Advertisement is one of the major ways through which businesses can attract customers. A lot of money and time is invested in order to create ads. However, these days a helping hand has come for rescue and is successfully able to attract customers by presenting customized ads. Machine Learning Algorithms, Artificial Intelligence and Deep Learning have come into play. With the help of these technologies, customized ads can be created based on the current searches done by customer. For example, you recently searched for “affordable mobile phones”. These learning algorithms tracks it down and soon starts displaying mobile phones ads presented by various companies. Other than that, Data Mining also plays an important role in this. Among various data that is available on world wide web, data mining algorithms browser and stores valuable data. 

Read more about this at https://www.techworm.net/2018/06/how-machine-learning-algorithms-help-businesses-target-their-ads.html

In this world of competition, companies need to take advantage of available data and take a look about what might happen in future. Predictive Analysis is one such branch of Data Analytics that aims to make predictions about future outcomes using various algorithms and other data analytics tools. Methods like data mining, big data, machine learning are back bone of Predictive Analysis and organizations are able to decode patterns and relations which helps them to detect risk and opportunity. Financial Services, Law Enforcements, Automotive, Healthcare are few fields which have already adapted this technology. 

To know more visit: https://www-cio-com.cdn.ampproject.org/c/s/www.cio.com/article/3273114/predictive-analytics/what-is-predictive-analytics-transforming-data-into-future-insights.amp.html

Some of the pros for executing data driven marketing efficiently are:-

1. Joint collaboration with IT department and marketing team is the key to achieve efficiency.

2. Hiring an industry analyst, professor or data scientist to review the data before publishing is necessary to check accuracy.

3. Planning the data collect and analysis before starting the project will help immensely.

4. It is important to focus on what the data means and what are its implications.

5. In order to increase exposures create strong relations with media and analyst (to find out what kind of data suits best for them) before publishing the data.

6. Company should decide whether they want to invest in product growth or data marketing.

7. If the company doesn't have data they don’t need to invest in costly data gathering procedures. Inexpensive tools are easily available.

To know more, read article by James A. Martin on the link : http://www.cio.com/article/3052442/marketing/how-to-do-data-driven-marketing-right.html?page=2

Information plays a vital role in sales. Sales analytics is the application of analytic techniques such as data mining, which helps in sales. It tells in which direction you are going, and also help in finding prospects, target the sales efforts, nurture leads and eventually close the sale. Analytics requires data, rather data from many different sources, and CRM system should be a repository of all your organization's information on customers. Read more at- http://it.toolbox.com/blogs/insidecrm/grow-your-sales-with-analytics-70578

Data mining has taken a new form termed as a Dark Web. Law enforcements have made use of dark web to analyze data on websites and concluded that in 2010 over 100,000 sites contained extremist and terrorists' content. Today, dark web has become popular in finding and shutting down websites that can be used to facilitate criminal activity. Big data is also making use of such technique to collect data to better understand criminal behavior. Dark web has made it easier to keep an eye on illegal activities. Making use of dark web can stop these criminal activities which would ultimately results in favor of people. Read more at:https://channels.theinnovationenterprise.com/articles/mining-in-the-dark

In our daily life we came across new technologies and making our life simpler. Today people are enjoying benefits of internet in form of online shopping, easily sending emails and transactions with friends online. Data is collected on the bases of these activities and improvements are made according to people’s preferences and tastes. Data mining is the key to collect big data and most of the businesses are dealing with these data to provide good services to consumers. Both of the large and small organizations are using big data. Earlier big data was providing benefits to retail and sales industry but now it also giving advantages to learning and development industries. Big data is also beneficial for employee training and can improve performance of current employees. Transformation of training process from traditional to modern techniques there has been a significant improvement in employees’ performance. Employers can easily motivate and inspire their employees. After getting good output from employees, employer gives more importance and satisfaction to employees. So, big data help employers to better understand their employees. Big data help companies to understand people’s requirements and providing facilities according to their preference. It also came in a form of learning and development tool for businesses to improve the performance of employees. Read more at: http://www.smartdatacollective.com/briggpatten/331869/how-big-data-shaping-future-learning-and-development-industry

Data scientists are the ones who use sophisticated quantitative and computer science skills to both structured and analyse unstructured data as well as derive intuitions from the data and suggest actions. Data scientists can tackle with problems that are complex, huge in size and disorganized using several coding languages. To identify a data scientist following points are to be taken into consideration:

• Qualifications: Data scientists are required to have an advanced degree usually a masters or PhD in a quantitative discipline such as economics, statistics and their educational background may be diversified.

• Skills: Data scientists are efficient users of different tools used for analytics and are well versed with coding languages such as Python or Java used for writing programs, transformations etc. They are also have expert knowledge about statistical and machine learning models such as R and SAS.

• Dataset size: They usually work with datasets measured in gigabytes up to petabytes.

• Job responsibility: Data scientists are well equipped to work on every stage of analytics life cycle which also include data acquisition, transformation/cleaning, analytics to predict patterns of the datasets, prescribing actions and programming/automations to contribute to a firms data products.

The main idea behind this is that whether you are a data scientist, analytics professional or programmer you always need to be well versed with the new languages coming in the market each day just as big data has been gaining importance and keep up with the new technology.

Read more at: http://www.smartdatacollective.com/lburtch/320541/more-just-title-how-identify-data-scientist

Predictive analytics is a “business game changer” that will separate the winners from the losers, according to Forrester. The better a company is at predicting what will happen in the future, the better positioned they are to do something about it. While data scientists will do the heaviest analytic-related lifting at big enterprises, the improvements that have been made to predictive analytic (also called advanced analytic) applications enables regular business people and developers to partake of the predictive bounty. With so many companies coming into the foray of analytics services, today the users have more options to choose from keeping the cost-benefit & need-value trade-offs in mind.  RapidMiner offers a “rock solid” enterprise solution with more than 1,500 methods that address all stages of the analytics lifecycle and has among the tightest integration with the cloud, Forrester says. There are also other options like SAS, SPSS, KNIME, sap, oracle to name a few.

To read more, visit:

http://www.datanami.com/2015/04/07/predictive-analytics-now-in-reach-of-the-average-enterprise-forrester-says/

Billions of dollars are lost by gamblers every year along the Vegas Strip, but some casino operators are taking strides to soften the blow of serious gambling losses and leveraging big data to keep customers coming back, according to one executive.  "They could win a lot or they lose a lot or they could have something in the middle. So we do try to make sure that people don't have really unfortunate visits," said Caesars Entertainment Chairman and CEO Gary Loveman on Big Data Download.  Caesars and other casino operators offer loyalty programs. As gamblers spend, companies gather data on those spending trends. Customers also receive tailored incentives for gambling and spending. 

"We give you very tangible and immediate benefits for doing so. So we give you meals, and hotel rooms and limousines and show tickets. You share with us information on what you've been doing, what sorts of transactions you've made," said Loveman, whose company is the biggest U.S. casino operator.

Caesars in particular employs about 200 data experts at its Flamingo Hotel alone. They scour through data on the types of games customers have played, what hotel they've stayed at and where they've been dining. So the next time when you visit a casino, expect a suddenly friendlier slot machine after you are on a losing streak.

Read the complete report here:  http://www.cnbc.com/id/101027330

A Geographic Information System (GIS) integrates hardware, software, and data for capturing, managing, analyzing, and displaying all forms of geographically referenced information. GIS allows us to view, understand, question, interpret, and visualize data in many ways that reveal relationships, patterns, and trends in the form of maps, globes, reports, and charts. It helps you answer questions and solve problems by looking at your data in a way that is quickly understood and easily shared. There are a lot of advantages of using GIS technology. It makes data handling easier, covers large area, used for monitoring various things because of repetitive coverage, it is fast, can be used in inaccessible areas, unbiased, more accurate, reliable and economical. Data could be collected in several bands/ colors so it could be used for micro level analysis. GIS will help in decision making by government officials and in increasing transparency and accountability for good governance. It will help in management of natural resources, improved allocation of resources and planning, improved communication during crisis, cost savings by improved decision makings etc.

The effective use and implementation of Radio-frequency identification (RFID), GPS & data mining techniques in Public Distribution System (PDS) can facilitate PDS supply chain and promise eradicating mismanagement, corruption, trafficking, theft and anti-social elements. RFID provides highly accurate and detailed information by capturing the data and information at each stage of the supply chain, automatically. It also improves the safety and efficiency of the food supply chain. Location technology GPS can also be combined with RFID technology to automatically track and record the information regarding the field where the produce was picked, when and where it was transported and the current location of the produce. This also helps in reducing theft and trafficking. Data mining techniques based on the rule base classification model is used to identify the suspicious moving behavior of the objects. To read more: http://www.articlesbase.com/information-technology-articles/control-of-public-distribution-system-using-gps-gis-remote-sensing-with-data-mining-rfid-3393327.html

Text mining, which is sometimes referred to "text analytics", is one way to make qualitative or "unstructured" data usable by a computer. Also known as text data mining, refers to the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text, deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interestingness. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, sentiment analysis etc. Text analysis involves information retrieval, analysis to study word frequency distributions, pattern recognition, tagging, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics. The main goal is, essentially, to turn text into data for analysis, via application of natural language processing (NLP) and analytical methods. To read more about text mining: http://www.scientificcomputing.com/blogs/2014/01/text-mining-next-data-frontier.

Fraudsters are more competent, better made, and creatively excellent than whatever possible time in the later past. Their adulteration arrangements include complex frameworks of individuals, records, and events. The evidence for these schemes may exist on multiple systems, incorporate various data sorts, and deliberately represent hidden activity. So an analyst has abundant investigative focuses on these frameworks with no true approach to join data or results. To prevent and uncover deception, one needs a solution that is more exceptional and advanced than hoaxers. A basic venture in fraud detection analytics is visualizing the patterns in your data between people, places, frameworks, and events. These data mining and profound analysis capabilities provide more context and better information, enabling more accurate data segmentation and data labelling, which further improves pattern recognition. To read more about it: http://www.21ct.com/solutions/fraud-detection-analytics/.

Professional sports organizations are multi-million dollar enterprises with millions of dollars spent on a single decision. With this amount of capital at stake, just one bad or misguided decision has the potential of setting an organization back by several years. With such a large amount of risk involved it requires a critical need to make good decisions, and hence it’s an attractive environment for data mining applications.

Sports Data Mining has experienced rapid growth in recent years. The task is not how to collect the data, but what data should be collected and how to make the best use of it. From players improving their game-time performance using video analysis techniques, to scouts using statistical analysis and projection techniques to identify what talent will provide the biggest impact, data mining is quickly becoming an integral part of the sports decision making landscape where managers and coaches using machine learning and simulation techniques can find optimal strategies for an entire upcoming season. By finding the right ways to make sense of data and turning it into actionable knowledge, sports organizations have the potential to secure a competitive advantage over their peers. To read more how it has been used: http://www.ukessays.com/essays/psychology/data-mining-in-sports-in-the-past-few-years-psychology-essay.php 

This article elaborates on the sentiment analysis from tweets using data mining techniques. Instead of using SQL, it shows how to conduct such analysis using a more sophisticated software called RapidMiner. It explains how one can extract Twitter data into Google Docs spread sheet and then transfer it into a local environment utilizing two different methods. The emphasis is on how to amass a decent pool of tweets in two different ways using a service called Zapier, Google Docs and a tool called GDocBackUpCMD, along with SSIS and a little bit of C#. Zapier is used to extract Twitter feeds into Google Docs spread sheet and then copy the data across to local environment to mine it for sentiment trends. Next, it is shown how this data can be analyzed for sentiments i.e. whether a concrete Twitter feed can be considered as negative or positive. For this purpose, RapidMiner as well as two separate data sets of already pre-relegated tweets for model learning and Microsoft SQL Server for some data polishing and storage engine. Read more at:http://bicortex.com/twitter-sentiment-analysis-mining-twitter-data-using-rapidminer-part-1/

The last five years, we have seen a massive upsurge of data; we have generated 90 percent of the entire world's data in the last two years!!! Why today analytics is so successful? Just because of availability of data? No! We have to admit the technological advancement we made through the last decade to analyze the available data, also the success can be attributed to the decreased cost of storage space. Twenty years ago, we had all the data analysis tools in place but data was scarce. Today there is a frightening abundance of data... But then what to do with this available data and how! Yeah we got the techniques, but we need the applicability mapping... Think about the applicability of analytics into the telecom sector, which represents an agile supply chain management ...These examples points to the tremendous possibilities that big data offer in telecom sector. But we must never forget the underlying secret: only blue-blooded telecom experts can usher in this exciting telecom future. Today's big buzzword is big data. Its companion, buzzword analytics, is also a big favorite. There are good reasons why this is so. 

For More Information Go To This Link:

http://www.business-standard.com/article/management/a-secret-about-analytics-114030200628_1.html

The numbers are coming in thick and fast but can big data and advanced analytics influence a team’s performance in the Soccer Premier Leagues?

Since the 1950s, when retired RAF pilot Charles Reep’s roughly-researched analytics led to the long-ball strategy, the football community has been enthralled by statistics. Today Premier League teams and their followers are being inundated with data as high-tech cameras capture every pass, dribble, free kick or touch of the ball while monitoring players’ xy coordinates every tenth of a second throughout the 90-minute match! What we call it as?

For more information check the following link:

http://knowledge.insead.edu/operations-management/beyond-moneyball-data-mining-the-premier-league-3155

In recent years, data mining has been used widely in the areas of science and engineering, such as bioinformatics, genetics, medicine, and electrical power engineering. Several researchers and organizations have conducted reviews of data mining tools and surveys of data miners. These identify some of the strengths and weaknesses of the software packages. They also provide an overview of the behaviors, preferences and views of data miners. How about reaching a few inches deep into the applicability and usability of data mining which is the analysis stage of a typical KDD in terms of Education?

Please Follow this link

http://www.thehindu.com/news/cities/chennai/harnessing-data-mining-to-revolutionise-education/article5690365.ece