization’s data assets across multiple data sources. It organizes them into a simple, easy- to-digest format and then publishes them to data communities for knowledge-sharing and collaboration.

Measurement 97

Measurement Sales Data Governance Metadata 97

erwin

APRIL 21, 2020

So one of the biggest lessons we’re learning from COVID-19 is the need for data collection, management and governance. What’s the best way to organize data and ensure it is supported by business policies and well-defined, governed systems, data elements and performance measures? Put border controls in place.

Measurement 96

Measurement Sales Data Governance Metadata 96

Anmut

OCTOBER 29, 2020

COVID-19 exposes shortcomings in data management. Getting consistency is also a daunting challenge in the face of a tsunami of data. Having a data-driven approach creates much sought after competitive advantage. To get consistent and reliable data, this is the kind of standardisation we need.

Data Governance 52

Data Governance Data Collection Data-driven Statistics 52

FineReport

FEBRUARY 28, 2020

Then the reporting engine publishes these reports to the reporting portal to allow non-technical end-users access. In this way, users can gain insights from the data and make data-driven decisions. . The underlying data is responsible for data management, including data collection, ETL, building a data warehouse, etc.

Reporting 96

Reporting Enterprise Visualization Business Intelligence 96

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?

KPI 221

KPI Management Key Performance Indicator Measurement 221

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

Smart Data Collective

MAY 4, 2023

UMass Global has a very insightful article on the growing relevance of big data in business. Big data has been discussed by business leaders since the 1990s. The term was first published in 1999 and gained a solid definition in the early 2000s. It refers to datasets too large for normal statistical methods.

Data mining 91

Data mining Big Data Data-driven Measurement 91

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. Use of influence functions goes back to the 1970s in robust statistics. That seems much more robust.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

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.

Visualization 56

Visualization Reporting Statistics Dashboards 56

IBM Big Data Hub

AUGUST 7, 2023

2,3 When clinical trials are prematurely discontinued due to trial site underperformance, the research questions remain unanswered and research findings end up not published. AI algorithms have the potential to surpass traditional statistical approaches for analyzing comprehensive recruitment data and accurately forecasting enrollment rates.

Optimization 55

Optimization Forecasting Data-driven Strategy 55

Occam's Razor

JUNE 6, 2007

I am thrilled to say that my book Web Analytics: An Hour A Day has been published and is now widely available. If you have no experience with Web Analytics then you'll learn what it is and the nitty gritty of data collection and core metrics such as Visits and Time on Site and Bounce Rate and Top Destinations etc.

Analytics 60

Analytics Metrics Marketing Dashboards 60

Domino Data Lab

JULY 22, 2019

He was saying this doesn’t belong just in statistics. He also really informed a lot of the early thinking about data visualization. It involved a lot of interesting work on something new that was data management. To some extent, academia still struggles a lot with how to stick data science into some sort of discipline.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

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 53

Data Science Machine Learning Cost-Benefit Deep Learning 53

The Unofficial Google Data Science Blog

JULY 18, 2023

Editor's note : The relationship between reliability and validity are somewhat analogous to that between the notions of statistical uncertainty and representational uncertainty introduced in an earlier post. Measurement challenges Assessing reliability is essentially a process of data collection and analysis.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

FineReport

JUNE 24, 2022

However, sometimes we may find some inconveniences in the process of data collection and data visualization. For example, when making routine work analysis reports, we find it is inconvenient to re-summarize the latest data and recreate a new report, especially when the data is in a large volume.

Reporting 52

Reporting IT Sales Recreation/Entertainment 52

Andrew White

JANUARY 9, 2022

I try to relate as much published research as I can in the time available to draft a response. – In the webinar and Leadership Vision deck for Data and Analytics we called out AI engineering as a big trend. – In the webinar and Leadership Vision deck for Data and Analytics we called out AI engineering as a big trend.

Analytics 64

Analytics Measurement Data-driven Modeling 64

Domino Data Lab

MARCH 3, 2019

This month, the theme is not specifically about conference summaries; rather, it’s about a set of follow-up surveys from Strata Data attendees. We had big surprises at several turns and have subsequently published a series of reports. Evolving Data Infrastructure: Tools and Best Practices for Advanced Analytics and AI (Jan 2019).

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

O'Reilly on Data

OCTOBER 18, 2022

Despite the possibilities for feedback loops and error propagation, we may end up needing AI to label data for other AI systems. We’ve also observed organizations claiming that data privacy concerns prevent data collection that would enable bias testing. More are published all the time.

Risk Management 296

Risk Management Risk Testing Measurement 296

datapine

AUGUST 29, 2022

Due to this book being published recently, there are not any written reviews available. 4) Big Data: Principles and Best Practices Of Scalable Real-Time Data Systems by Nathan Marz and James Warren. and this book will give you an insight into their data collecting procedures and the reasons behind them.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Insight

MARCH 12, 2020

There was only one problem: literary agents, the gatekeepers of the publishing industry, kept rejecting the book?—?often Galbraith eventually opted to publish Cuckoo’s Calling through an acquaintance of sorts. but the publishing industry failed to see it. Data Collection The AIgent leverages book synopses and book metadata.

Modeling 59

Modeling Metadata Publishing Sales 59

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

Smart Data Collective

JULY 2, 2019

At Smart Data Collective, we strive to have a balanced conversation about the impact of big data. There are obviously a lot of beneficial changes that big data has spurred. However, big data has also created some important challenges as well, which we feel duty-bound to discuss.

Big Data 80

Big Data ROI Testing Statistics 80

Jet Global

MAY 1, 2023

Let’s just give our customers access to the data. You’ve settled for becoming a data collection tool rather than adding value to your product. While data exports may satisfy a portion of your customers, there will be many who simply want reports and insights that are available “out of the box.”

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52

Jet Global

NOVEMBER 2, 2021

Those without KPIs are left without any valuable statistics, while those with established performance tracking dashboards are able to make data driven decisions. Setting up an insightful university KPI system requires three main components: effective data collection, an automated process, and realistic goals.

Metrics 52

Metrics Reporting KPI Key Performance Indicator 52

Jet Global

MAY 9, 2024

Data ingestion methods can include batch ingestion (collecting data at scheduled intervals) or real-time streaming data ingestion (collecting data continuously as it is generated). Technologies used for data ingestion include data connectors, ingestion frameworks, or data collection agents.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52