Domino Data Lab

AUGUST 12, 2021

In this blog we will continue our discussion about data exploration with Pandas Profiler and include another tool that may come handy for those of us that may be more inclined to use visual clues, D-Tale. The purpose of Data Exploration. Data exploration is a very important step before jumping onto the machine learning wagon.

Machine Learning 126

Machine Learning Reporting Statistics Visualization 126

FineReport

JUNE 26, 2023

These reports commonly incorporate graphical elements such as charts, graphs, tables, and statistics, which complement the text-based information and offer visual representation. Serving as foundational documents : Performance reports serve as the groundwork for other reports and documentation.

Reporting 52

Reporting Key Performance Indicator Sales Visualization 52

DataRobot Blog

DECEMBER 1, 2022

Artificial intelligence (AI) can help improve the response rate on your coupon offers by letting you consider the unique characteristics and wide array of data collected online and offline of each customer and presenting them with the most attractive offers. How Can AI Target the Right Prospects with Sharper Personalization?

Machine Learning 52

Machine Learning Marketing Modeling Statistics 52

FineReport

JUNE 24, 2022

Analysis reports are constantly required documents in almost every company. In the digital age, analytical reporting seems to be more indispensable but challenging because of the data explosion. However, sometimes we may find some inconveniences in the process of data collection and data visualization.

Reporting 52

Reporting IT Sales Recreation/Entertainment 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. In the real world, things are never this easy, and it is a battle to get the data into the correct shape as we will see later in this chapter. shows the output in Jupyter Notebook of the describe command. In Figure 6.8,

Statistics 59

Statistics Machine Learning Testing Modeling 59

datapine

AUGUST 29, 2022

Most people are aware that companies collect our GPS locale, text messages, credit card purchases, social media posts, Google search history, etc., and this book will give you an insight into their data collecting procedures and the reasons behind them. Khan Analytic Philosophy: A Very Short Introduction by Michael Beaney.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Darkhorse

JULY 6, 2017

Once you’ve documented and understood your sources, you have reached the second stage: data sloughs (note, you have more than one). Now comes the fun part: cleaning up your data. This means getting rid of the duplicates, dealing with the blanks, and fixing data type problems. Proceed visually. By human or machine?)

Data Lake 74

Data Lake Sales Machine Learning Visualization 74

CIO Business Intelligence

FEBRUARY 3, 2023

Optical character recognition, for example, might extract a purchase order from an uploaded document image and trigger accounting software to deal with it. The ability to suck words and numbers from images are a big help for document-heavy businesses such as insurance or banking.

Data-driven 145

Data-driven Interactive Enterprise Statistics 145

Depict Data Studio

MAY 8, 2023

All of these classes fall under the broad data communications umbrella. One class focuses on data analysis (cleaning and tabulating our raw datasets to get them ready for graphs). Davey Strategies “I am a university researcher and have a lot of familiarity with data collection and statistical analysis programs/platforms (e.g.

Dashboards 52

Dashboards Visualization Reporting Interactive 52

Domino Data Lab

AUGUST 20, 2019

Real-world datasets can be missing values due to the difficulty of collecting complete datasets and because of errors in the data collection process. Recentering the data means that we translate the values so that the extremes are different and the intermediate values are moved in some consistent way. Discretization.

Testing 68

Testing Modeling Interactive Measurement 68

datapine

APRIL 16, 2019

As a direct result, less IT support is required to produce reports, trends, visualizations, and insights that facilitate the data decision making process. From these developments, data science was born (or at least, it evolved in a huge way) – a discipline where hacking skills and statistics meet niche expertise.

Data-driven 287

Data-driven Dashboards Visualization Cost-Benefit 287

DataKitchen

JANUARY 23, 2023

Data testing is an essential aspect of DataOps Observability; it helps to ensure that data is accurate, complete, and consistent with its specifications, documentation, and end-user requirements. Data testing can be done through various methods, such as data profiling, Statistical Process Control, and quality checks.

Testing 130

Testing Data Governance Data Quality Data-driven 130

Jet Global

MAY 1, 2023

This is in contrast to traditional BI, which extracts insight from data outside of the app. We rely on increasingly mobile technology to comb through massive amounts of data and solve high-value problems. Plus, there is an expectation that tools be visually appealing to boot. Their dashboards were visually stunning.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52

Jet Global

NOVEMBER 2, 2021

Those without KPIs are left without any valuable statistics, while those with established performance tracking dashboards are able to make data driven decisions. Location Rates: It is very interesting to visualize where all your students are coming from. Effective Data Collection. Accurate and Realistic Goals.

Metrics 52

Metrics Reporting KPI Key Performance Indicator 52