Data Mining Within E-Commerce

By Gary C.

In e-commerce, data mining is critically essential in order to compete with the rapidly growing competition amongst retailers. E-commerce is the exchange of data within the online world in order to garner business transactions. There are patterns and trends within shoppers that are analyzed and broken down in order to determine strategies to identify a multitude of situations, such as from what customers may like based on a previously purchased product all the way to why customers tend to avoid a certain product. The amount of raw data that is transmitted through data mining is astounding and requires a tremendous amount of research in order to determine the most of every possible likely scenario. read more...

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Machine Learning Applied to Agriculture

By Johnny C.

Technology is always changing and growing. In the modern day, advancements in technology come at exponential rates. One of the fastest ways to get ahead in an industry is by investing in cutting-edge technology. Agriculture is a huge part of the economy and is very important in sustaining countries. A more recent development in agriculture industry, is machine learning. Machine learning is a type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed (Rouse, 2016). An example of this is, is an advancement made by biologist David Hughes and epidemiologist Marcel Salathe. “They fed a computer more than 50,000 images, and by learning on its own, the program can correctly identify 99.35 percent of the new images they throw at it.” This is a huge advantage for farmers, allowing them to understand what is affecting the crops, and finding faster ways to cure them. Machine learning can also help discover the best times and locations for crops, maximizing the yield. Although there are limits and challenges to machine learning, it is capable of pushing the boundaries in agriculture. read more...

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Data Mining and Database use in the Construction industry

By Mackenzie B.

With the advancement of technology and our ability to pool and collect information digitally, we’re able to minimize the collection of previously written copies of work and compile them into simple small archives of information. Where previously files, folders, and endless drawers of signed documents were once stored; Databases allows us to maximize the efficiency of the construction industry ranging from a collection of completed land permits, to legal documents and leases, to client information and a collection of parts for labour. Having engineers being able to pre-test their designed structures and building managers and clients being able to understand how strongly designed their structures are, are some of the most important steps in creating a well built building. read more...

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Real World Product Database

By Aerold B.

A product or a service is the bread and butter of all businesses. Every business needs to keep track of every item that they sell. Whether it’s the current stock of a product or the product’s location. In the past, businesses do not have a computer to keep track of their products. Instead, they use paper to communicate and keep track of every product that they sell. These papers are installed in cabinets so that it can be accessed in the future. The introduction of personal computing revolutionized how businesses store their data. Database replaced cabinets. According to AJ Graham, there are several types of databases that have been around since the 1960s. It was not until the 1970s when the most commonly used type of database was created. This most commonly used type of database is the Relational databases. Database made it easier for businesses to keep track of their product. Database also made it possible for businesses to enter their product data in the cloud and can be accessed by authorized employees in the company. Database are also used for business services such as banking, insurance, and transportation services. read more...

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Real World Product Database

By Aerold B.

A product or a service is the bread and butter of all businesses. Every business needs to keep track of every item that they sell. Whether it’s the current stock of a product or the product’s location. In the past, businesses do not have a computer to keep track of their products. Instead, they use paper to communicate and keep track of every product that they sell. These papers are installed in cabinets so that it can be accessed in the future. The introduction of personal computing revolutionized how businesses store their data. Database replaced cabinets. According to AJ Graham, there are several types of databases that have been around since the 1960s. It was not until the 1970s when the most commonly used type of database was created. This most commonly used type of database is the Relational databases. Database made it easier for businesses to keep track of their product. Database also made it possible for businesses to enter their product data in the cloud and can be accessed by authorized employees in the company. Database are also used for business services such as banking, insurance, and transportation services. read more...

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Data Mining in Agriculture

By: Paola A.

Every year, technology changes and new developments help many economic sectors discover new ways to improve, forecast a change, etc. For example, one economic sector that is benefitting from using new developments in technology is agriculture. In agriculture, a way to discover this type of changes is through data mining. But what exactly is data mining and how is agriculture benefitting from this. “Data mining involves the process of finding large quantity of previously unknown data, and then their use in important business decision making” (Milovic & Radojevic, 2015). For instance, in developing countries such as India, using data mining for “price prediction helps the farmers and also Government to make effective decision[s]” (Hemageetha & Nasira, 2012). Furthermore, data mining is something that could also help consumers by preparing them in case of any change in prices. Finally, this method of collecting data is something that can definitely improve and benefit the way farmers, government, and consumers make better decisions in the future with different applications, but it also has challenges and limitations.
Using data mining in agriculture benefits farmers, government, etc. in many ways. One of the ways that using data mining is beneficial in agriculture is “possibility to study hidden patterns in datasets in agricultural domain. These patterns can be used for diagnosing crop condition, prognosing market development, monitoring customer solvency” (Milovic & Radojevic, 2015). In other words, it can help farmers tell around the time that their crop would flourish, make predictions of when their product is more likely to sell, and what product customers are buying more. Also, another benefit is that “Agricultural institutions use data mining technique and applications for different areas, for instance agronomists use patterns measuring growth indicators of plants, crop quality indicators, success of taken agro technical measures and managers of agricultural organizations pay attention on user satisfaction and economically optimal decisions” (Milovic & Radojevic, 2015). read more...

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Data Mining in Marketing

By: Mohammed A.

Have you ever experienced this, where you open your social media page and all of a sudden see advertisements of a shirt or pair of shoes you were looking at a day ago; well know that it is not a coincidence. It is very likely that the company has been monitoring your online habits for some time and used that data to market and advertise specific products that align with your interests. Within the journal article “Revisiting the problem of market segmentation,” the author emphasizes how data mining helps marketing users to target marketing campaigns and also to align campaigns with the needs, wants, and attitudes of customers and prospects (Lien). This process is most widely used digitally, where every website a person visits leaves a digital footprint, which allows companies to gather that data and market products that will appeal to the individual the most. In essence, data mining can be very beneficial in the advertisement and market industry, where companies are able to gain a competitive advantage through the use of various tools and techniques that allow them to monitor their potential customers’ behaviors. read more...

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Predictive Analytics

By Andrew C.

Predictive analytics, a technique of using data metrics, current and historical, as well as predictors to determine a future outcome. It is also referred to as predictive modeling or forecasting. In essence, one could say it is an attempt at predicting the future, while a bit simple it is not entirely incorrect. Although it sounds outlandish, predictive analytics is very much a core aspect of our lives today, and it is very real.
The benefits of predictive analytics are obvious if you can imagine what kind of advantages predicting future outcomes might give you. The overall premise is that it can drive competitive advantage through a number of ways: identifying trends, understanding customers better, aiding in strategic decision making, predicting industry behavior, process and performance optimization among others. Generally speaking, it can provide a more accurate picture of your business environment; your customers, operations, threats, opportunities, etc. In point of fact, Harrah’s Entertainment, a casino conglomerate uses predictive analytics to drive its marketing and operational decision making and in 2003 it saw an increase in income from operations of 26.6% (Felipe-Barkin, 2011).
Predictive analytics is used in many industries, though there is heavy use in financial services, insurance, retail, telecommunications, healthcare, and pharmaceuticals. In fact, predictive analytics is an aspect that affects our lives every day – credit scoring. Credit scores, an estimate on your ability to pay your bills, maintain debt, etc. in the future are driven by predictive analytics statistical mode and are an everyday facet of our lives today (Brown, 2015). Not only business aspects are affected by predictive analytics. In the early 2000s Billy Beane, the general manager for the Oakland Athletics, with the help of Paul DePodesta used predictive analytics and modeling to put together a team that could still compete with its richer competitors – and they went all the way to the playoffs. A remarkable feat all things considered.
While predictive analytics is a powerful tool, it should be noted that it is simply a tool. It is not a crystal ball or an oracle. It requires large amounts of data, both historical and current, as well as the tools and personnel to organize and interpret that data and turn it into meaningful information. read more...

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Analytics Tools: Flurry vs. Mixpanel

By Caleb W.

For mobile app developers, knowing how their target audience uses their product is an important part of the app’s development cycle. Knowing demographic information about the user, like their age and gender, help developers keep the app relevant to their interests. Tracking usage of the app allows developers to see where users are having difficulty with the app interface and streamline it for a more pleasant user experience. Developers can focus their efforts on improving key functions by logging user activities, like creating accounts, posting comments, and liking content. Integration of analytical tools into apps allow for a wealth of information into how apps are used and the people who use them.
Implementing analytical tools is a typically straightforward process, although ease of use can vary among different software. After importing the software library, some tools may require additional calls within the code in order to track events. The software is able to associate all instances of an app through a uniquely generated API token. Once conditions are set, the app will report when the event defined by the corresponding condition occurs. The developer can then segment the data, filtering it by a specific demographic. This gives developers the information they need to cater to the specific needs of each of their users.
Flurry is an effective analytics tool that can be deployed easily and quickly. Many of its users noted its support for many different platforms and the ease of integration of its SDK. Flurry also has built-in installation tracking, allowing the developer to see the ads that resulted in the user downloading the app. Flurry is free to developers as well, and also has the ability to push ads and notifications to the user. Being free software, however, comes with several drawbacks. Flurry is unable to process data in real-time, usually taking about a day to generate reports. It has limited setting of conditions and segmentation, thus depriving developers the chance to dig deep into certain parameters they may want to track. This inflexible reporting is compounded by several user complaints that some metrics needed to be defined more accurately. Flurry also lacks detailed external support for its API, limiting the methods by which apps can report usage data. Nevertheless, for a free product, Flurry is perfect for developers who simply want to get the ball rolling on mobile analytics.
One of Flurry’s more robust competitors is Mixpanel. Although developers reported difficulty in understanding and integrating the library, they also widely complemented the ease of usage of the app’s interface and its ability to optimize the end user’s experience. The app also provided concise reporting of data and allowed developers to easily set conditions and segment the funneled data. Along with reporting data within seconds of the corresponding events being triggered, Mixpanel also provided support for third-party installation tracking services, as well as a publicly documented API. Mixpanel’s biggest disadvantage, unsurprisingly, was its cost, with its only free option limiting event reports to 25,000 data points or 1,000 unique profiles. Given all the benefits that come with it, however, Mixpanel makes a much better choice for developers needing much more breadth and depth in usage analysis.
Tracking mobile app usage in today’s technological age is a must for any developer. With app stores filled with so many different apps performing so many similar functions, it is important for an app to stand out to users in its functionality and ease of use. For the very low price of free, Flurry is able to provide basic analysis of user activity and demographics. If developers want more insight and control into their data, however, Mixpanel fits the bill. Failure to take advantage of mobile analytics software, however, leaves developers in the dark on how to effectively cater to the needs of their app’s clients.

Works Cited
Flurry Analytics. Retrieved from https://developer.yahoo.com/flurry/docs/analytics/
Help Center. Retrieved from https://mixpanel.com/help/
Lin, Y. (2014, July 30). App Analytics Strategies & Tools. Retrieved from http://blog.kiip.me/developers/mobile-app-analytics/
Khorkov, E. (2015, March 6). Mobile analytics: Mixpanel vs Amplitude vs Flurry vs Localytics. Retrieved from https://medium.com/polecat-blog/mobile-analytics-mixpanel-vs-amplitud-vs-flurry-vs-localytics-aeb6bf02b734#.2mnklr62f read more...

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Analytics for Online Learning

By Chad C.

One of the main definitions for analytics is the “measurement, collection, analysis, and reporting of data about learners and their contexts, for the purposes of understanding and optimizing learning and the environments in which it occurs” (Hampson). It is a fact that technologies are being constantly upgraded, and previous forms of instruction are not as suitable to today’s students. Because of this, Massive Open Online Classes (MOOCs) have been created, and online classes are now more readily available. This means that students are able to complete courses fully online, rather than having to be physically present and face to face with an instructor. Tuition is steadily rising and students are becoming more and more busy with work, internships, and other obligations that might not necessarily allow enough time for them to go to university campuses and sit though hours worth of lectures. The availability and convenience of online classes offers students the classes that they need, as well as the flexibility in schedule that they desire. Big data analytics for online learning provides benefits in terms of bettering retention and completion rates, tracking at-risk students, and aligning institutional aids with participant satisfaction. read more...

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Analytics for Online Learning

By Bryan V.

Remember the time where the only place you could learn educational material was through a teacher or from a book at your local library? Our technological skills and equipment have increased tenfold since then and with that has come Learning Analytics. Learning Analytics is the process of collecting and analyzing data for the purpose of improving online learning environments. Our technology has become so advanced that you can learn almost anything from the comfort of your own home. Learning analytics has helped create most of the learning tools available through the internet today. In this post I will talk about the benefits, tools/software used in Learning Analytics, applications of Learning Analytics, and some limitations concerning the use of Learning Analytics. read more...

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