Smart Data Collective

JUNE 26, 2019

Get Rid of Blind Spots in Statistical Models With Machine Learning. Data-related blind spots could also exist in your statistical models. RiskSpan is a company that built a machine learning algorithm that can flag error-prone parts of a statistical model and indicate which associated outputs may be unreliable.

Statistics 111

Statistics ROI Machine Learning Measurement 111

datapine

SEPTEMBER 27, 2022

17) “SQL Database Programming” (2015 Edition) By Chris Fehily. Originally published in 2018, the book has a second edition that was released in January of 2022. Written in O’Reilly’s popular Problem / Solution / Discussion style, “The SQL Cookbook” moves quickly from problem to solution, saving you time each step of the way.

Business Intelligence 292

Business Intelligence Data Warehouse Data Processing Data mining 292

Smart Data Collective

APRIL 13, 2021

After a marginal increase in 2015, another steep rise happened in 2016 through 2017 before the volume decreased in 2018 and rose in 2019, and dropped again in 2020. Based on figures from Statista , the volume of data breaches increased from 2005 to 2008, then dropped in 2009 and rose again in 2010 until it dropped again in 2011.

Testing 131

Testing Behavioral Analytics Data-driven Big Data 131

Sisense

MARCH 16, 2020

In a recent report, the group concluded that: “Better data and statistics will help governments track progress and make sure their decisions are evidence-based; they can also strengthen accountability. This is not just about governments. International agencies, CSOs, and the private sector should be involved.

Statistics 72

Statistics Data-driven Big Data Finance 72

BizAcuity

MARCH 25, 2019

This is resulting in the largest event management companies across this sector spending more than $43 billion on revenue analytics – which is a multi-dimensional and evolving field harnessing statistics, Artificial Intelligence and other tools to identify meaningful patterns in large data sets. billion in 2015 to $21.92

Analytics 74

Analytics Dashboards Sales Marketing 74

The Unofficial Google Data Science Blog

JULY 22, 2020

For example, imagine a fantasy football site is considering displaying advanced player statistics. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. One reason to do ramp-up is to mitigate the risk of never before seen arms.

Experimentation 96

Experimentation Statistics Testing Knowledge Discovery 96

CIO Business Intelligence

APRIL 24, 2023

Amanda Merola had zero technical background when she came to The Hartford in 2015, despite a natural interest in computers and a proclivity for problem-solving. Internal talent is gold, and we’re making sure our current employees find places to grow and modernize their skill sets.”

IT 111

IT Strategy Insurance Software 111

CIO Business Intelligence

JULY 6, 2023

Spreading the news Telecom provider AT&T began trialing RPA in 2015 to decrease the number of repetitive tasks, such as order entry, for its service delivery group. The statistics in discovery create a scope of the problem and how each issue can be solved, whether by business redefining their process or by applying technology,” she says.

Cost-Benefit 119

Cost-Benefit Technology Unstructured Data Testing 119

Occam's Razor

MARCH 30, 2017

Yes, a silo but so much better than 2015. Take all the math class you can possibly take, including Calc I, Calc II, Calc III, Linear Algebra, Probability, and Statistics. If you’re a student: + Math and physics classes are where one learns the basic methods for AI, machine learning, data science, and many of the jobs of the future.

Marketing Analytics 166

Marketing Analytics Marketing Analytics Deep Learning 166

bridgei2i

MAY 26, 2020

You know the markets shake and the accompanying Swine Flu epidemic of 2015 and 2016, the Japanese tsunami and the Thailand floods in 2011 that shook up the high-tech value chain quite a bit, the great financial crisis and the accompanying H1N1 outbreak in 2008-2009, MERS and SARS before that in 2003. As the crisis evolved.

Enterprise 131

Enterprise Management Forecasting Manufacturing 131

The Unofficial Google Data Science Blog

JULY 11, 2017

SCOTT Time series data are everywhere, but time series modeling is a fairly specialized area within statistics and data science. They may contain parameters in the statistical sense, but often they simply contain strategically placed 0's and 1's indicating which bits of $alpha_t$ are relevant for a particular computation. by STEVEN L.

Forecasting 66

Forecasting Statistics Modeling Software 66

Depict Data Studio

DECEMBER 1, 2020

Women in Data is an international non-profit organization started in 2015 whose mission is to bring women together for career advancement and an opportunity to uplift one another. It’s very difficult to take a table, rows and columns of summary statistics and figure out what chart that is going to be.

Visualization 52

Visualization Consulting Statistics Data Integration 52

Jen Stirrup

OCTOBER 21, 2019

Davies Review (2015 ) I mproving the Gender Balance on British Boards. Office for National Statistics (2015) Gender Pay Gap. Parliament UK (2015) Parliament and Women. UK Commission for Employment and Skills (2015) Opportunities and outcomes in education and work: Gender effects. Further Reading.

Data-driven 49

Data-driven Marketing Testing Insurance 49

Smarten

NOVEMBER 20, 2023

The field of AI includes planning and learning, natural language processing (NLP) and machine learning, as well as reasoning and perception, neural networks, economics, mathematics and statistics. Services Open AI OpenAI was founded in 2015 by Elon Musk, Sam Altman and others.

Technology 52

Technology Digital Transformation Software Advertising 52

Alation

FEBRUARY 20, 2020

To address these needs, a catalog should provide data samples and statistical profiles, lineage, lists of users and stewards, and tips on how the data should be interpreted. Prescriptive vs. Descriptive. Experts can then confirm, reject, or amend the guesses, to teach the computers and provide gold-standard knowledge for end-users.

Metrics 52

Metrics Machine Learning Consulting Statistics 52

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. The annals of statistics (1991): 1-67.

Statistics 45

Statistics Optimization Modeling Experimentation 45

The Unofficial Google Data Science Blog

MARCH 31, 2016

We often use statistical models to summarize the variation in our data, and random effects models are well suited for this — they are a form of ANOVA after all. Journal of the American Statistical Association 68.341 (1973): 117-130. [5] arXiv preprint arXiv:1506.04416 (2015). [6] 5] Anoop Korattikara, et al. 7] Nicholas A. "A

Modeling 40

Modeling Statistics Advertising Testing 40

Rita Sallam

JULY 22, 2016

These intentions are also reflected in Gartner’s previous webinar poll results in March and in our 2015 market share results, which show a 63% growth in Modern BI market growth versus a 1.7% decline in traditional BI ( See: Market Share Analysis: Business Intelligence and Analytics Software, 2015 ).

Analytics 40

Analytics Business Intelligence Metadata Statistics 40

Domino Data Lab

JUNE 8, 2021

Support Vector Machines (SVMs) are supervised learning models with a wide range of applications in text classification (Joachims, 1998), image recognition (Decoste and Schölkopf, 2002), image segmentation (Barghout, 2015), anomaly detection (Schölkopf et al., 1999) and more. References. Barghout, L. Pedrycz & S.-M. Rouveirol (Eds.),

Optimization 76

Optimization Machine Learning Data Science Measurement 76

The Unofficial Google Data Science Blog

APRIL 23, 2024

If $Y$ at that point is (statistically and practically) significantly better than our current operating point, and that point is deemed acceptable, we update the system parameters to this better value. e-handbook of statistical methods: Summary tables of useful fractional factorial designs , 2018 [3] Ulrike Groemping. Hedayat, N.J.A.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Cloudera

MARCH 2, 2018

Bridgespan Group estimated in 2015 that only 6% of nonprofits use data to drive improvements in their work. Banking, transportation, healthcare, retail, and real estate, all have seen the emergence of new business models fundamentally changing how customers use their services.

Data-driven 43

Data-driven Prescriptive Analytics Machine Learning Descriptive Analytics 43

Jet Global

AUGUST 4, 2023

Power BI Visuals One competitive feature that Microsoft added to Power BI in 2015 was the implementation of Custom Visuals. Programming/Scripting Languages The most common programming/scripting languages for finance and data people are SQL, R and Python. It’s basically as if you are working in Excel, but in Power BI.

Data Analytics 52

Data Analytics Analytics Finance Visualization 52

The Unofficial Google Data Science Blog

MAY 31, 2016

Statistical power is traditionally given in terms of a probability function, but often a more intuitive way of describing power is by stating the expected precision of our estimates. This is a quantity that is easily interpretable and summarizes nicely the statistical power of the experiment. In the U.S.,

Advertising 43

Advertising Testing Sales Statistics 43

IBM Big Data Hub

AUGUST 11, 2023

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. Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation.

Data Science 53

Data Science Machine Learning Cost-Benefit Deep Learning 53

Domino Data Lab

JANUARY 6, 2019

For content, the foundational material needs hands-on examples which reinforce statistical thinking , how to build reproducible workflows , understanding how to use confidence intervals , how to visualize data , no free lunch theorem, creating a confusion matrix , and so on. Joel Grus (2015). Pedro Domingos (2015). Think Bayes.

Data Science 56

Data Science Machine Learning Visualization Reporting 56

Peter James Thomas

OCTOBER 1, 2019

Sometimes the bars are clustered to allow multiple series to be charted side-by-side, for example yearly sales for 2015 to 2018 might appear against each product category. Some authorities describe them as any diagram using a map to display statistical data; I cover this type of general chart in Map Charts below. Cartograms.

Sales 61

Sales Measurement Statistics Data Architecture 61

Occam's Razor

JANUARY 9, 2012

AND you can have analysis of your risk in almost real time to get an early read and in a few days with statistical significance! The 2015 Digital Marketing Rule Book. You can literally control for risk should everything blow up in your face. Have your own stories to share about brand destruction? Got your own? Change or Perish.

Marketing 142

Marketing B2B Measurement Risk 142

Andrew White

JANUARY 9, 2022

This was not statistic and we have not really explored this in any greater detail since. 2015) and What is Wrong with Interoperability (in healthcare)? I suspect we should. Since this analysis we have manured the success of CDOs and it is true that in some regions of the world, some industries, the success rate of CDOs are variable.

Analytics 64

Analytics Measurement Data-driven Modeling 64

O'Reilly on Data

DECEMBER 9, 2019

We develop an ordinary least squares (OLS) linear regression model of equity returns using Statsmodels, a Python statistical package, to illustrate these three error types. CI theory was developed around 1937 by Jerzy Neyman, a mathematician and one of the principal architects of modern statistics. and an error term ??

Statistics 33

Statistics Uncertainty Risk Marketing 33

Domino Data Lab

JULY 1, 2021

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. The process of statistical learning can automatically extract said rules from a training dataset. The CRF model.

Modeling 111

Modeling Statistics Testing Metrics 111

Smart Data Collective

MAY 19, 2021

This trend is prevalent in the US, UK and further afield, with the former’s Bureau of Labour Statistics reporting that three of the jobs with the highest forecast growth level are wind turbine technician, registered nurse, and solar technician.

Data Analytics 96

Data Analytics Analytics Big Data Predictive Analytics 96

Peter James Thomas

AUGUST 18, 2018

From 2000 to 2015, I had some success [5] with designing and implementing Data Warehouse architectures much like the following: As a lot of my work then was in Insurance or related fields, the Analytical Repositories tended to be Actuarial Databases and / or Exposure Management Databases, developed in collaboration with such teams.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63

Jen Stirrup

JUNE 20, 2020

My analysis is based on the Financial statements put forward by PASS using some basic metrics; until you do that piece, you can’t move forward to compare and contrast it with other data since you have not done your ‘descriptive statistical analysis’ first to ensure that the comparison is valid. I have tabulated the information below.

Metrics 104

Metrics Business Analytics Data-driven Statistics 104

datapine

DECEMBER 28, 2021

1) What Is A Misleading Statistic? 2) Are Statistics Reliable? 3) Misleading Statistics Examples In Real Life. 4) How Can Statistics Be Misleading. 5) How To Avoid & Identify The Misuse Of Statistics? If all this is true, what is the problem with statistics? What Is A Misleading Statistic?

Statistics 363

Statistics Advertising Visualization Data mining 363

Domino Data Lab

JULY 9, 2019

.” And this is one of his papers about “you’re doing it wrong” where he talked about the algorithmic culture that he was observing in the machine learning community versus the generative model community that was more traditional in statistics. However, in 2014, 2015 the editors were falling in love with Slack.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

Rita Sallam

APRIL 2, 2023

SAS created, on top of the traditional statistical and machine learning models to predict events, a set of four unique models specifically focused on helping people impacted by flooding: An optimization network model (cost network flow algorithm) to optimally help displaced people reach public shelters and safer areas.

Optimization 52

Optimization Machine Learning Insurance Data Science 52

datapine

JUNE 7, 2022

From political issues to sports statistics and the recent report you received on the ROI of your company blog, the internet as well as informational reports are flooded with examples of misleading data visualization. If you want to go deeper into the topic, take a look at our misleading statistics blog post.

Visualization 267

Visualization Metrics Statistics Dashboards 267