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Tag: dataanalytics

DSC Weekly Digest 12 October 2021

Build statistical and analytical expertise as well as the management and leadership skills necessary to implement high-level, data-driven decisions in Northwestern’s Online MS in Data Science. Earn your degree entirely online in classes that are led by industry experts who are redefining how data is used to boost efficiency and effectiveness in a wide range of fields. Learn more

Get to know TIBCO’s enterprise analytics platform that allows data scientists and business…… Read more...

How Can You Keep Your Analytics from Withering in the Transition Valley of Death? LTC Kristin Saling Explains

Between its active and reserve guard and a substantial civilian component, the U.S. Army’s total workforce stands at roughly between 1.2 and 1.4 million people. Now add in the fact that Army troops are stationed in roughly 800 military bases in more than 70 countries and territories around the world, and you can understand the organizational challenges this organization faces each day.

Hence, the Army formed their Data and Artificial Intelligence (AI) Team to…… Read more...

Almost Half of Organizations Still Struggle with the Quality of their Data

Nearly half (48%) of organizations are still struggling to use and access quality data as underlying technology is failing to deliver on a number of critical functions. According to new research conducted by ESG in partnership with InterSystems. Businesses are challenged with the diversity, scale, and distributed nature of data, but 56% of data leaders are using analytics tools to improve tech and employee performance survey of almost 400 business decision-makers finds

 …

Retail Predictive Analytics: Popular Use Cases

There was a time when brick-and-mortar stores were the only face of retail. The system worked well too and everyone seemed happy. That is until technology came along and, well, changed everything. Of course, the retail sector, as well as its customers, have benefitted immensely from such technologies. Case in point: Predictive analytics; as the industry evolves and adapts to a changing market and evolving customer demands, predictive analytics is helping retailers not only keep up but…… Read more...

How Low-Code Analytics Will Democratize Data

Click to learn more about author Dan Robinson.

I’d wager that most people who build websites today know very little HTML. That’s fine – most websites are built via low-code tools that hide code-level details behind GUIs. This lets website builders focus on creating products instead of worrying about CSS formatting. Web design has gone low-code, and other domains seem sure to follow. In fact, Gartner has predicted that low-code platforms will account for 65% of app…

https://www.dataversity.net/how-low-code-analytics-will-democratize-data/…

The Data Warehouse, the Data Lake, and the Future of Analytics

Data lakes were created in response to the need for Big Data analytics that has been largely unmet by data warehousing. The pendulum swing toward data lake technology provides some remarkable new capabilities, but can be problematic if the swing goes too far in the other direction. Far from being at the end of this evolutionary process, we are in the middle of it, said Anthony Algmin, CEO of Algmin Data Leadership, during his presentation titled Data Warehouse vs. Data Lake…… Read more...

Understanding the Deep Learning Landscape

Extending our previous theme Companies who had AI and Digital at their core have fared far bette…, in this post, we consider the approaches to AI from individual companies
Sometimes you see a picture and it says something which you always suspected but were not quite able to fully articulate
The image above is one example (source analytics India mag – link below)
In a nutshell, what it says is .. companies in the AI space are choosing their favourite deep learning technique and…

Continue reading: http://www.datasciencecentral.com/xn/detail/6448529:BlogPost:1070919

Source: www.datasciencecentral.com

Getting Started with Jupyter+IntelligentGraph

Since IntelligentGraph combines Knowledge Graphs with embedded data analytics, Jupyter is an obvious choice as a data analysts’ IntelligentGraph workbench.

The following are screen-captures of a Jupyter-Notebook session showing how Jupyter can be used as an IDE for IntelligentGraph to perform all of the following:

Create a new IntelligentGraph repository
Add nodes to that repository
Add calculation nodes to the same repository
Navigate through the calculated results
Query the results using SPARQL

GettingStarted is available as a JupyterNotebook here: GettingStarted JupyterNotebook

Images of the GettingStarted JupyterNotebook follow:


Using the Jupyter ISparql, we can easily perform SPARQL queries over the same IntelligentGraph created above.… Read more...

Why Dynamic Algorithms Still Haven’t Replaced Human Rules

The general perception among data-centric organizations is that data management technology is progressing linearly. Cloud warehouses, for example, are generally deemed superior to on-premise relational ones. Kubernetes’ portability is viewed as more utilitarian than monolithic ERP systems are, and dynamic algorithms that improve over time are considered the successor to static, human made rules—especially for analytics.

The rationale for the purported triumph of machine learning’s aptitude over that of human devised rules is relatively simple and, for the most part, convincing. “Most importantly, on a fundamental level, rules are by definition backwards looking,” posited Forter COO Colin Sims. “You write a rule based on something you know that happened, and then you’re assuming that more is going to happen based on the past.”… Read more...

Calculating the Distance between Two Locations Using Geocodes

PYTHON. GEOSPATIAL ANALYTICS. LOCATION DATA.How to use Python to calculate the distance between two sets of geocodesPhoto by Tamas Tuzes-Katai on Unsplash

You’ve heard the famous phrase “Location, Location, Location” for instances where people want to emphasize the centrality of the location to business and real estate.

In data analysis and computing, however, this phrase is a bit ambiguous. The way computers understand the concept of “location” is through what we know as “geocodes.” These are the longitudes and latitudes and are specific to a particular location.

Note: For those who want to know how to calculate these geocodes, I have written an article regarding this.Read more...

Spark Troubleshooting, Part 1 – Ten Challenges

“The most difficult thing is finding out why your job is failing, which parameters to change. Most of the time, it’s OOM errors…” Jagat Singh, Quora

Spark has become one of the most important tools for processing data – especially non-relational  data – and deriving value from it. And Spark serves as a platform for the creation and delivery of analytics, AI, and machine learning applications, among others. But troubleshooting Spark applications is hard – and we’re here to help.

In this blog post, we’ll describe ten challenges that arise frequently in troubleshooting Spark applications. We’ll start with issues at the job level, encountered by most people on the data team – operations people/administrators, data engineers, and data scientists, as well as analysts.… Read more...

Institutional Investors Hold the Key to Startups’ Applied AI Success

A lot of ink—and keyboard strokes—have been dedicated to how the pandemic has accelerated the move to the cloud and the application of AI in a vast array of business contexts. AI is transforming every industry you can think of as businesses figure out ways to support remote work, automate business processes, and deliver customer value without requiring in-person interaction. In one sense, AI tools have been democratized; resources and tool-kits are readily available to any company looking to innovate. But as much as it may seem obvious that organizations need to apply AI to their business, execution isn’t so easy.… Read more...

How to Transfer Fundamental AI Advances into Practical Solutions for Healthcare

In this special guest feature, Dave DeCaprio, CTO and Co-founder, ClosedLoop.ai, discusses what it really takes to make AI that physicians trust. Dave has more than 20 years of experience transitioning advanced technology from academic research labs into successful businesses. His experience includes genome research, pharmaceutical development, health insurance, computer vision, sports analytics, speech recognition, transportation logistics, operations research, real time collaboration,…

Continue reading: https://insidebigdata.com/2021/09/29/how-to-transfer-fundamental-ai-advances-into-practical-solutions-for-healthcare/

Source: insidebigdata.com

🎙 Judah Phillips / Squark about No-Code Predictive Analytics

Getting to know the experience gained by researchers, engineers, and entrepreneurs doing real ML work is an excellent source of insight and inspiration. Share this interview if you find it enriching. No subscription is needed.Share👤 Intro / Judah PhillipsTell us a bit about yourself. Your background, current role and how did you get started in machine learning? Judah Phillips (JP): I’m an entrepreneur who started working in software in the late ’90s. When the dotcom bubble burst,…… Read more...

DSC Weekly Digest 28 September 2021

  • The growth of self-service BI is driving organizations to create data literacy programs to ensure that business users have the data knowledge and skills they need to understand data, work with it to generate useful information and communicate the analytics results to others. Check out the Search Business Analytics e-Handbook How to develop a data literacy program in your organization for an in depth look at why investments in data literacy frameworks can help improve data quality and integrity.
  • Constantly changing digital workflows are hastening acceptability and powering the escalation of automated document management system deployments. Read the Search Content Management e-handbook Automated document management system tools transform workflows to learn about why DMS are driving the hybrid workforce, and get insight about the tools, features and applications to consider when choosing the right DMS for your organization.

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

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

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.


Introducing PostHog: An open-source product analytics platform – KDnuggets

PostHog is an open-source product analytics platform that helps you and your product team capture, analyze, and make informed decisions based on user behaviour.

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An all-in-one platform

PostHog offers a suite of product analysis tools, including funnels, heat maps, session recording and more, all in a single platform. This enables data and engineering teams to get information faster, without writing any SQL, while teams who prefer to avoid directly manipulating data are able to self-serve and get answers without needing support.

Find out more about the PostHog features and functionality.

Posthog Dashboard Example

Retain control & compliance

PostHog is the only analytics platform that enables users to self-host on their own infrastructure.


Messy Data is Beautiful


Once these types of data have been cleaned, they do more than show organized data sets. They reveal unlimited possibilities, and AI analytics can reveal these possibilities faster and more efficiently than ever before.

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Data scientists have always been expected to curate data into ‘aha’ moments and tell stories that can reach a wider business audience. But what is the cost of this curation?

The real signal is in the noise

Tidy data doesn’t help that much.

Every aggregation and pivot performed on datasets reduces the total amount of information available to analyze. That clever NLP topic mining on free text fields was no doubt very useful, but the raw text is more interesting.


Free virtual event: Big Data and AI Toronto

This year’s Big Data and AI Toronto conference and expo, held virtually Oct 13-14, will provide attendees with a 360° view of the industry through a unique 4-in-1 experience: Artificial intelligence, big data, cloud, and cybersecurity.

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Since 2016, Big Data and AI Toronto has been providing a unique platform for IT decision-makers and data innovators to explore and discuss insights, showcase the latest innovative projects, and connect with other data and analytics professionals.

This year’s conference and expo, held virtually on October 13-14, 2021, will provide attendees with a 360° view of the industry through a unique 4-in-1 experience: Artificial intelligence, big data, cloud, and cybersecurity.


Data Engineering Technologies 2021


By Tech Ninja, OpenSource, Analytics & Cloud enthusiast.

A partial list of top engineering technologies, image created by KDnuggets.

Complete curated list of emerging technologies in Data Engineering

  • Abacus AI, enterprise AI with AutoML, similar space to DataRobot.
  • Algorithmia, enterprise MLOps.
  • Amundsen, an open-sourced data discovery and metadata engine.
  • Anodot, monitors all your data in real-time for lightning-fast detection of incidents.
  • Apache Arrow, essential because of non-JVM, in-memory, columnar format and vectorized.
  • Apache Calcite, framework for building SQL databases and data management systems without owning data. Hive, Flink, and others use Calcite.

Features are the New Data

In my prior blog “Reframing Data Management: Data Management 2.0”, I talked about the importance of transforming data management into a business strategy that supports the sharing, re-using and continuous refinement of the data and analytics assets to derive and drive new sources of customer, product, and operational value. If data is “the world’s most valuable resource”, then we must transform data management into an offensive, “data monetization” business strategy that proactively guides organizations in the application of their data to the business to drive quantifiable financial impact (Figure 1).

Figure 1: Activating Data Management

In this blog I want to drill into the importance of Machine Learning (ML) “Features”.


How to label time series efficiently – and boost your AI

Data labeling is a critical step in building high-quality AI models. This blog explains how to speed up the labeling process of time series data from sensors and IoT devices.

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The increasing digitization of machines and production processes opens up many exciting possibilities, ranging from early fault detection to usage-based pricing. Such applications build on real-time analytics of sensor data for identifying different states of machine operation.

In order to make a machine learning algorithm recognize meaningful operation states from sensor data, the information about the states must be explicitly available for sensor data from the past. For example, training an AI model requires information such as: State A occurred from 3:10 to 3:17 and from 5:23 to 5:35, State B occurred from 7:28 to 8:11.


How to be a Data Scientist without a STEM degree

1. Learn the fundamentals of all pillars of data science

“Data Science” is a vague term—it can mean different things to different companies, and there are a plethora of skills that are relevant to data scientists.

That being said, there are a few core skills that I recommend that you learn. The following skills are pivotal for any data scientist: SQL, Python, Statistics, Machine Learning. I also recommend that you learn these skills in that order. It may sound like a lot, but it’s no different than when you had to complete 4–6 courses per semester in college!