datapine

MAY 14, 2019

We gave you a curated list of our top 15 data analytics books , top 18 data visualization books , top 16 SQL books – and, as promised, we’re going to tell you all about the world’s best books on data science. 2) “Deep Learning” by Ian Goodfellow, Yoshua Bengio and Aaron Courville. click for book source**.

Data Science 350

Data Science Machine Learning Data-driven Big Data 350

datapine

JANUARY 6, 2022

Through the art of streamlined visual communication, data dashboards permit businesses to engage in real-time and informed decision-making and are key instruments in data interpretation. More often than not, it involves the use of statistical modeling such as standard deviation, mean and median. agree, strongly agree, disagree, etc.).

Visualization 246

Visualization Dashboards Cost-Benefit Measurement 246

Cloudera

FEBRUARY 25, 2021

Cloudera has a front-row seat to organizational challenges as those enterprises make Machine Learning a core part of their strategies and businesses. The work of a machine learning model developer is highly complex. We work with the largest companies in the world to help tackle their most challenging ML problems.

Deep Learning 82

Deep Learning Machine Learning Forecasting Visualization 82

Ontotext

JUNE 22, 2023

Recent statistics shed light on the realities in the world of current drug development: out of about 10,000 compounds that undergo clinical research, only 1 emerges successfully as an approved drug. The current process involves costly wet lab experiments, which are often performed multiple times to achieve statistically significant results.

Metrics 52

Metrics Statistics Visualization Metadata 52

Sisense

APRIL 10, 2020

R is a tool built by statisticians mainly for mathematics, statistics, research, and data analysis. It’s quite popular for its visualizations: charts, graphs, pictures, and various plots. These visualizations are useful for helping people visualize and understand trends , outliers, and patterns in data.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

FineReport

DECEMBER 9, 2021

Untracked metrics: data you will not track. This will be a subset of the metrics you track. BI visualization to analyze cost(by FineReport). Business Analyst: The person who provides expertise in data validation, processing, and visualization to other team members and who translates analysis into actionable insights.

Strategy 98

Strategy Business Intelligence KPI Key Performance Indicator 98

DataRobot Blog

MAY 26, 2022

Validating Modern Machine Learning (ML) Methods Prior to Productionization. Validating Machine Learning Models. Within DataRobot, each model created in the model leaderboard contains a feature impact visualization, which makes use of a mathematical technique called permutation importance to measure variable importance.

Risk 52

Risk Modeling Metrics Business Objectives 52

IBM Big Data Hub

AUGUST 11, 2023

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

Data Science 57

Data Science Machine Learning Cost-Benefit Deep Learning 57

DataRobot

APRIL 20, 2021

The performance of any machine learning model is tightly linked to the data it was trained on and validated against. This includes the basics: Computing summary statistics on each feature Measuring associations between features Observing feature distributions and their correlation with the predictive target Identifying outliers.

Measurement 52

Measurement Visualization Advertising Metrics 52

Smart Data Collective

OCTOBER 14, 2019

From artificial intelligence and machine learning to blockchains and data analytics, big data is everywhere. Machine Learning. Machine learning is a trending field and a hot topic right now. Statistics, qualitative analysis and quant are some of the backbones of big data. Big Data Skillsets.

Big Data 75

Big Data Software Data mining Prescriptive Analytics 75

BizAcuity

MAY 16, 2022

Fast food companies like Domino’s, McDonald’s and KFC collect massive amounts of data which includes customer data and other key business metrics for their own operations. Visual Analytics employs data mining to identify patterns and trends which would have been incredibly difficult to find without it. That was in the 1900’s.

Business Intelligence 52

Business Intelligence Data mining Visualization Data Lake 52

IBM Big Data Hub

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. What is machine learning?

Machine Learning 81

Machine Learning Modeling Deep Learning Predictive Modeling 81

Birst BI

AUGUST 23, 2018

A key goal of AI or machine learning automation is to have machines complete tasks for you, freeing up time so you can focus on the more complex, higher-value tasks. Data scientists building AI applications require numerous skills – data visualization, data cleansing, artificial intelligence algorithm selection and diagnostics.

Machine Learning 73

Machine Learning Data Science Testing Metrics 73

IBM Big Data Hub

AUGUST 7, 2023

AI algorithms have the potential to surpass traditional statistical approaches for analyzing comprehensive recruitment data and accurately forecasting enrollment rates. It can be integrated into real-time dashboards, visualizations, and reports that provide stakeholders with a comprehensive and up-to-date insight into site performance.

Optimization 59

Optimization Forecasting Data-driven Strategy 59

Business Over Broadway

MARCH 13, 2019

IBM is using the power of its Watson Studio platform to extend the power of AI to people who fall outside the realm of data science, machine learning and AI experts. Next, we can explore our data by calculating some descriptive statistics for our measures. To view the statistics, click on the Statistics Node and hit run.

Statistics 78

Statistics Measurement Descriptive Analytics Machine Learning 78

Domino Data Lab

JULY 28, 2021

Data science is a field at the convergence of statistics, computer science and business. Data science is an interdisciplinary field within STEM that combines math and statistics, computer science, and business acumen to find patterns in complex datasets that can be used to improve decision making. None of these techniques are new.

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

AWS Big Data

FEBRUARY 6, 2023

In this post, we dive deep into the tool, walking through all steps from log ingestion, transformation, visualization, and architecture design to calculate TCO. With QuickSight, you can visualize YARN log data and conduct analysis against the datasets generated by pre-built dashboard templates and a widget.

Dashboards 73

Dashboards Optimization Data Lake Cost-Benefit 73

Cloudera

JUNE 28, 2022

For each transaction, NiFi makes a call to a production model in Cloudera Machine Learning (CML) to score the fraud potential of the transaction. A dashboard feeds from the Kudu database to show fraud summary statistics. The streaming SQL job also saves the fraud detections to the Kudu database. Acquiring with Cloudera DataFlow.

Dashboards 81

Dashboards Machine Learning Statistics KPI 81

Data Science 101

OCTOBER 31, 2019

A definition from the book ‘Data Mining: Practical Machine Learning Tools and Techniques’, written by, Ian Witten and Eibe Frank describes Data mining as follows: “ Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. Regression.

Data mining 77

Data mining KDD Data Science Forecasting 77

Occam's Razor

JULY 24, 2018

work (collection, processing, reporting, analysis), processes, org structure, governance models, last-mile gaps , metrics ladders of awesomeness , and… so… much… more. Remember, tools, work, processes, org structure, governance models, last-mile gaps, metrics ladders of awesomeness, and… so… much… more. The Implications of Complexity.

Analytics 93

Analytics Metrics Strategy Measurement 93

Cloudera

JULY 18, 2022

We will also compute some summary statistics on the fly so that we can have a real-time dashboard of what is happening. For each transaction, NiFi makes a call to a production model in Cloudera Machine Learning (CML) to score the fraud potential of the transaction. Apache Flink.

Analytics 86

Analytics Dashboards Statistics Visualization 86

Domino Data Lab

DECEMBER 4, 2020

Having calculated AUC/AUMC, we can further derive a number of useful metrics like: Total clearance of the drug from plasma. We can now pass the preprocessed data to the Pumas NCAReport function, which calculates a wide range of relevant NCA metrics. We can merge all the metrics in a separate DataFrame for further analysis.

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

Domino Data Lab

JULY 22, 2019

why data governance, in the context of machine learning is no longer a “dry topic” and how the WSJ’s “global reckoning on data governance” is potentially connected to “premiums on leveraging data science teams for novel business cases”. He was saying this doesn’t belong just in statistics. It’s not going to happen.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Sisense

SEPTEMBER 25, 2020

A data scientist must be skilled in many arts: math and statistics, computer science, and domain knowledge. No matter your skill, career level, or title, the ability to analyze, organize, and visualize data are vital skills in our world of quickly growing and ever-changing data. Statistics and programming go hand in hand.

Statistics 98

Statistics Predictive Modeling Modeling Machine Learning 98

datapine

MARCH 25, 2019

Data analysis method focuses on strategic approaches to taking raw data, mining for insights that are relevant to the business’s primary goals, and drilling down into this information to transform metrics, facts, and figures into initiatives that benefit improvement. Conduct statistical analysis. Build a data management roadmap.

Key Performance Indicator 217

Key Performance Indicator Statistics Visualization Big Data 217

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

On the one hand, basic statistical models (e.g. On the other hand, sophisticated machine learning models are flexible in their form but not easy to control. Introduction Machine learning models often behave unpredictably, as data scientists would be the first to tell you.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

datapine

MARCH 25, 2022

While some experts try to underline that BA focuses, also, on predictive modeling and advanced statistics to evaluate what will happen in the future, BI is more focused on the present moment of data, making the decision based on current insights. But let’s see in more detail what experts say and how can we connect and differentiate the both.

Business Intelligence 264

Business Intelligence Analytics Statistics Dashboards 264

Sisense

JUNE 26, 2020

The human brain processes visual data better than any other kind of data, which is good because about 90% of the information our brains process is visual. Visual processing and responses both occur more quickly compared to other stimuli. The brain processes data in visuals or images faster than data in text or rows of numbers.

Visualization 97

Visualization Unstructured Data Measurement Data-driven 97

The Unofficial Google Data Science Blog

APRIL 23, 2024

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

datapine

SEPTEMBER 6, 2022

A content dashboard is an analytical tool that contains critical performance metrics to assess the success of all content-related initiatives. Modern dashboard software provides you with the necessary tools to visualize all your most important sources of information in a centralized location. Which we present below.

Dashboards 179

Dashboards Reporting ROI Metrics 179

DataRobot

NOVEMBER 9, 2021

Signal classification models are typically built using time series principles; traditionally used features that include central, windowed, lag, and lead statistics can do the job but sometimes there might be scenarios where we want to eke out more performance out of the data. This is evident from the activation maps provided by DataRobot.

Visualization 52

Visualization Unstructured Data Statistics Metrics 52

Smarten

APRIL 19, 2024

Anomaly Alerts KPI monitoring and Auto Insights allows business users to quickly establish KPIs and target metrics and identify the Key Influencers and variables for the target KPI.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

datapine

NOVEMBER 13, 2019

Fortunately, we live in a digital age rife with statistics, data, and insights that give us the power to spot potential issues and inefficiencies within the business. With the help of a dashboard designer , you can simply drag-and-drop your values and create interactive visualizations that will point out errors immediately.

Reporting 158

Reporting KPI Cost-Benefit Metrics 158

CIO Business Intelligence

JANUARY 20, 2023

But the benefits of BI extend beyond business decision-making, according to data visualization vendor Tableau , including the following: Data-driven business decisions: The ability to drive business decisions with data is the central benefit of BI.

Business Intelligence 141

Business Intelligence Dashboards Data mining OLAP 141

AWS Big Data

APRIL 28, 2023

AWS Glue is a serverless data integration service that makes it simple to discover, prepare, and combine data for analytics, machine learning (ML), and application development. For example, you can configure an Amazon EventBridge rule to invoke an AWS Lambda function to publish CloudWatch metrics every time AWS Glue jobs finish.

Optimization 94

Optimization Metrics Interactive Data Integration 94

Insight

MARCH 12, 2020

The most powerful approach for the first task is to use a ‘language model’ (LM), i.e. a statistical model of natural language. It is worth keeping in mind that the choice of threshold will impact downstream metrics like precision as well as shifting the size of our dataset. features) and metadata (i.e. 95 F1 scores across genres.

Modeling 59

Modeling Metadata Publishing Sales 59

Domino Data Lab

JUNE 23, 2019

Pete Skomoroch ’s “ Product Management for AI ”session at Rev provided a “crash course” on what product managers and leaders need to know about shipping machine learning (ML) projects and how to navigate key challenges. Be aware that machine learning often involves working on something that isn’t guaranteed to work.

Management 110

Management Machine Learning Experimentation Metrics 110