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Tag: modeling

Webinar: Data Modeling and Relational to NoSQL

To view just the slides from this presentation, click HERE

About the Webinar

Making the move to a document database can be intimidating. Yes, its flexible data model gives you a lot of choices, but it also raises questions: Which way is the right way? Is a document database even the right tool? Join this live session on the basics of data modeling with JSON to learn:

How a document database compares to a traditional RDBMSWhat JSON data modeling means for your…

Continue reading: https://www.dataversity.net/webinar-data-modeling-and-relational-to-nosql/

Source: www.dataversity.net
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Slides: Data Modeling and Relational to NoSQL

Homepage Uncategorized Slides: Data Modeling and Relational to NoSQL

To view just the On-Demand recording from this presentation, click HERE

About the Webinar

Making the move to a document database can be intimidating. Yes, its flexible data model gives you a lot of choices, but it also raises questions: Which way is the right way? Is a document database even the right tool? Join this live session on the basics of data modeling with JSON to learn:

How a…

Continue reading: https://www.dataversity.net/slides-data-modeling-and-relational-to-nosql/

Source: www.dataversity.net
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How to Determine the Best Fitting Data Distribution Using Python – KDnuggets

Sometimes you know the best fitting distribution, or probability density function, of your data prior to analysis; more often, you do not. Approaches to data sampling, modeling, and analysis can vary based on the distribution of your data, and so determining the best fit theoretical distribution can be an essential step in your data exploration process.

This is where distfit comes in.

distfit is a python package for probability density fitting across 89 univariate distributions to non-censored data by residual sum of squares (RSS), and hypothesis testing. Probability density fitting is the fitting of a probability distribution to a series of data concerning the repeated measurement of a variable phenomenon.… Read more...

SAP BW Data Mining Analytics: Regression Reporting (Part 3)

Summary
Regression analysis is one of the methods supplied “built-in” with SAP BW Data Mining. Based on this method regression models can be created and configured to satisfy specific analysis requirements (e.g., choice between linear or non-linear approximation, etc.). The method includes regression-specific reporting that allows analysis of the modeling results. In this paper we are suggesting a number of ways to extend this reporting in order to improve insight into the results of…

Continue reading: http://www.datasciencecentral.com/xn/detail/6448529:BlogPost:1070388

Source: www.datasciencecentral.com
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DAS Slides: Data Modeling Techniques

Homepage Education Resources For Use & Management of Data DAS Slides: Data Modeling Techniques

To view the webinar from this presentation, click HERE

About the Webinar

Data modeling continues to be a tried-and-true method of managing critical data aspects from both the business and technical perspective. Like any tool or methodology, there is a “right tool for the right job”, and specific model types exist for both business and technical users across operational, reporting, analytic, and other use cases. This webinar will provide an overview of the various data modeling techniques available, and how to use each for maximum value to the organization.… Read more...

DAS Webinar: Data Modeling Techniques

To view the slides from this presentation, click HERE

About the Webinar

Data modeling continues to be a tried-and-true method of managing critical data aspects from both the business and technical perspective. Like any tool or methodology, there is a “right tool for the right job”, and specific model types exist for both business and technical users across operational, reporting, analytic, and other use cases. This webinar will provide an overview of the various data modeling techniques available, and how to use each for maximum value to the organization.

About the Speaker

Donna Burbank

Managing Director, Global Data Strategy, Ltd

Donna Burbank is a recognized industry expert in information management with over 20 years of experience helping organizations enrich their business opportunities through data and information.  … Read more...

Best Data Science Certifications In 2022

Over a span of the recent few years, data science has become an integral part of all the major industry sectors, ranging from agriculture, marketing analytics, public policy, to fraud detection, risk management, and marketing optimization. One of the goals of data science is to resolve the many issues that preside within the economy at large, and its other branches and individual sectors, through the use of machine learning, predictive modeling, statistics, and data preparation.

Data science emphasizes the utilization of the general methods but without changing its application, no matter what its domain is. In this way, this approach is a lot more different from the other traditional statistics scenario that usually tends to focus solely upon seeking specific solutions to particular domains or sectors.

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Topic Modeling: Algorithms, Techniques, and Application

Used in unsupervised machine learning tasks, Topic Modeling is treated as a form of tagging and primarily used for information retrieval wherein it helps in query expansion. It is vastly used in mapping user preference in topics across search engineers. The main applications of Topic Modeling are classification, categorization, summarization of documents. AI methodologies associated with genetics, social media, and computer vision tasks are associated with Topic Modeling. It also powers analysis on social networks pertaining to the sentiments of users.

Topic Modeling Difference and Related Algorithms

Topic Modeling is performed on unsupervised information and has a clear distinction from text classification and clustering tasks.

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Optimizing and Accelerating COVID-19 Predictive Modeling

The COVID-19 pandemic has wreaked havoc across the globe. As of July 30, 2021, nearly 197.5 million people have been infected with coronavirus, and 4.2 million people have died. The COVID-19 pandemic has resulted in significant pressure on healthcare systems around the globe. The need for effective diagnostic, prognostic and therapeutic procedures has never been as urgent as it is today. Despite significant investment and research on understanding and managing this disease, there is still a lack of efficient predictive models for patient stratification and management of this disease. 

Since the transmission rate of COVID-19 is extremely high, healthcare facilities are continuously facing the challenges of managing patient surges while ensuring the safety of staff, family members, and patients suffering from other illnesses. 

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Introduction to Marketing Mix Modeling in Python

To keep a business running, spending money on advertising is crucial — this is the case regardless of whether the company is small or already established. And the number of ad spendings in the industry are enormous:

Source: https://www.webstrategiesinc.com/blog/how-much-budget-for-online-marketing-in-2014, (article updated in 2020)

These volumes make it necessary to spend each advertising dollar wisely. However, this is easier said than done, or as US retail magnate John Wanamaker or UK industrialist Lord Leverhulme put it about a hundred years ago:

“Half the money I spend on advertising is wasted; the trouble is I don’t know which half.”

You might think that this is less of a problem nowadays, but strangely enough, it still persists.

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AI-Ethics in Engineering

The Bias of Traditional Engineers in AI-based Modeling of Physics — PART 2

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85% of data science projects fail – here’s how to avoid it

Here are a few common traps that data scientists can avoid to NOT be one of the 85% of data science projects that fail.

Sponsored Post.

85% of data science projects fail. So how do you avoid being part of that statistic? Here are a few common traps that data scientists can avoid.

1. Move beyond predictions

There’s no doubt that predictive modeling is a big upside of data science — especially during those frequent instances when we know that the result is out of our control so predicting it is all we can do. But why only limit data science to predictions?

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NCAR will collaborate on new initiative to integrate AI with climate modeling | NCAR & UCAR News – UCAR

NSF announces new Center for Learning the Earth with Artificial Intelligence and Physics

Sep 10, 2021

by Laura Snider

The National Center for Atmospheric Research (NCAR) is a collaborator on a new $25 million initiative that will use artificial intelligence to improve traditional Earth system models with the goal of advancing climate research to better inform decision makers with more actionable information.

The Center for Learning the Earth with Artificial Intelligence and Physics (LEAP) is one of six new Science and Technology Centers announced by the National Science Foundation to work on transformative science that will broadly benefit society. LEAP will be led by Columbia University in collaboration with several other universities as well as NCAR and NASA’s Goddard Institute for Space Studies.

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Columbia University to Launch $25 Million AI-based Climate Modeling Center – HPCwire

“We still have these huge cones of uncertainty,” added deputy director Galen McKinley, a professor of earth and environmental sciences who is based at Lamont-Doherty, part of the Columbia Climate School. “Our goal is to harness data from observations and simulations to better represent the underlying physics, chemistry, and biology of Earth’s climate system. More accurate models will help give us a clearer vision of the future.”

Dealing with massive data requires a modern infrastructure. In collaboration with Google Cloud and Microsoft, Ryan Abernathey, an associate professor of earth and environmental sciences based at Lamont-Doherty, will create a platform to allow researchers to share and analyze data.

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New York University Join Hands with National Science Foundation On Climate Modeling – Technology Times Pakistan

New York University will join a new, National Science Foundation-supported center that will develop the next generation of data-driven, climate modeling.

NYU will join a new, National Science Foundation-supported center that will develop the next generation of data-driven, physics-based climate models.

Photo credit: benedek/Getty Images
Center Aims to Improve Climate Projections and to Motivate Investment in Policies and Infrastructure to Confront Rising Seas and Warmer Temperatures

New York University will join a new, National Science Foundation-supported center that will develop the next generation of data-driven, physics-based climate models, with the larger aim of providing actionable information for societies to adapt to climate change and to protect vulnerable populations.

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NLP Preprocessing and Latent Dirichlet Allocation (LDA) Topic Modeling with Gensim

The gensim Python library makes it ridiculously simple to create an LDA topic model. The only bit of prep work we have to do is create a dictionary and corpus.

A dictionary is a mapping of word ids to words. To create our dictionary, we can create a built in gensim.corpora.Dictionary object. From there, the filter_extremes() method is essential in order to ensure that we get a desirable frequency and representation of tokens in our dictionary.

id2word = corpora.Dictionary(data_preprocessed)
id2word.filter_extremes(no_below=15, no_above=0.4, keep_n=80000)

The filter_extremes() method takes 3 parameters. Let’s break down what those mean:

  • filter out tokens that appear in less than 15 documents
  • filter out tokens that appear in more than 40% of documents
  • after the above two steps, keep only the first 80,000 most frequent tokens

A corpus is essentially a mapping of word ids to word frequencies.

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Columbia to Launch $25 Million AI-based Climate Modeling Center – Columbia University


“We still have these huge cones of uncertainty,” added deputy director Galen McKinley, a professor of earth and environmental sciences who is based at Lamont-Doherty, part of the Columbia Climate School. “Our goal is to harness data from observations and simulations to better represent the underlying physics, chemistry, and biology of Earth’s climate system. More accurate models will help give us a clearer vision of the future.”

Dealing with massive data requires a modern infrastructure. In collaboration with Google Cloud and Microsoft, Ryan Abernathey, an associate professor of earth and environmental sciences based at Lamont-Doherty, will create a platform to allow researchers to share and analyze data.

Read more...

Modeling and Generating Time-Series Data using TimeGAN

Generating time-series data using a library with a high-level implementation of TimeGAN

Archit Yadav
Photo by Agê Barros on Unsplash

In a previous article, the idea of generating artificial or synthetic data was explored, given a limited amount of dataset as a starter. The data taken at that time was tabular, which is like a regular dataset which we usually encounter. In this article however, we will look at time-series data and explore a way to generate synthetic time-series data.

So how does a time-series data differ from a regular tabular data? A time-series dataset has one extra dimension — time.

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GSDMM: Topic Modeling for Social Media Posts and Reviews

In this post, we’ll look at a set of tweets and try to determine its major theme(s). But first, a little bit of context.

As written by yamini5 on Analytics Vidhya, “Topic modelling[sic] refers to the task of identifying topics that best describes a set of documents.” Simply put, topic modeling refers to the process of ingesting a bunch of unstructured and unlabeled text data and then classifying them into the different topics that they represent. For example, we might have a collection of Emily Dickinson’s poems. When we try to classify them, we’re probably going to end up with the topics of life, death, and love.

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Cold planets exist throughout our galaxy, even in the galactic bulge, research suggests

Although thousands of planets have been discovered in the Milky Way, most reside less than a few thousand light years from Earth. Yet our Galaxy is more than 100,000 light years across, making it difficult to investigate the Galactic distribution of planets. But now, a research team has found a way to overcome this hurdle.

In a study published in The Astrophysical Journal Letters, researchers led by Osaka University and NASA have used a combination of observations and modeling to determine how the planet-hosting probability varies with the distance from the Galactic center.

The observations were based on a phenomenon called gravitational microlensing, whereby objects such as planets act as lenses, bending and magnifying the light from distant stars.

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Speed Transition Matrix: Novel road traffic data modeling technique

Let’s firstly explain the basic parts of the STM:

1. Transition

In STM concept, the transition is defined as a movement of a single vehicle between two consecutive road segments (links). In one transition, we have two links, origin and destination. It is important to mention that link length depends on the map that you use. For example, OpenStreetMap uses very short links (few meters), while other map providers define one link as all road segments between two junctions.

Examples of two transitions on the road network (Image by: Author)

2. Speed

The second step is the speed calculation. To construct an STM, we compute the speed of every vehicle that travels through the transition.

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Improve Linear Regression for Time Series Forecasting

Time series forecasting is a very fascinating task. However, build a machine-learning algorithm to predict future data is trickier than expected. The hardest thing to handle is the temporal dependency present in the data. By their nature, time-series data are subject to shifts. This may result in temporal drifts of various kinds which may become our algorithm inaccurate.

One of the best tips I recommend, when modeling a time series problem, is to stay simple. Most of the time the simpler solutions are the best ones in terms of accuracy and adaptability. They are also easier to maintain or embed and more persistent to possible data shifts.

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Automated Marketing Mix Modeling with Facebook Experimental’s Robyn

Two big questions every marketeer has are What’s the impact of my current marketing channels? and How should I allocate my budget strategically to get the optimal marketing mix?

These questions are not new. John Wanamaker (1838–1922), considered by some to be a pioneer in marketing had the same questions and is known for his famous and often cited quote:

Half my advertising spend is wasted; the trouble is, I don’t know which half.

To address these challenges econometricians developed multivariate regression techniques known as Marketing Mix Modeling (MMM). A very new tool in this area and currently in its beta version is Facebook’s Robyn.

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Bayesian Hierarchical Modeling in PyMC3

We can also plot only the 94% high-density intervals (HDI), i.e. short credible intervals containing 94% of the posterior mass in a single figure via

az.plot_forest(unpooled_trace, var_names=['slope'], combined=True)

We get

Image by the author.

You can see how groups 0 to 6 have small slopes, and group 7 being way out there. But this is totally wrong because all of our slopes should be around a value of two. What happened? Simple: we tricked the model by introducing outliers to the smallest group.

This is the exact problem I was talking about earlier: the sub-model for group 7 has no chance, as it does not know what is going on in the group 0 to 6.

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NLP Tutorial: Topic Modeling in Python with BerTopic

BerTopic is a topic modeling technique that uses transformers (BERT embeddings) and class-based TF-IDF to create dense clusters. It also allows you to easily interpret and visualize the topics generated. In this NLP tutorial, we will use Olympic Tokyo 2020 Tweets with a goal to create a model that can automatically categorize the tweets by their topics. The BerTopic algorithm contains 3 stages:Embed the textual data(documents) Embed the documents with BERT, or it can use any other embedding technique. The algorithm uses UMAP to reduce the dimensionality of embeddeddings and the HDBSCAN technique.

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Davis David Hacker Noon profile picture

@davisdavidDavis David

Data Scientist | AI Practitioner | Software Developer.

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