datapine

MAY 14, 2019

By gaining the ability to understand, quantify, and leverage the power of online data analysis to your advantage, you will gain a wealth of invaluable insights that will help your business flourish. The ever-evolving, ever-expanding discipline of data science is relevant to almost every sector or industry imaginable – on a global scale.

Data Science 350

Data Science Machine Learning Data-driven Big Data 350

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

IBM Big Data Hub

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? This post will dive deeper into the nuances of each field.

Machine Learning 54

Machine Learning Data Science Statistics Deep Learning 54

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 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 135

Data Analytics Diagnostic Analytics Management Analytics 135

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 Data Science 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 144

Data mining Data-driven Statistics OLAP 144

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

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 53

Data Science Machine Learning Cost-Benefit Deep Learning 53

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 132

Data Architecture Data Warehouse Statistics Visualization 132

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

Analytics: The products of Machine Learning and Data Science (such as predictive analytics, health analytics, cyber analytics). A reference to a new phase in the Industrial Revolution that focuses heavily on interconnectivity, automation, Machine Learning, and real-time data. They cannot process language inputs generally.

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

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 131

Analytics Data Science Statistics Unstructured Data 131

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

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. Improper Use of Comments.

Data mining 98

Data mining Big Data Data Science Statistics 98

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. So, I have updated the list, adding some new opportunities, keeping many of the previous ones, and removing the obsolete ones.

Data Science 38

Data Science Machine Learning Deep Learning Statistics 38

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. Free tier.

Predictive Analytics 137

Predictive Analytics Analytics Statistics Machine Learning 137

Smart Data Collective

DECEMBER 19, 2021

Therefore, if you don’t preprocess the data before applying it in the machine learning or AI algorithms, you are most likely to get wrong, delayed, or no results at all. Hence, data preprocessing is essential and required. Python as a Data Processing Technology.

Optimization 136

Optimization Machine Learning Data mining Statistics 136

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 144

Business Intelligence Dashboards Data mining OLAP 144

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

CIO Business Intelligence

AUGUST 11, 2023

It was developed in the Department of Computer and Information Science at the University of Pennsylvania and provides interfaces to more than 50 corpora and lexical resources, a suite of text processing libraries, wrappers for natural language processing libraries, and a discussion forum. NLTK is offered under the Apache 2.0

Unstructured Data 142

Unstructured Data Machine Learning Data Science Modeling 142

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 124

Predictive Analytics Analytics Predictive Modeling Data mining 124

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. Analytics, Collaboration Software, Data Management, Data Mining, Data Science, IT Strategy, Small and Medium Business.

Analytics 130

Analytics Machine Learning Software Data mining 130

FineReport

DECEMBER 10, 2019

Do you want to know if there are some guidelines to regulate the quality of these data? Here, I summarize the data cleaning rules into the following 4 key points: Completeness : Whether there is a null value for a single piece of data and whether the statistical fields are complete. Data Cleaning in Python.

Data mining 98

Data mining Data Quality Statistics Measurement 98

Smart Data Collective

OCTOBER 14, 2019

That, along with data mining can help if the developer wants to work with supply chains, for example. Statistics, qualitative analysis and quant are some of the backbones of big data. Knowledge of data analytics tools like SAS, R and SPSS can also help software developers find competitive and lucrative careers.

Big Data 76

Big Data Software Data mining Prescriptive Analytics 76

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

BizAcuity

MAY 7, 2019

Traditionally, demand forecasting involved sales managers coming together at the end of every three months to analyze the sales statistics of the bygone business quarter. Today, several methods involving data science, statistical model, trend line, time-phased analysis, data mining and more are used to predict consumer demand.

Forecasting 75

Forecasting IT Sales Statistics 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

Smart Data Collective

SEPTEMBER 21, 2022

Python is one of the best languages for data science and AI , so it is a good idea to find Python programmers for your AI startup. Developing AI technology requires decent programming skills. You obviously need to hire developers that understand the programming languages that help create AI applications.

Statistics 88

Statistics Data mining Software Marketing 88

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 Statistics Visualization 52

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 Reporting Experimentation 98

The Unofficial Google Data Science Blog

JULY 22, 2020

For example, imagine a fantasy football site is considering displaying advanced player statistics. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. One reason to do ramp-up is to mitigate the risk of never before seen arms.

Experimentation 95

Experimentation Statistics Testing Knowledge Discovery 95

The Unofficial Google Data Science Blog

JANUARY 14, 2016

by AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of data science. Because individual observations have so little information, statistical significance remains important to assess. We must therefore maintain statistical rigor in quantifying experimental uncertainty.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

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

Analytics Vidhya

JANUARY 25, 2023

Introduction What’s most crucial to us? Could it be the ability to create a fortune, have good physical health, or be the focus of attention? In line with the latest World Happiness Report, it is evident that being happy has become a worldwide priority.

Reporting 290

Reporting Analytics IT Data mining 290

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