Sisense

APRIL 10, 2020

Text data is proliferating at a staggering rate, and only advanced coding languages like Python and R will be able to pull insights out of these datasets at scale. R or Python?”. People looking into data science languages are usually confused about which language they should learn first: R or Python. Python: Versatile workhorse.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

IBM Big Data Hub

NOVEMBER 29, 2023

With the emergence of new advances and applications in machine learning models and artificial intelligence, including generative AI, generative adversarial networks, computer vision and transformers, many businesses are seeking to address their most pressing real-world data challenges using both types of synthetic data: structured and unstructured.

Metrics 88

Metrics Machine Learning Risk Statistics 88

Sisense

APRIL 10, 2020

In this article, we discuss how this data is accessed, an example environment and set-up to be used for data processing, sample lines of Python code to show the simplicity of data transformations using Pandas and how this simple architecture can enable you to unlock new insights from this data yourself. Let’s get started.

Visualization 105

Visualization Reporting Data Processing Dashboards 105

DataRobot Blog

FEBRUARY 28, 2023

While this can be an excellent strategy for a future-oriented company, it can prove futile if you don’t maximize the value of your investment. We recently announced DataRobot’s new Hosted Notebooks capability. According to Flexera 1 , 92% of enterprises have a multi-cloud strategy, while 80% have a hybrid cloud strategy.

Data Processing 72

Data Processing Experimentation Machine Learning Modeling 72

IBM Big Data Hub

SEPTEMBER 19, 2023

Your skill set should include the ability to write in the programming languages Python, SAS, R and Scala. Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts.

Data Science 84

Data Science Data Analytics Prescriptive Analytics Analytics 84

Cloudera

JANUARY 28, 2021

Developers create a new branch of this git project, then update the source_change_request.json file to contain the changed flows and versions. Developers create a new branch of this git project, then update the source_change_request.json file to contain the changed flows and versions. DataFlow Continuous Delivery Architecture.

Management 67

Management Testing IT 67

Ontotext

DECEMBER 2, 2021

In our previous blog posts of the series, we talked about how to ingest data from different sources into GraphDB , validate it and infer new knowledge from the extant facts as well as how to adapt and scale our basic solution. And the LAZY system from our previous blog posts is at the threshold of that important step.

Visualization 98

Visualization Interactive Dashboards Enterprise 98

DataRobot Blog

JULY 28, 2022

That commonality is that low-cost, high-accuracy solution methods, or approaches of excellence, can have a significant impact on results. In this blog post, we would like to present some examples of actual cases in which noise reduction had a significant effect in real-world applications, and in which powerful features were obtained.

Data Science 72

Data Science Machine Learning Statistics Modeling 72

Cloudera

FEBRUARY 9, 2021

If you are new to Cloudera Operational Database, see this blog post. In this blog post, we’ll look at both Apache HBase and Apache Phoenix concepts relevant to developing applications for Cloudera Operational Database. Use the Python driver for Apache Phoenix. And, check out the documentation here. .

Interactive 101

Interactive Machine Learning Data Science IT 101

DataKitchen

JANUARY 3, 2022

DataOps consolidates processes and workflows into a process hub that curates and manages the workflows that drive the creation of analytics. A DataOps process hub offers a way for business analytics teams to cope with fast-paced requirements without expanding staff or sacrificing quality. Analytics Hub and Spoke. Business Analytic Challenges.

Business Analytics 245

Business Analytics Analytics Testing Dashboards 245

Alation

NOVEMBER 28, 2022

introduces Alation Connected Sheets, a new product under Alation Cloud Service that empowers spreadsheet users with access to trusted data. This means that business users can reduce their reliance on IT and data teams for access to data, all without having to learn a new tool. billion people use Excel globally. Let’s dive in.

Metadata 52

Metadata Enterprise Cost-Benefit Finance 52

AWS Big Data

JUNE 29, 2023

Tracking such user queries as part of the centralized governance of the data warehouse helps stakeholders understand potential risks and take prompt action to mitigate them following the operational excellence pillar of the AWS Data Analytics Lens. The subscription filter triggers an AWS Lambda function for pattern matching.

Data Warehouse 60

Data Warehouse Dashboards Testing Visualization 60

Domino Data Lab

JANUARY 6, 2019

Lately I’ve been developing curriculum for a client for their new “Intro to Data Science” sequence of courses. This is not a new gig, by any stretch. What’s a reasonably good persona to target for employees who want training in data science to shift into new roles? Introduction. What are the foundational parts of our field?”.

Data Science 56

Data Science Machine Learning Reporting Visualization 56

Insight

JANUARY 14, 2020

Will a network trained with fake data be able to generalize to the real world? Lauren Holzbauer was an Insight Fellow in Summer 2018. Today, I don’t think twice about walking into any gym, assessing the equipment, and throwing a really good workout together, but it hasn’t always been that way. Take a closer look at the three benchpress images above.

Metrics 58

Metrics Optimization Modeling Testing 58

Domino Data Lab

JULY 22, 2019

This blog post provides a concise session summary, a video, and a written transcript. Paco Nathan presented, “Data Science, Past & Future” , at Rev. Session Summary. Nathan also provided excerpts and insights from recent surveys to help provide additional context. Key highlights from the session include. Transcript.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Data Science 101

NOVEMBER 8, 2023

New systems, new platforms, new processes. You are welcome to follow along and let me know on Threads if you would like to be interviewed. Note: The views in these interviews are personal views and do not represent the interviewee’s employer. Thanks so much for having me, I’m very excited for this! Nothing is static!

Data Science 59

Data Science Visualization Management Data Processing 59

DataKitchen

JULY 27, 2023

The Ten Standard Tools To Develop Data Pipelines In Microsoft Azure. While working in Azure with our customers, we have noticed several standard Azure tools people use to develop data pipelines and ETL or ELT processes. We counted ten ‘standard’ ways to transform and set up batch data pipelines in Microsoft Azure. Is it overkill? It does the job.

Machine Learning 130

Machine Learning Cost-Benefit Data Transformation Testing 130

DataKitchen

OCTOBER 11, 2021

First, a business analyst consolidates data from some public websites, an SFTP server and some downloaded email attachments, all into Excel. First, you must understand the existing challenges of the data team, including the data architecture and end-to-end toolchain. Second, you must establish a definition of “done.” List of Challenges.

Testing 157

Testing Metadata Dashboards Statistics 157

O'Reilly on Data

JULY 25, 2023

So far I’ve read a gazillion blog posts about people’s experiences with these AI coding assistance tools. For instance, problems like “write a Python function that takes a list of names, splits them by first and last name, and sorts by last name.” or “ha look how incompetent it is … it couldn’t even get my simple question right!”

Consulting 246

Consulting Interactive Software IT 246

Cloudera

NOVEMBER 3, 2021

While deep learning is an excellent use of the processing power of a graphics card, it is not the only use. If your data is very unlikely to ever reach a row count in the millions, you can probably disregard this blog (that is, unless you want to learn something new). Today, deep learning and GPUs are practically synonymous.

Deep Learning 115

Deep Learning Machine Learning Data Science Cost-Benefit 115

Domino Data Lab

JANUARY 21, 2021

This blog explores the challenges associated with doing such work manually, discusses the benefits of using Pandas Profiling software to automate and standardize the process, and touches on the limitations of such tools in their ability to completely subsume the core tasks required of data science professionals and statistical researchers.

Statistics 106

Statistics Unstructured Data Data Science Predictive Modeling 106

Domino Data Lab

AUGUST 12, 2021

We all have heard how data is the new oil. In a previous blog , we have covered how Pandas Profiling can supercharge the data exploration required to bring our data into a predictive modelling phase. Python and Pandas Profiling. The purpose of Data Exploration. Determining how much missing data there is.

Machine Learning 126

Machine Learning Reporting Statistics Visualization 126

Domino Data Lab

FEBRUARY 12, 2020

Dean Wampler provides a distilled overview of Ray, an open source system for scaling Python systems from single machines to large clusters. If you’re wondering if Ray should be part of your technical strategy for Python-based applications, especially ML and AI, this post is for you. this post on the Ray project blog ?.

Experimentation 137

Experimentation Modeling Data Science Management 137

datapine

SEPTEMBER 16, 2022

Successful visualizations are aesthetically beautiful, providing layers of detail that generate deeper dimensions of insight and whole new layers of understanding. Successful visualizations are aesthetically beautiful, providing layers of detail that generate deeper dimensions of insight and whole new layers of understanding.

Visualization 304

Visualization Dashboards Data-driven Statistics 304

Sisense

JANUARY 15, 2021

Big data is now modeled and queried using advanced coding languages like SQL, Python, and R. And rather than answering prescriptive questions — something that BI teams excel at — data teams are able to model future events and understand how changing a past variable could have affected the present. We live in a world driven by data.

Analytics 75

Analytics Data-driven Business Intelligence Visualization 75

BizAcuity

MAY 10, 2022

And how this transformation will impact businesses in the short and long run is the main discussion in this blog. Cloud technology and innovation drives data-driven decision making culture in any organization. It is the epitome of modern technology right now with multi-dimensional innovations shaping every layer.

Data-driven 52

Data-driven IoT Unstructured Data Data Lake 52

Sisense

APRIL 13, 2020

In Navigating Change in Crisis, we explore how individuals and companies are adapting to a “new normal” to keep essential services functioning. Or perhaps instead they prefer to hone their skills in SQL , Python, or R. These insights aim to help you and your team navigate these unprecedented times.

Uncertainty 83

Uncertainty Marketing Digital Transformation Forecasting 83

Domino Data Lab

JUNE 15, 2021

Tracking and managing a small number of experiments that deal with data in Excel is fairly straightforward. Snowpark is a new developer experience, which enables data scientists to leverage their favourite tools and deploy code directly within Snowflake. The challenges surface once the company hits the scalability wall.

Modeling 69

Modeling Data Science Data-driven Data Warehouse 69

Sisense

FEBRUARY 24, 2021

Whatever a company does, how it uses data is a key differentiator in its success or failure. Whether that data is generated internally or gathered from an external application used by customers, organizations now use on-demand cloud computing resources to make sense of the data, discover trends, and make intelligent forecasts.

Data Warehouse 69

Data Warehouse Big Data Metrics Visualization 69

Smart Data Collective

OCTOBER 14, 2019

When Google talked about releasing this tool in its blog, the brand pointed out that if you don’t protect user data, you risk losing people’s trust. Here are some open-source options to consider. Ludwig is a tool that allows people to build data-based deep learning models to make predictions. Kubernetes.

Data Science 67

Data Science Machine Learning Big Data Interactive 67

Cloudera

OCTOBER 17, 2017

Deep learning is in the news. In this blog, we provide a few examples that show how organizations put deep learning to work. Cloudera offers multiple ways to train and deploy deep learning models, without new silos or data movement. It’s changing the game. It’s changing your life. It’s changing everything.

Deep Learning 46

Deep Learning Data Science Machine Learning Modeling 46

Insight

DECEMBER 30, 2019

Wikipedia In our last blog , we walked through a super fast introduction of “vanilla” flavored neural networks. These partially connected layers are what make the CNN such an excellent tool for working with images. Lauren Holzbauer was an Insight Fellow in Summer 2018. Let’s dig in! Original image is on the left.

Visualization 72

Visualization Modeling Machine Learning IT 72

Ontotext

OCTOBER 7, 2020

From its roots in an Excel workbook known as the Semantic Repository, FIBO has grown into a sophisticated ontology based upon RDF and OWL. An ontology or a knowledge graph of any appreciable size requires some effort on the part of the consumer before it becomes a useful tool. FIBO Overview. FIBO is specified using RDF(S) and OWL.

Finance 59

Finance Visualization Modeling Optimization 59

Darkhorse

MAY 8, 2014

Java Excellent, now just run it all through Conveyal’s conveniently open source tool. Python Wrestling We use python at Darkhorse, and learning some geographic libraries could be useful. Python Wrestling We use python at Darkhorse, and learning some geographic libraries could be useful. How naive I was. installing.

IT 70

IT Visualization Analytics 70

Alation

FEBRUARY 17, 2023

Attendance was high, as were the number of excellent questions. In this blog, I’ll address some of the questions we did not have time to answer live, pulling from both Dr. Can you have proper data management without establishing a formal data governance program? Yes, it is possible to have data management without data governance.

Data Governance 52

Data Governance Data Quality Metadata Cost-Benefit 52

Sisense

JULY 28, 2019

In the ‘80s, Business Intelligence was created, which gave companies a brand new way to look at information, even if it was limited to a small set of specific information in those databases. This combination of databases and BI was so valuable that it remained relatively unchanged for most of the next 20 years. What’s possible today.

Statistics 75

Statistics Machine Learning Business Intelligence Measurement 75

Domino Data Lab

OCTOBER 14, 2020

We’ve developed a model-driven software platform, called Climate FieldView , that captures, visualizes, and analyzes a vast array of data for farmers and provides new insight and personalized recommendations to maximize crop yield. The introduction of machine learning to the agricultural domain is relatively new.

Experimentation 99

Experimentation Modeling Deep Learning Testing 99