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. Current Ratio. Let me draw that out for you.

Metrics 104

Metrics Business Analytics Data-driven Statistics 104

The Data Visualisation Catalogue

APRIL 4, 2019

Then in around 2016, I first started using VR hardware and from there I had two thoughts: first, that VR is going to be the most revolutionary technology of my lifetime; and second, that VR can make the process of data analysis and presentation much easier (especially in my job as an investment analyst).

Dashboards 75

Dashboards Predictive Modeling Visualization Modeling 75

Occam's Razor

SEPTEMBER 13, 2016

With that as context, you can imagine how heart-broken I was when Jane shared the following visual from a study done by Econsultancy and Lynchpin. Visual perception of information. Ability to create insightful data visualizations. Ok, maybe statistical modeling smells like an analytical skill. Strategic thinking skills.

Modeling 128

Modeling Reporting Strategy Consulting 128

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 64

Forecasting Statistics Modeling Software 64

The Unofficial Google Data Science Blog

MARCH 13, 2017

Typically, causal inference in data science is framed in probabilistic terms, where there is statistical uncertainty in the outcomes as well as model uncertainty about the true causal mechanism connecting inputs and outputs. A note on visualization The most convenient way to inspect our feature importances (attributions) is to visualize them.

IT 68

IT Visualization Modeling Uncertainty 68

Domino Data Lab

OCTOBER 23, 2019

As a result, there has been a recent explosion in individual statistics that try to measure a player’s impact. Knowing that the ultimate goal is to compare the social-media influence and power of NBA players, a great place to start is with the roster of the NBA players in the 2016–2017 season. 05) in predicting changes in attendance.

Statistics 59

Statistics Machine Learning Testing Modeling 59

Smarten

SEPTEMBER 6, 2019

SSDP allows average business users to compile and prepare data and use that data in analytics to test hypotheses, visualize and share data, prepare reports and support day-to-day tasks with complete drill-down and drill-through capability, custom alerts and mobile access that supports the needs of every team member.

Analytics 51

Analytics IT Business Intelligence Testing 51

Smarten

JANUARY 3, 2019

Business users can work with self-serve advanced data discovery and advanced analytical tools using a drag and drop interface, with no advanced skill requirement for statistical analysis, algorithms or technical knowledge. ’ Clearly, Citizen Analysts are here to stay!

Business Intelligence 40

Business Intelligence Statistics Interactive Data Science 40

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. Figure 4: Visualization of a central composite design. Journal of Statistical Software, 56(1):1-56, 2014. [5] Hedayat, N.J.A.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

On the one hand, basic statistical models (e.g. For example, when the prediction of an example is unexpectedly large we can trace back the prediction through visualization of the final block and identify its problematic, unexpectedly large input. linear regression, trees) can be too rigid in their functional forms. Pfeifer, J.,

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

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. Often our data can be stored or visualized as a table like the one shown below. Journal of the American Statistical Association 68.341 (1973): 117-130. [5] 9] Steven L.

Modeling 40

Modeling Statistics Advertising Testing 40

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. Data visualization for prediction accuracy ( credit: R2D3 ).

Data Science 56

Data Science Machine Learning Visualization Reporting 56

Domino Data Lab

AUGUST 22, 2019

Although it’s not perfect, [Note: These are statistical approximations, of course!] Human brains are not well suited to visualizing anything in greater than three dimensions. Visualizing data using t-SNE. Example 11.6 Detecting collocated bigrams with more conservative thresholds. Joulin, A., arXiv: 1607.01759. Bojanowski, P.,

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

AWS Big Data

JULY 10, 2024

We are excited to announce a new capability of the AWS Glue Studio visual editor that offers a new visual user experience. Now you can author data preparation transformations and edit them with the AWS Glue Studio visual editor. You can configure all these steps in the visual editor in AWS Glue Studio. Choose Save.

Interactive 103

Interactive Visualization Data Integration Statistics 103

Domino Data Lab

AUGUST 8, 2019

Having participated in several Foo Camps—and even co-chaired the Ed Foo series in 2016-17— most definitely, a Foo will turn your head around. He’s been out of Wolfram for a while and writing exquisite science books including Elements: A Visual Explanation of Every Known Atom in the Universe and Molecules: The Architecture of Everything.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

datapine

AUGUST 29, 2022

It was lately revised and updated in January 2016. With a very strong practical focus “Analytics in a Big Data World” starts by providing the readers with the basic nomenclature, the analytics process model, and its relation to other relevant disciplines, such as statistics, machine learning, and artificial intelligence.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Domino Data Lab

SEPTEMBER 25, 2019

A big part of statistics, particularly for financial and econometric data, is analyzing time series, data that are autocorrelated over time. predict(usBest, n.ahead=5, se.fit=TRUE) $pred Time Series: Start = 2012 End = 2016 Frequency = 1 [1] 49292.41 Chapter Introduction: Time Series and Autocorrelation. > attGarch.

Forecasting 87

Forecasting Modeling Statistics Optimization 87

Occam's Razor

JULY 13, 2017

In service of that belief, there are few things that bring me as much joy as visualizing data (smart segmentation comes close). While I am partial to the simplest of visualizations in a business data context, I love a simple Bar Chart just as much as a Chord or Fisher-Yates Shuffle. 6: Turbocharging Data Visuals with Storytelling.

Visualization 125

Visualization Strategy Interactive Statistics 125

TIBCO

FEBRUARY 22, 2016

Visualization is at the heart of this accessible makeover; morphing statistics into clear graphics that provide an instant snapshot of the story usually buried in the data, so that non-experts can spot anomalies in performance and gain insight in real time.

Data Analytics 40

Data Analytics Analytics Snapshot Statistics 40

Smarten

SEPTEMBER 9, 2024

The Definition and Evolution of the Citizen Data Scientist Role The world-renowned technology research firm, Gartner, first introduced the concept of the Citizen Data Scientist in 2016. Since then, the idea has grown in popularity, and the role has grown in importance and prominence. ‘To Who is a Citizen Data Scientist ?

Prescriptive Analytics 52

Prescriptive Analytics Predictive Analytics Data-driven Business Intelligence 52

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

MARCH 27, 2019

Special thanks to Addison-Wesley Professional for permission to excerpt the following “Manipulating data with dplyr” chapter from the book, Programming Skills for Data Science: Start Writing Code to Wrangle, Analyze, and Visualize Data with R. While this grouping is not visually apparent (i.e., Introduction.

Statistics 85

Statistics Data Science Visualization IT 85

Smarten

SEPTEMBER 9, 2024

In fact, the world-renowned technology research firm, Gartner, first introduced the concept in 2016. Gartner defines a citizen data scientist as, ‘ a person who creates or generates models that leverage predictive or prescriptive analytics, but whose primary job function is outside of the field of statistics and analytics.’

Consulting 52

Consulting Cost-Benefit Data Science Business Intelligence 52