Data Science 101

OCTOBER 31, 2019

This data alone does not make any sense unless it’s identified to be related in some pattern. Data mining is the process of discovering these patterns among the data and is therefore also known as Knowledge Discovery from Data (KDD). Machine learning provides the technical basis for data mining.

Data mining 77

Data mining KDD Data Science Forecasting 77

Smart Data Collective

JUNE 4, 2021

What is data science? Data science is analyzing and predicting data, It is an emerging field. Some of the applications of data science are driverless cars, gaming AI, movie recommendations, and shopping recommendations. These data models predict outcomes of new data. Statistics.

Data Science 105

Data Science Statistics Deep Learning Machine Learning 105

Analytics Vidhya

DECEMBER 13, 2021

Pursuing any data science project will help you polish your resume. The post Top Data Science Projects to add to your Portfolio in 2021 appeared first on Analytics Vidhya. Introduction 2021 is a year that proved nothing is better than a Proof of Work to evaluate any candidate’s worth, initiative, and skill.

Data Science 334

Data Science Analytics Data mining Deep Learning 334

CIO Business Intelligence

JUNE 7, 2022

It comprises the processes, tools and techniques of data analysis and management, including the collection, organization, and storage of data. The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. What are the four types of data analytics?

Data Analytics 133

Data Analytics Diagnostic Analytics Management Analytics 133

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.

Data Science 59

Data Science Machine Learning Cost-Benefit Deep Learning 59

FineReport

JULY 28, 2021

Good data can give you keen insights, convincing evidence to make informed decisions. By observing and analyzing data, we can develop more accurate theories and formulate more effective solutions. For this reason, data science and/vs. Definition: BI vs Data Science vs Data Analytics.

Business Intelligence 98

Business Intelligence Data Science Data Analytics Analytics 98

CIO Business Intelligence

JUNE 14, 2023

Certification of Professional Achievement in Data Sciences The Certification of Professional Achievement in Data Sciences is a nondegree program intended to develop facility with foundational data science skills. The online program includes an additional nonrefundable technology fee of US$395 per course.

Big Data 126

Big Data Data Analytics Analytics Predictive Modeling 126

Data Science 101

DECEMBER 11, 2019

This weeks guest post comes from KDD (Knowledge Discovery and Data Mining). Every year they host an excellent and influential conference focusing on many areas of data science. Honestly, KDD has been promoting data science way before data science was even cool. 1989 to be exact. ACM SIGKDD.

KDD 81

KDD Knowledge Discovery Data mining Statistics 81

CIO Business Intelligence

NOVEMBER 14, 2022

With organizations increasingly focused on data-driven decision making, decision science (or decision intelligence) is on the rise, and decision scientists may be the key to unlocking the potential of decision science systems. Commonly used models include: Statistical models. Some experts consider BI a successor to DSS.

Data mining 136

Data mining Data-driven Statistics OLAP 136

Jen Stirrup

SEPTEMBER 30, 2021

Although CRISP-DM is not perfect , the CRISP-DM framework offers a pathway for machine learning using AzureML for Microsoft Data Platform professionals. AI vs ML vs Data Science vs Business Intelligence. They may also learn from evidence, but the data and the modelling fundamentally comes from humans in some way.

Business Intelligence 135

Business Intelligence Data mining Machine Learning Testing 135

Rocket-Powered Data Science

JULY 7, 2019

AGI (Artificial General Intelligence): AI (Artificial Intelligence): Application of Machine Learning algorithms to robotics and machines (including bots), focused on taking actions based on sensory inputs (data). Examples: (1-3) All those applications shown in the definition of Machine Learning. (4)

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

datapine

AUGUST 29, 2022

James Warren, on the other part, is a successful analytics architect with a background in machine learning and scientific computing. 5) Data Analytics Made Accessible, by Dr. Anil Maheshwari. Best for : the new intern who has no idea what data science even means. The author, Anil Maheshwari, Ph.D.,

Big Data 263

Big Data Data Analytics Analytics Data mining 263

CIO Business Intelligence

OCTOBER 13, 2023

The data architect also “provides a standard common business vocabulary, expresses strategic requirements, outlines high-level integrated designs to meet those requirements, and aligns with enterprise strategy and related business architecture,” according to DAMA International’s Data Management Body of Knowledge.

Data Architecture 131

Data Architecture Data Warehouse Statistics Visualization 131

Smart Data Collective

DECEMBER 19, 2021

The rise of machine learning and the use of Artificial Intelligence gradually increases the requirement of data processing. That’s because the machine learning projects go through and process a lot of data, and that data should come in the specified format to make it easier for the AI to catch and process.

Optimization 135

Optimization Machine Learning Data mining Statistics 135

CIO Business Intelligence

MAY 12, 2022

The tools include sophisticated pipelines for gathering data from across the enterprise, add layers of statistical analysis and machine learning to make projections about the future, and distill these insights into useful summaries so that business users can act on them. On premises or in SAP cloud. Per user, per month.

Predictive Analytics 137

Predictive Analytics Analytics Statistics Machine Learning 137

FineReport

MARCH 24, 2024

Data analysts contribute value to organizations by uncovering trends, patterns, and insights through data gathering, cleaning, and statistical analysis. They identify and interpret trends in complex datasets, optimize statistical results, and maintain databases while devising new data collection processes.

Statistics 52

Statistics Data mining Visualization Reporting 52

CIO Business Intelligence

AUGUST 9, 2022

These generalists are often responsible for every step of the data process, from managing data to analyzing it. Dataquest says this is a good role for anyone looking to transition from data science to data engineering, as smaller businesses often don’t need to engineer for scale. Data engineer vs. data architect.

Analytics 128

Analytics Data Science Statistics Unstructured Data 128

CIO Business Intelligence

AUGUST 11, 2023

How natural language processing works NLP leverages machine learning (ML) algorithms trained on unstructured data, typically text, to analyze how elements of human language are structured together to impart meaning. Licensed by MIT, SpaCy was made with high-level data science in mind and allows deep data mining.

Unstructured Data 141

Unstructured Data Modeling Machine Learning Data mining 141

Rocket-Powered Data Science

APRIL 25, 2019

A few years ago, I generated a list of places to receive data science training. Learn the what, why, and how of Data Science and Machine Learning here. That list has become a bit stale. Follow Kirk Borne on Twitter @KirkDBorne. Follow Kirk Borne on Twitter @KirkDBorne.

Data Science 38

Data Science Machine Learning Deep Learning Statistics 38

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 122

Predictive Analytics Analytics Predictive Modeling Data mining 122

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. A background in (or a firm grasp of) data warehousing and mining. BI engineer.

Business Intelligence 239

Business Intelligence Statistics Visualization Data-driven 239

Smart Data Collective

OCTOBER 14, 2019

Whether they want a career as an app developer or data analyst, the skillsets below can help them find lucrative careers in a competitive job market. Big Data Skillsets. From artificial intelligence and machine learning to blockchains and data analytics, big data is everywhere. Machine Learning.

Big Data 75

Big Data Software Data mining Prescriptive Analytics 75

Sisense

APRIL 10, 2020

One of the most-asked questions from aspiring data scientists is: “What is the best language for data science? People looking into data science languages are usually confused about which language they should learn first: R or Python. NLP can be used on written text or speech data. R or Python?”.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

datapine

NOVEMBER 27, 2019

Often seen as the highest foe-friend of the human race in movies ( Skynet in Terminator, The Machines of Matrix or the Master Control Program of Tron), AI is not yet on the verge to destroy us, in spite the legit warnings of some reputed scientists and tech-entrepreneurs. Prescriptive analytics goes a step further into the future.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

Peter James Thomas

AUGUST 18, 2018

From 2000 to 2015, I had some success [5] with designing and implementing Data Warehouse architectures much like the following: As a lot of my work then was in Insurance or related fields, the Analytical Repositories tended to be Actuarial Databases and / or Exposure Management Databases, developed in collaboration with such teams.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63

Jet Global

MAY 9, 2024

Data pipelines are designed to automate the flow of data, enabling efficient and reliable data movement for various purposes, such as data analytics, reporting, or integration with other systems. For example, migrating customer data from an on-premises database to a cloud-based CRM system. What is an ETL pipeline?

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

FineReport

JULY 18, 2022

Key features: As a professional data analysis tool, FineBI successfully meets business people’s flexible and changeable data processing requirements through self-service datasets. FineBI is supported by a high-performance Spider engine to extract, calculate and analyze a large volume of data with lightweight architecture.

Forecasting 52

Forecasting Dashboards Visualization Statistics 52

Grooper

AUGUST 19, 2019

Applied analytics Business analytics Machine learning and data science. 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. Simulations.

Analytics 54

Analytics Machine Learning Data Science Data-driven 54

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. e-handbook of statistical methods: Summary tables of useful fractional factorial designs , 2018 [3] Ulrike Groemping. Hedayat, N.J.A.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

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

Cloudera

NOVEMBER 30, 2017

Taken together, the six strategies will help you apply big data analytics to attracting and retaining your organization’s most valuable asset: its customers. “It is a competitive advantage to know more about your customers and to apply this knowledge to marketing, sales, support, and the development of products and services.

Strategy 40

Strategy Big Data Data mining Visualization 40

Rita Sallam

JULY 22, 2016

Research VP, Business Analytics and Data Science. Again, check out the Critical Capabilities for BI and Analytic Platforms for how each vendor compares. We are on the cusp of the next wave of BI market disruption beyond the current one started by Tableau and Qlik – but that’s for my next blog post. Enjoy your summer!!

Analytics 40

Analytics Business Intelligence Metadata Statistics 40

CIO Business Intelligence

DECEMBER 22, 2022

Such teams tend to view analytic pipelines as analogous to lean manufacturing lines and regularly reflect on feedback provided by customers, team members, and operational statistics. The DataOps approach is not limited to machine learning,” they add.

Analytics 129

Analytics Machine Learning Software Data Quality 129

CIO Business Intelligence

JANUARY 20, 2023

BI focuses on descriptive analytics, data collection, data storage, knowledge management, and data analysis to evaluate past business data and better understand currently known information. Whereas BI studies historical data to guide business decision-making, business analytics is about looking forward.

Business Intelligence 143

Business Intelligence Dashboards Data mining OLAP 143

Edwin Chen

SEPTEMBER 6, 2011

A machine learning group of folks in orange centered around people like Oliver Grisel, Christian Langreiter, and Joseph Turian. The green cluster of users is reversed: 77% of users follow more of the red and purple clusters of topics (on startups and general intellectual matters) than machine learning and technical topics.

Machine Learning 52

Machine Learning Broadcasting Visualization Data mining 52