TDAN

AUGUST 17, 2021

If you are planning on using predictive algorithms, such as machine learning or data mining, in your business, then you should be aware that the amount of data collected can grow exponentially over time.

Predictive Modeling 115

Predictive Modeling Modeling Data mining Big Data 115

Smart Data Collective

AUGUST 27, 2019

Big data has helped us learn more about the changing nature of the economy. A growing number of digital firms are using machine learning to discover insights into the nature of the new world of commerce. New Hadoop and other data extraction tools have provided a great deal of information about these trends.

Big Data 75

Big Data Data mining Machine Learning Software 75

Smart Data Collective

MAY 15, 2019

Big data has created both positive and negative impacts on digital technology. On the one hand, big data technology has made it easier for companies to serve their customers. Big data has created a number of security risks for Bluetooth users. Bluetooth Security Risks in the Age of Big Data.

Big Data 93

Big Data Risk Measurement Data mining 93

FineReport

APRIL 26, 2023

This has led to the emergence of the field of Big Data, which refers to the collection, processing, and analysis of vast amounts of data. With the right Big Data Tools and techniques, organizations can leverage Big Data to gain valuable insights that can inform business decisions and drive growth.

Big Data 52

Big Data Data Analytics Analytics Cost-Benefit 52

Smart Data Collective

JUNE 25, 2019

Big data is making it easier for marketers to make the most of their campaigns. Facebook, Google and other major companies collect massive troves of data , which are invaluable for advertisers. Unfortunately, this data is useless without a well-thought out strategy. Big data is vital to consumer research.

Big Data 75

Big Data Strategy Data Processing Data mining 75

Sisense

AUGUST 19, 2020

Disrupting Markets is your window into how companies have digitally transformed their businesses, shaken up their industries, and even changed the world through the use of data and analytics. The use of big data analytics and cloud computing has spiked phenomenally during the last decade.

Big Data 52

Big Data Machine Learning Digital Transformation Marketing 52

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

Smart Data Collective

MARCH 5, 2022

Even if you already have a full-time job in data science, you will be able to leverage your expertise as a big data expert to make extra money on the side. You will have a much easier time creating a successful dropshipping business if you are proficient with big data. It uses complex data analytics features.

Data mining 134

Data mining Big Data Data-driven Data Science 134

Rocket-Powered Data Science

MAY 20, 2020

I have written articles in many places. In 2019, I was listed as the #1 Top Data Science Blogger to Follow on Twitter. In 2019, I was listed as the #1 Top Data Science Blogger to Follow on Twitter. And then there’s this — not a blog, but a link to my 2013 TedX talk: “ Big Data, Small World.”

Data Science 239

Data Science Big Data Internet of Things Data mining 239

Smart Data Collective

JUNE 4, 2021

Here are the chronological steps for the data science journey. First of all, it is important to understand what data science is and is not. Data science should not be used synonymously with data mining. Mathematics, statistics, and programming are pillars of data science. Basics of Machine Learning.

Data Science 105

Data Science Statistics Deep Learning Machine Learning 105

FineReport

JUNE 11, 2021

And do you know what the key to unlocking value from data is? This article will discuss the definition of business intelligence and analytics and the difference between them. Let’s go back and see what we discussed in this article. Definition. Note: the reports and dashboards samples used here are made with FineReport.

Business Intelligence 98

Business Intelligence Analytics Dashboards Statistics 98

FineReport

MARCH 24, 2024

Given the critical role they play, employers actively seek data analysts to enhance efficiency and stimulate growth. This article explores the data analyst job description, covering essential skills, tools, education, certifications, and experience.

Statistics 52

Statistics Data mining Visualization Reporting 52

Rocket-Powered Data Science

DECEMBER 15, 2018

This wisdom applies not only to life but to machine learning also. Specifically, the availability and application of labeled data (things past) for the labeling of previously unseen data (things future) is fundamental to supervised machine learning. This would be a problem. Good judgment comes experience.

Machine Learning 171

Machine Learning Optimization Metrics Modeling 171

Octopai

APRIL 19, 2021

Donna Burbank is a Data Management Consultant and acts as the Managing Director at Global Data Strategy, Ltd. Her Twitter page is filled with interesting articles, webinars, reports, and current news surrounding data management. TDAN stands for The Data Administration Newsletter. Donna Burbank. IRM UK Connects.

Metadata 52

Metadata Management Business Intelligence Data Governance 52

FineReport

JUNE 11, 2021

And do you know what the key to unlocking value from data is? This article will discuss the definition of business intelligence and analytics and the difference between them. Let’s go back and see what we discussed in this article. Definition. Note: the reports and dashboards samples used here are made with FineReport.

Business Intelligence 52

Business Intelligence Analytics Dashboards Statistics 52

Smart Data Collective

MARCH 23, 2022

Evan Morris of Towards Data Science discussed this in one of his recent articles. Use a machine learning tool to automate compliance. You will be able to use AI to learn more about different groups by data mining public records and tailoring your message to them. Protect Yourself Against Lawsuits.

Cost-Benefit 102

Cost-Benefit Data mining Sales Testing 102

Peter James Thomas

AUGUST 18, 2018

No this article has not escaped from my Maths & Science section , it is actually about data matters. The image at the start of this article is of an Ichthyosaur (top) and Dolphin. Even back then, these were used for activities such as Analytics , Dashboards , Statistical Modelling , Data Mining and Advanced Visualisation.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63

Sisense

APRIL 10, 2020

We’ll actually do this later in this article. These support a wide array of uses, such as data analysis, manipulation, visualizations, and machine learning (ML) modeling. Some standard Python libraries are Pandas, Numpy, Scikit-Learn, SciPy, and Matplotlib. R libraries. Modeling in R and Python.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

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

MARCH 31, 2022

While analysts focus on historical data to understand current business performance, scientists focus more on data modeling and prescriptive analysis. They use advanced technologies such as machine learning models to generate predictions about future business performance. Here we will name 3 of the top ones.

Business Intelligence 239

Business Intelligence Statistics Visualization Data-driven 239

Rocket-Powered Data Science

JULY 6, 2021

I provide below my perspective on what was interesting, innovative, and influential in my watch list of the Top 10 data innovation trends during 2020. 1) Automated Narrative Text Generation tools became incredibly good in 2020, being able to create scary good “deep fake” articles.

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

BizAcuity

MARCH 31, 2022

There have been so many articles published about AI and its applications, you can find millions of articles from broad concepts to deep technical literature on the internet. You must be tired of continuously hearing quotes like, ‘data is the new oil’ and what not. Source: Gartner Research). Source: TCS). AI in Finance.

Data Analytics 52

Data Analytics Analytics Prescriptive Analytics Predictive Analytics 52

Sisense

MAY 11, 2021

According to a recent survey conducted by IDC , 43% of respondents were drawing intelligence from 10 to 30 data sources in 2020, with a jump to 64% in 2021! With that much data flowing into analytics systems, the right data model is vital to helping your users derive actionable intelligence from them. Dig into AI.

Modeling 84

Modeling Big Data IoT Data Warehouse 84

datapine

NOVEMBER 27, 2019

DQM consists of acquiring the data, implementing advanced data processes, distributing the data effectively and managing oversight data. We detailed the benefits and costs of good or bad quality data in our previous article on data quality management , where you can read the five important pillars to follow.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

Jet Global

MAY 1, 2023

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.” Ideally, your primary data source should belong in this group. Bid Goodbye to Standalone Users don’t want to have to leave their app or call IT for insights.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52