Smart Data Collective

SEPTEMBER 4, 2020

Data mining in Search Engine Optimization is a new concept and has gained importance in the digital marketing field. It can be understood as a process that can be used for extracting useful information from a large amount of data. In other words, data mining is a process that can be used by companies for converting raw data into useful data with the help of a software. What is Data Mining? How Data Mining is Changing SEO?

Data mining 49

Data mining Optimization Marketing Data Processing 49

Smart Data Collective

AUGUST 25, 2020

Before the turn of the century, the reliance on data technology was little more than nonexistent. Web developers utilized data to some capacity as well, but marketers rarely considered doing so. Big data has become critical to the evolution of digital marketing.

Data mining 80

Data mining Metadata Marketing Snapshot 80

DataFloq

APRIL 30, 2019

Data mining and clustering are closely interlinked. They help in discovering patterns in data. Clustering is one of the various methods of data mining. What is clustering in data mining? Generally, the mining of data ends up at spotting the pattern. If you talk about clustering in particular, it’s an unsupervised data mining method that splits the data into natural groups. It’s what the data mining is.

Data mining 199

Data mining Statistics Machine Learning IT 199

Data Science 101

OCTOBER 31, 2019

Today we are generating data more than ever before. Over the last two years, 90 percent of the data in the world was generated. 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 52

Data mining KDD Forecasting Knowledge Discovery 52

DataFloq

OCTOBER 23, 2019

Data mining, a process that involves identifying patterns and anomalies in large data sets, is widespread among many of today’s companies. Experts predict the big data market will reach $103 billion in revenue by 2027, far exceeding 2019's predicted $49 billion. One of the main reasons why data mining is so pervasive is the wide variety of applications it has. But data mining is proving to be an effective way to combat fraud. Big Data

Data mining 165

Data mining Reporting Modeling Marketing 165

Smart Data Collective

MARCH 18, 2020

Big data is changing the direction of small and medium sized businesses. They can use big data for many purposes. However, the value of their big data strategies will vary considerably. Using big data to get a better understanding of your customers is important. Wired author Mike Dickey has written a great article on 10 ways that big data can be used to get a more thorough understanding of your customers. Unfortunately, not all SMEs use this data effectively.

Data mining 68

Data mining Sales Data Strategy ROI 68

DataFloq

MARCH 28, 2019

Companies receive information in the form of digital data or content, more commonly in the shape of performance and user metrics. The entire operation that involves collecting data, processing it and then putting it to use is called business intelligence. But even when teams are not actively working with data, it’s still collected and stored. To make use of this data it must be processed and analyzed, often referred to as “mining.”. Big Data

Data mining 215

Data mining Data Warehouse Metrics Business Intelligence 215

Analytics Vidhya

MAY 31, 2020

Overview Feature engineering is a key aspect in acing data science hackathons Learn how to perform feature engineering here as we walk through a. The post Want to Ace Data Science Hackathons? Beginner Data Engineering Data Mining Machine Learning Python datahack feature engineering feature importance pythonThis Feature Engineering Guide is for you appeared first on Analytics Vidhya.

Data Science 214

Data Science Data mining Analytics Machine Learning 214

Analytics Vidhya

JUNE 7, 2020

Overview SQL is a mandatory language every analyst and data science professional should know Learn about the basics of SQL here, including how to. Beginner Data Mining Database Programming Python SQL Structured Data database RDBMs sql analytics sql data sql data science sql for beginners sql sqlite SQLiteThe post SQL for Beginners and Analysts – Get Started with SQL using Python appeared first on Analytics Vidhya.

Data mining 219

Data mining Structured Data Data Science Analytics 219

Analytics Vidhya

APRIL 29, 2020

Classification Data Engineering Data Mining Python Advanced Python machine learning model deployment Model deployment text classification Twitter APIOverview Deploying your machine learning model is a key aspect of every ML project Learn how to use Flask to deploy a machine learning. The post How to Deploy Machine Learning Models using Flask (with Code!) appeared first on Analytics Vidhya.

Machine Learning 213

Machine Learning Modeling Data mining Analytics 213

Analytics Vidhya

MARCH 3, 2020

Honestly, most data. Data Mining Intermediate Libraries Python Structured Data data exploration feature engineering pandasIntroduction The Transform function in Pandas (Python) can be slightly difficult to understand, especially if you’re coming from an Excel background. The post Learn How to use the Transform Function in Pandas (with Python code) appeared first on Analytics Vidhya.

Data mining 195

Data mining Structured Data Analytics 195

KDnuggets

DECEMBER 24, 2019

2019 Dec Tutorials, Overviews Apriori Association Rules Data Mining PythonThis article is about Market Basket Analysis & the Apriori algorithm that works behind it.

Marketing 110

Marketing Data mining IT 110

Rocket-Powered Data Science

MAY 20, 2020

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.” Rocket-Powered Data Science (the website that you are now reading). Big Data Data Science Internet of Things Machine Learning Artificial Intelligence Data Literacy Data MiningI have written articles in many places.

Data Science 169

Data Science Internet of Things Data mining Machine Learning 169

Rocket-Powered Data Science

DECEMBER 16, 2018

In a related post we discussed the Cold Start Problem in Data Science — how do you start to build a model when you have either no training data or no clear choice of model parameters. An example of a cold start problem is k -Means Clustering, where the number of clusters k in the data set is not known in advance, and the locations of those clusters in feature space ( i.e., the cluster means) are not known either. What data do we have?

Machine Learning 199

Machine Learning Metrics Modeling Data mining 199

Rocket-Powered Data Science

DECEMBER 15, 2018

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. Without labels (diagnoses, classes, known outcomes) in past data, then how do we make progress in labeling (explaining) future data? kNN ( k-Nearest Neighbors ), which is a supervised learning technique in which the data set itself becomes the model.

Modeling 147

Modeling Optimization Data mining Metrics 147

Forrester's Customer Insights

OCTOBER 22, 2019

It’s T minus two weeks to Forrester’s 2nd Data Strategy & Insights Forum in Austin, TX. Over 300 data and analytics leaders will gather to share, learn and get inspired! For those of you who have already registered and planning to attend, you answered one key question during the registration process: What is your top […].

Strategy 34

Strategy Analytics B2B IT 34

Decision Management Solutions

FEBRUARY 19, 2020

These different elements will lend themselves to different kinds of technology for automation – some will be rules based, some might use data mining, some might need machine learning algorithms. Focus instead on capturing data about how well the current approach is working and on regular, weekly updates to your decision-making. Automating decisions about transactions lets them be handled in real-time, providing better customer service.

Risk 50

Risk Testing Technology Data mining 50

Decision Management Solutions

JULY 8, 2020

To meet a reporting need, an organization must present some or all of the data it has gathered in a report to some internal or external body. The organization that produces the report is only required to provide that data in report form. The start by looking at their data.

Reporting 92

Reporting ROI Key Performance Indicator Metrics 92

Srividya Sridharan

OCTOBER 16, 2013

Customer insights professionals have many customer analytics methods (sub's reqd) to choose from today to perform behavioral customer analysis, and new techniques emerge as the complexity of customer data increases. Analysis of customer data involves the use of data-mining and statistical methods that span descriptive and predictive analytics. customer analytics customer insights customer intelligence data mining predictive analytics

Statistics 40

Statistics Data mining Predictive Analytics Customer Analytics 40

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. Big Data Data Science Machine Learning Products Training Analytics Data MiningThat 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 33

Data Science Deep Learning Statistics Data-driven 33

Decision Management Solutions

DECEMBER 12, 2018

They had some great predictions and suggested priorities around the ethics of analytics, the value of data and the use of AI in fraud and cybersecurity. As they quote in the paper: According to the Rexer Data Science Survey, barely 10 to 15% of companies “almost always” deploy results and another 50% only deploy “often.” This lets business owners get used to analytics and data-driven decisioning and manages technology risk at the same time. Citizen Data Scientists.

Data Science 40

Data Science Publishing Machine Learning Data-driven 40

Data Virtualization

JANUARY 8, 2020

According to CIO magazine, the first chief data officer (CDO) was employed at Capital One in 2002, and since then the role has become widespread, driven by the recent explosion of big data. Business analysis big data Chief Data Officer data assets Data Governance Data Lakes Data Mining data virtualization Data Warehouse; Denodo Platform digital transformation ETL ETL Processes Gartner hadoop Logical Data Warehouse

Data Warehouse 12

Data Warehouse Data mining Digital Transformation Data Lake 12

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.

KDD 61

KDD Knowledge Discovery Data mining Statistics 61

Ms SQL Girl

JUNE 10, 2015

Chapter 3 Selecting the Data Architecture that Fits Your Organization. Chapter 4 Searching and Combining Data with Power Query. Chapter 6 Discovering and Analyzing Data with Power Pivot. Chapter 9 Discovering Knowledge with Data Mining. Chapter 12 Visualizing Your Data Interactively with Power View. Chapter 13 Exploring Geographic and Temporal Data with Power Map.

Reporting 40

Reporting Visualization Operational Reporting Data Architecture 40

Ms SQL Girl

JUNE 10, 2015

Chapter 3 Selecting the Data Architecture that Fits Your Organization. Chapter 4 Searching and Combining Data with Power Query. Chapter 6 Discovering and Analyzing Data with Power Pivot. Chapter 9 Discovering Knowledge with Data Mining. Chapter 12 Visualizing Your Data Interactively with Power View. Chapter 13 Exploring Geographic and Temporal Data with Power Map.

Reporting 40

Reporting Visualization Operational Reporting Data Architecture 40

KDnuggets

DECEMBER 12, 2019

ACM SIGKDD Invites Industry and Academic Experts to Submit Advancements in Data Mining, Knowledge Discovery and Machine Learning for 26 th Annual Conference in San Diego. 2019 Dec Events Applications CA KDD KDD-2020 Research San Diego

KDD 46

KDD Knowledge Discovery Data mining Machine Learning 46

DataFloq

MAY 27, 2019

They both attempt to make sense of unstructured data, like language, as opposed to structured data like statistics, actions, etc. However, NLP and NLU are opposites of a lot of other data mining techniques. NLP is an already well-established, decades-old field operating at the cross-section of computer science, artificial intelligence, and increasingly data mining.

Data mining 204

Data mining IT Unstructured Data Statistics 204

DataFloq

MAY 20, 2020

Raw textual data is often unstructured and is therefore unused by the very businesses that collect it. Until recently, it was nearly impossible to analyze unstructured textual data at scale. When integrated with health IT systems, data mining helps to reduce subjectivity and improve diagnosis and treatment while improving predictive models. Once set in motion, data integrated healthcare IT systems are capable of improving responses to medical emergencies on.

Technology 212

Technology Predictive Modeling Data mining Machine Learning 212

DataFloq

MAY 19, 2020

Machine learning-based methods of demand forecasting in the retail industry leverage historical data, but the limitations of that data are clear as we grapple with COVID-19. The following are three methods that may boost the accuracy of demand.Short-Term POS Data AnalysisOne proven and efficient technique for identifying shifts in patterns of demand is to analyze the most up-to-date point of sale data. Big Data Artificial Intelligence

Forecasting 190

Forecasting Machine Learning Data mining Sales 190

DataFloq

SEPTEMBER 9, 2019

Statistics has its own significance in data science, but it’s not the only thing which data scientists have to deal with. The commonly used way to address hidden characteristics within a data set is known as SCD. A data scientist should have a proper understanding of mathematics in all areas. Processing of big data can’t be achieved through conventional methods. Data programming. Data mining. Data cleansing.

Statistics 212

Statistics Machine Learning Unstructured Data Data mining 212

DataFloq

JULY 11, 2019

Data mining. A quick look at any employment-based social networking site is enough to tell that you cloud computing skills are more valuable to employers today than ever before. Some people have gone so far as to say that many companies are in crisis-mode since they can't find people who have the skills they need. Many firms transitioned to cloud-based software deployment models without hiring sufficient numbers of people trained to maintain these kinds of environments.

Data mining 191

Data mining Statistics Software Optimization 191

FineReport

MAY 6, 2020

Setiap hari, kita menjawab pertanyaan tentang skill seorang data analyst. Menurut saya, data analyst nampaknya cuma menganalisis data bisnis dan saya tidak tahu bagaimana cara meningkatkan skill saya.” Ini karena dia tidak sepenuhnya menggali nilai dari analisis big data. Analisis data dipakai untuk mengoptimalkan produk, strategi pemasaran, dan strategi pengoperasian melalui data yang didapatkan. Framework Big Data Processing: Hadoop, storm, spark.

Data mining 63

Data mining Software Data Warehouse Machine Learning 63

Smart Data Collective

SEPTEMBER 13, 2020

Big data is becoming more important to modern marketing. You can’t afford to ignore the benefits of data analytics in your marketing campaigns. Search Engine Watch has a great article on using data analytics for SEO. Keep in mind that big data drives search engines in 2020.

Data Analytics 91

Data Analytics Data mining Optimization Analytics 91

TDAN

MARCH 3, 2020

In 2018, it was discovered that Cambridge Analytica had harvested the data of at least 87 million Facebook users without their knowledge after obtaining it via a few thousand accounts that had used a quiz app. There is no getting away from how incredibly valuable our data is. In 2018, the Global Data Mining Tools […].

Data mining 40

Data mining IT Data Governance Business Intelligence 40

Smart Data Collective

FEBRUARY 21, 2020

Many careers have been heavily impacted by changes in big data. The big data revolution has had a profound effect on healthcare, marketing and many other fields. One of the fields that has been most affected by big data is electrical engineering.

Big Data 112

Big Data Data mining IoT Testing 112

FineReport

DECEMBER 10, 2019

Why We Need Data Cleaning?. Data analysis is a time-consuming task, but are you prepared before the data analysis, and have you omitted the important step: data cleaning? For data scientists, we will encounter all kinds of data. Data Quality Guidelines.

Data mining 83

Data mining Data Quality Statistics Measurement 83

Smart Data Collective

JANUARY 29, 2020

However, many federal agencies have finally discovered the countless benefits of big data. The Internal Revenue Service (IRS) is one of the organizations that has started using big data to enforce its policies. Many accounting and bookkeeping services are using big data these days.

Predictive Analytics 65

Predictive Analytics Big Data Data mining Analytics 65

Smarten

FEBRUARY 10, 2020

Citizen Data Scientists Will Lead the Charge with Augmented Analytics! You have probably heard a lot about the concept of Citizen Data Scientists in industry conferences and journals.

ROI 67

ROI Data mining Machine Learning Data Science 67

Smart Data Collective

SEPTEMBER 9, 2020

Analysis of medical data collected from different groups and demographics allows researchers to understand patterns and connexions in diseases and identify factors that increase the efficacy of certain treatments.

Technology 82

Technology Insurance Data mining Publishing 82

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. Data Sourcing. During this stage, one of your best practices will be to identify data gaps in your outputs.

Predictive Analytics 75

Predictive Analytics Analytics Predictive Modeling Modeling 75

Tamr

AUGUST 14, 2019

Historically, it was nearly impossible for a large enterprise to optimize the use of geospatial data and specifically in relation to other datasets and attributes. Tamr is leading the charge by evolving our data unification platform to natively understand geospatial data to help enterprises achieve analytical breakthroughs and transformational outcomes. . The post Geospatial Mapping: The New Frontier of Data Unification appeared first on Tamr Inc.

Data mining 74

Data mining Optimization Machine Learning Visualization 74

FineReport

AUGUST 14, 2020

With the development of enterprise informatization, there are more and more kinds of data produced, and the demand for reports surges day by day. Based on the process from data to knowledge, a standard reporting system’s functional architecture is shown below.

Reporting 79

Reporting Informatics OLAP Data Warehouse 79

Smart Data Collective

SEPTEMBER 17, 2020

Data precision has completely revamped our understanding of geography in countless ways. We also use big data to facilitate navigation. One of the tools that utilizes big data is Google Maps. The Emerging Role of Big Data with Google Analytics.

Visualization 71

Visualization Big Data Data mining Sales 71