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

Domino Data Lab

AUGUST 12, 2021

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. It enables us to build context around the data at hand and lets us develop appropriate models that then can be interpreted correctly. The purpose of Data Exploration.

Machine Learning 126

Machine Learning Reporting Statistics Visualization 126

IBM Big Data Hub

DECEMBER 20, 2023

Instead of using explicit instructions for performance optimization, ML models rely on algorithms and statistical models that deploy tasks based on data patterns and inferences. In other words, ML leverages input data to predict outputs, continuously updating outputs as new data becomes available.

Machine Learning 77

Machine Learning Modeling Deep Learning Predictive Modeling 77

DataRobot Blog

DECEMBER 1, 2022

For example, you could set up a data pipeline that delivers DataRobot predictions to HubSpot to automatically initiate offers within the business rules you set. You could also use the predictions to visualize a BI dashboard or report for your marketing managers to access. Training and Testing Different AI Models.

Machine Learning 52

Machine Learning Marketing Modeling Statistics 52

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. The describe function on a Pandas DataFrame provides descriptive statistics, including the number of columns, in this case 27, and median (this is the 50 percent row), for each column. 05) in predicting changes in attendance.

Statistics 59

Statistics Machine Learning Testing Modeling 59

Darkhorse

JULY 6, 2017

Let’s further pretend you’re starting out with the aim of doing a big predictive modeling thing using machine learning. Once you’ve documented and understood your sources, you have reached the second stage: data sloughs (note, you have more than one). Proceed visually. I hope you enjoy that sort of thing.

Data Lake 74

Data Lake Sales Machine Learning Visualization 74

Domino Data Lab

AUGUST 20, 2019

The two major ways of rescaling are either by changing on a fixed scale or changing the values with respect to some statistic computed from the data. Standardization , a statistical rescaling, is a bit trickier. Visually, the shape has relatively flat sides. Often, rescaling will also result in recentered data. Discretization.

Testing 68

Testing Modeling Interactive Measurement 68

Domino Data Lab

JULY 9, 2019

Diving into examples of building and deploying ML models at The New York Times including the descriptive topic modeling-oriented Readerscope (audience insights engine), a prediction model regarding who was likely to subscribe/cancel their subscription, as well as prescriptive example via recommendations of highly curated editorial content.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

Domino Data Lab

AUGUST 22, 2019

There are steps you can take to preprocess natural language data such that the modeling you carry out downstream may be more accurate. Common natural language preprocessing options include: Tokenization: This is the splitting of a document (e.g., Execute gutenberg.fileids() to print the names of all 18 documents.) Example 11.6

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

datapine

NOVEMBER 27, 2019

Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards. Companies are no longer wondering if data visualizations improve analyses but what is the best way to tell each data-story. 2) Data Discovery/Visualization. Data exploded and became big.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

Jet Global

MAY 1, 2023

Plus, there is an expectation that tools be visually appealing to boot. In the past, data visualizations were a powerful way to differentiate a software application. Their dashboards were visually stunning. Today, free visualizations seem to be everywhere. It’s all about context. End users expect more from analytics too.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52