FineReport

MARCH 24, 2024

Data Analyst Job Description: Major Tasks and Duties Data analysts collaborate with management to prioritize information needs, collect and interpret business-critical data, and report findings. Each language serves distinct purposes, from performance-oriented applications to web development and data science.

Statistics 52

Statistics Data mining Visualization Reporting 52

O'Reilly on Data

JULY 28, 2020

There’s a substantial literature about ethics, data, and AI, so rather than repeat that discussion, we’ll leave you with a few resources. Ethics and Data Science is a short book that helps developers think through data problems, and includes a checklist that team members should revisit throughout the process.

Marketing 362

Marketing Experimentation Metrics Testing 362

Sisense

OCTOBER 29, 2020

“Because AVs collect data in public where there is little ‘reasonable expectation of privacy’, they are not subject to many of the privacy laws in the U.S. The data collected by AVs in the U.S. will likely be owned by the collector of the data, not the data subject. and abroad,” she explained. Advertising?

Machine Learning 90

Machine Learning Data Collection Cost-Benefit Advertising 90

erwin

JUNE 11, 2020

How Business Benefits from Data Intelligence. Traditional business models and processes can be detrimental to today’s evolving data-driven society. Businesses are then introduced to modern data science and data intelligence tools to enhance and fine-tune their products and processes. Data quality management.

Key Performance Indicator 103

Key Performance Indicator KPI Data-driven Machine Learning 103

Sisense

SEPTEMBER 3, 2020

Programming and statistics are two fundamental technical skills for data analysts, as well as data wrangling and data visualization. Data analysts in one organization might be called data scientists or statisticians in another.

Statistics 97

Statistics Metrics Visualization Finance 97

Peter James Thomas

AUGUST 29, 2018

My role encompasses being the business driver for the data platform that we are rolling out across the organisation and its success in terms of the data going onto the platform and the curation of that data in a governed state, depending on the consumer requirements. Data democratisation is enabled by the data platform.

Data-driven 61

Data-driven Cost-Benefit Metadata Technology 61

Cloudera

OCTOBER 1, 2022

While the word “data” has been common since the 1940s, managing data’s growth, current use, and regulation is a relatively new frontier. . Governments and enterprises are working hard today to figure out the structures and regulations needed around data collection and use. Infrastructure.

Data Governance 71

Data Governance Strategy Enterprise Machine Learning 71

AWS Big Data

MARCH 26, 2024

A Gartner Marketing survey found only 14% of organizations have successfully implemented a C360 solution, due to lack of consensus on what a 360-degree view means, challenges with data quality, and lack of cross-functional governance structure for customer data. This is aligned to the five pillars we discuss in this post.

Data Strategy 68

Data Strategy Strategy Data Warehouse Prescriptive Analytics 68

O'Reilly on Data

MARCH 18, 2020

By contrast, AI adopters are about one-third more likely to cite problems with missing or inconsistent data. The logic in this case partakes of garbage-in, garbage out : data scientists and ML engineers need quality data to train their models. This is consistent with the results of our data quality survey.

Enterprise 313

Enterprise Deep Learning Data Governance Risk 313

Cloudera

AUGUST 30, 2022

For state and local agencies, data silos create compounding problems: Inaccessible or hard-to-access data creates barriers to data-driven decision making. Legacy data sharing involves proliferating copies of data, creating data management, and security challenges. Towards Data Science ).

Data Architecture 75

Data Architecture Data Lake Metadata Data Warehouse 75

CIO Business Intelligence

NOVEMBER 28, 2023

“By recognizing milestones, leaders give other stakeholders visibility into the progress being made, and also ensure that their team members feel appreciated for the level of effort they are putting in to make unstructured data actionable.” Quality is job one. Another key to success is to prioritize data quality.

Unstructured Data 130

Unstructured Data Data-driven Visualization Data Quality 130

Domino Data Lab

FEBRUARY 4, 2019

In Paco Nathan ‘s latest column, he explores the role of curiosity in data science work as well as Rev 2 , an upcoming summit for data science leaders. Welcome back to our monthly series about data science. and dig into details about where science meets rhetoric in data science.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

datapine

AUGUST 29, 2022

Best for : the new intern who has no idea what data science even means. An excerpt from a rave review : “I would definitely recommend this book to everyone interested in learning about data from scratch and would say it is the finest resource available among all other Big Data Analytics books.”.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Domino Data Lab

MARCH 3, 2019

Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise. Welcome back to our monthly series about data science! This month, the theme is not specifically about conference summaries; rather, it’s about a set of follow-up surveys from Strata Data attendees.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Andrew White

JANUARY 9, 2022

This research does not tell you where to do the work; it is meant to provide the questions to ask in order to work out where to target the work, spanning reporting/analytics (classic), advanced analytics and data science (lab), data management and infrastructure, and D&A governance. We write about data and analytics.

Analytics 64

Analytics Measurement Modeling Data-driven 64

FineReport

JULY 18, 2022

Most data analysts are very familiar with Excel because of its simple operation and powerful data collection, storage, and analysis. Key features: Excel has basic features such as data calculation which is suitable for simple data analysis. RapidMiner. From RapidMiner. From KNIME. Apache Spark.

Forecasting 52

Forecasting Dashboards Visualization Statistics 52

Domino Data Lab

JUNE 23, 2019

They have the foundations of data infrastructure. I didn’t mention in this talk, but there’s a great diagram from Monica Rogati, the Data Science Pyramid of Needs, with a lot of foundational infrastructure and logging, feature extraction, all those things that you need to do to make these models work.

Management 110

Management Machine Learning Experimentation Metrics 110

The Unofficial Google Data Science Blog

JULY 18, 2023

Measurement challenges Assessing reliability is essentially a process of data collection and analysis. To do this, we collect multiple measurements for each unit of observation, and we determine if these measurements are closely related. In this case, the scale is not measuring the construct that interests us.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

datapine

FEBRUARY 22, 2022

Over the past 5 years, big data and BI became more than just data science buzzwords. Without real-time insight into their data, businesses remain reactive, miss strategic growth opportunities, lose their competitive edge, fail to take advantage of cost savings options, don’t ensure customer satisfaction… the list goes on.

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Key Performance Indicator 218

datapine

JANUARY 31, 2022

As Dan Jeavons Data Science Manager at Shell stated: “what we try to do is to think about minimal viable products that are going to have a significant business impact immediately and use that to inform the KPIs that really matter to the business”.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Rocket-Powered Data Science

MARCH 20, 2023

We live in a data-rich, insights-rich, and content-rich world. Data collections are the ones and zeroes that encode the actionable insights (patterns, trends, relationships) that we seek to extract from our data through machine learning and data science. Datasphere is not just for data managers.

Data Warehouse 324

Data Warehouse Metadata Digital Transformation Unstructured Data 324

Alation

DECEMBER 15, 2021

Modern business is built on a foundation of trusted data. Yet high-volume collection makes keeping that foundation sound a challenge, as the amount of data collected by businesses is greater than ever before. An effective data governance strategy is critical for unlocking the full benefits of this information.

Data Governance 52

Data Governance Strategy Metadata Data Strategy 52

O'Reilly on Data

MARCH 24, 2020

Beyond the autonomous driving example described, the “garbage in” side of the equation can take many forms—for example, incorrectly entered data, poorly packaged data, and data collected incorrectly, more of which we’ll address below. Data collected for one purpose can have limited use for other questions.

Machine Learning 346

Machine Learning Statistics Data Quality Modeling 346

Alation

FEBRUARY 17, 2023

The firms that get data governance and management “right” bring people together and leverage a set of capabilities: (1) Agile; (2) Six sigma; (3) data science; and (4) project management tools. Some data seems more analytical, while other is operational (external facing). Establishing a solid vision and mission is key.

Data Governance 52

Data Governance Data Quality Metadata Cost-Benefit 52

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

O'Reilly on Data

DECEMBER 19, 2018

We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline. In a previous post , we talked about applications of machine learning (ML) to software development, which included a tour through sample tools in data science and for managing data infrastructure.

Machine Learning 261

Machine Learning Software Modeling Testing 261

Alation

OCTOBER 12, 2021

Folks can work faster, and with more agility, unearthing insights from their data instantly to stay competitive. Yet the explosion of data collection and volume presents new challenges. Build a roadmap for future data and analytics projects, like cloud computing. Evaluate and monitor data quality.

Strategy 52

Strategy Enterprise Analytics Data Governance 52