IBM Big Data Hub

SEPTEMBER 19, 2023

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. Those who work in the field of data science are known as data scientists.

Data Science 80

Data Science Data Analytics Prescriptive Analytics Analytics 80

Smart Data Collective

SEPTEMBER 13, 2020

Relevance refers to the contextual match of a page, and can be increased with keyword optimization. Search engines use data mining tools to find links from other sites. Having more links, from more referring domains, is generally associated with a higher “authority,” and therefore higher search rankings.

Data Analytics 100

Data Analytics Analytics Big Data Data mining 100

IBM Big Data Hub

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming.

Machine Learning 56

Machine Learning Data Science Statistics Deep Learning 56

Smart Data Collective

SEPTEMBER 16, 2020

Predictive analytics, sometimes referred to as big data analytics, relies on aspects of data mining as well as algorithms to develop predictive models. These predictive models can be used by enterprise marketers to more effectively develop predictions of future user behaviors based on the sourced historical data.

Predictive Analytics 121

Predictive Analytics Analytics Predictive Modeling Data mining 121

datapine

JANUARY 24, 2021

If you want to survive, it’s time to act.” – Capgemini and EMC² in their study Big & Fast Data: The Rise of Insight-Driven Business. You’ll want to be mindful of the level of measurement for your different variables, as this will affect the statistical techniques you will be able to apply in your analysis. Who are they?

IT 317

IT Statistics KPI Data-driven 317

Smart Data Collective

JUNE 14, 2022

Data scientists need to have a number of different skills. In addition to understanding the logistics of networking and a detailed knowledge of statistics, they must possess solid programming skills. When you are developing big data applications, you need to know how to create code effectively.

Data mining 91

Data mining Big Data Data Science Statistics 91

datapine

MARCH 31, 2022

According to the US Bureau of Labor Statistics, demand for qualified business intelligence analysts and managers is expected to soar to 14% by 2026, with the overall need for data professionals to climb to 28% by the same year. This beats projections for almost all other occupations. BI engineer. BI Project Manager.

Business Intelligence 239

Business Intelligence Statistics Visualization Data-driven 239

CIO Business Intelligence

AUGUST 11, 2023

While the term originally referred to a system’s ability to read, it’s since become a colloquialism for all computational linguistics. Mallet , an open-source, Java-based package for statistical NLP, document classification, clustering, topic modeling, information extraction, and other ML applications to text. Amazon Comprehend.

Unstructured Data 142

Unstructured Data Modeling Machine Learning Data mining 142

Smart Data Collective

SEPTEMBER 21, 2022

They refer to personal qualities that are transferable to any type of role. Problem solving refers to the ability to find solutions to any issues in quite a timely manner. In fact, one expert points out that 85% of the success in the technology sector can be attributed to soft skills like good communication. Problem Solving.

Statistics 85

Statistics Software Data mining Marketing 85

BizAcuity

OCTOBER 21, 2019

. – into structured data to develop actionable managerial insights to enhance their operations. . . Text mining is also referred to as text analytics, is the process of deriving high -quality information from text.

Analytics 73

Analytics Unstructured Data Data mining Structured Data 73

FineReport

JUNE 11, 2021

According to the definition, business intelligence and analytics refer to the data management solutions implemented in companies to collect, analyze and drive insights from data. In contrast, business analytics is often described as a more statistically-based field. BI Dashboard (by FineReport). Business Analytics.

Business Intelligence 52

Business Intelligence Analytics Dashboards Statistics 52

BizAcuity

MAY 7, 2019

For instance, if the demand is underestimated, sales can be lost due to the lack of supply of goods – which is referred to as a negative gap. Similarly, if the demand is overestimated, then the supplier is left with a surplus – also referred to as a positive gap – which then becomes a financial drain.

Forecasting 75

Forecasting IT Sales Statistics 75

datapine

SEPTEMBER 27, 2022

One of the best beginners’ books on SQL for the analytical mindset, this masterful creation demonstrates how to leverage the two most vital tools for data query and analysis – SQL and Excel – to perform comprehensive data analysis without the need for a sophisticated and expensive data mining tool or application.

Business Intelligence 292

Business Intelligence Data Warehouse Data Processing Data mining 292

Peter James Thomas

AUGUST 18, 2018

Convergent Evolution refers to something else. Even back then, these were used for activities such as Analytics , Dashboards , Statistical Modelling , Data Mining and Advanced Visualisation. So far so simple.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63

BizAcuity

OCTOBER 21, 2019

. – into structured data to develop actionable managerial insights to enhance their operations. Text mining is also referred to as text analytics, is the process of deriving high -quality information from text.

Analytics 40

Analytics Unstructured Data Data mining Structured Data 40

Sisense

AUGUST 11, 2019

BA is a catch-all expression for approaches and technologies you can use to access and explore your company’s data, with a view to drawing out new, useful insights to improve business planning and boost future performance. What About “Business Intelligence”? But on the whole, BI is more concerned with the whats and the hows than the whys.

Business Intelligence 75

Business Intelligence Business Analytics Analytics OLAP 75

FineReport

DECEMBER 19, 2019

Professional data analysts must have a wealth of business knowledge in order to know from the data what has happened and what is about to happen. In addition, tools for data analysis and data mining are also important. Excel, Python, Power BI, Tableau, FineReport are frequently used by data analysts.

Knowledge Discovery 98

Knowledge Discovery Visualization Experimentation Reporting 98

datapine

MAY 14, 2019

This interdisciplinary field of scientific methods, processes, and systems helps people extract knowledge or insights from data in a host of forms, either structured or unstructured, similar to data mining. If you want to become an Ipython legend, this is one of the best books on data science on offer at the moment.

Data Science 350

Data Science Machine Learning Data-driven Big Data 350

The Unofficial Google Data Science Blog

JANUARY 14, 2016

Unlike experimentation in some other areas, LSOS experiments present a surprising challenge to statisticians — even though we operate in the realm of “big data”, the statistical uncertainty in our experiments can be substantial. We must therefore maintain statistical rigor in quantifying experimental uncertainty.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

datapine

MARCH 25, 2019

What Is A Data Analysis Method? 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.

Key Performance Indicator 217

Key Performance Indicator Statistics Visualization Big Data 217

CIO Business Intelligence

JANUARY 20, 2023

BI tools access and analyze data sets and present analytical findings in reports, summaries, dashboards, graphs, charts, and maps to provide users with detailed intelligence about the state of the business. Whereas BI studies historical data to guide business decision-making, business analytics is about looking forward.

Business Intelligence 144

Business Intelligence Dashboards Data mining OLAP 144

The Unofficial Google Data Science Blog

APRIL 23, 2024

If $Y$ at that point is (statistically and practically) significantly better than our current operating point, and that point is deemed acceptable, we update the system parameters to this better value. Our main tools are the difference-of-convex-programs paradigm[9] and the embedded conic solver[10]; the reference [11] is also very useful.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

DataKitchen

OCTOBER 11, 2021

Historic Balance – compares current data to previous or expected values. These tests rely upon historical values as a reference to determine whether data values are reasonable (or within the range of reasonable). . Statistical Process Control – applies statistical methods to control a process.

Testing 152

Testing Metadata Dashboards Statistics 152

The Unofficial Google Data Science Blog

JULY 22, 2020

This post considers a common design for an OCE where a user may be randomly assigned an arm on their first visit during the experiment, with assignment weights referring to the proportion that are randomly assigned to each arm. For example, imagine a fantasy football site is considering displaying advanced player statistics.

Experimentation 95

Experimentation Statistics Testing Strategy 95

Smart Data Collective

OCTOBER 17, 2022

A job is any unit of assigned work that will perform a specific said task related to data. The source from which data enters the pipeline is called upstream while downstream refers to the final destination where the data will go. Data flows down the pipeline just like water. Data Pipeline Architecture Planning.

Data Warehouse 131

Data Warehouse Data Lake Visualization Big Data 131

Grooper

AUGUST 19, 2019

Data science skills. Technology – i.e. data mining, predictive analytics, and statistics. Best practices for exploring collected data. Data is crucial to the success of business analytics. Just as Henry Ford used data to ensure success in the early 1900’s, we also depend on volumes of high-quality data.

Analytics 54

Analytics Machine Learning Data Science Data-driven 54

The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

In this post we explore why some standard statistical techniques to reduce variance are often ineffective in this “data-rich, information-poor” realm. Despite a very large number of experimental units, the experiments conducted by LSOS cannot presume statistical significance of all effects they deem practically significant.

Experimentation 40

Experimentation Metrics Statistics Measurement 40

datapine

DECEMBER 28, 2021

1) What Is A Misleading Statistic? 2) Are Statistics Reliable? 3) Misleading Statistics Examples In Real Life. 4) How Can Statistics Be Misleading. 5) How To Avoid & Identify The Misuse Of Statistics? If all this is true, what is the problem with statistics? What Is A Misleading Statistic?

Statistics 363

Statistics Advertising Visualization Data mining 363

datapine

NOVEMBER 27, 2019

The demand for real-time online data analysis tools is increasing and the arrival of the IoT (Internet of Things) is also bringing an uncountable amount of data, which will promote the statistical analysis and management at the top of the priorities list. It’s an extension of data mining which refers only to past data.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

Jet Global

MAY 1, 2023

that gathers data from many sources. Users Want to Help Themselves Data mining is no longer confined to the research department. Today, every professional has the power to be a “data expert.” Some cloud applications can even provide new benchmarks based on customer data. Ask your vendors for references.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52

Jet Global

MAY 9, 2024

ETL is a specific type of data pipeline that focuses on the process of extracting data from sources, transforming it, and loading it into a destination, such as a data warehouse or data lake. ETL is primarily used for data warehousing and business intelligence applications.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52