Jan 07, 21: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Accuracy check  Source

[ FEATURED COURSE]

Hadoop Starter Kit

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Hadoop learning made easy and fun. Learn HDFS, MapReduce and introduction to Pig and Hive with FREE cluster access…. more

[ FEATURED READ]

Antifragile: Things That Gain from Disorder

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Antifragile is a standalone book in Nassim Nicholas Taleb’s landmark Incerto series, an investigation of opacity, luck, uncertainty, probability, human error, risk, and decision-making in a world we don’t understand. The… more

[ TIPS & TRICKS OF THE WEEK]

Save yourself from zombie apocalypse from unscalable models
One living and breathing zombie in today’s analytical models is the pulsating absence of error bars. Not every model is scalable or holds ground with increasing data. Error bars that is tagged to almost every models should be duly calibrated. As business models rake in more data the error bars keep it sensible and in check. If error bars are not accounted for, we will make our models susceptible to failure leading us to halloween that we never wants to see.

[ DATA SCIENCE Q&A]

Q:Do you know a few “rules of thumb” used in statistical or computer science? Or in business analytics?

A: Pareto rule:
– 80% of the effects come from 20% of the causes
– 80% of the sales come from 20% of the customers

Computer science: “simple and inexpensive beats complicated and expensive” – Rod Elder

Finance, rule of 72:
– Estimate the time needed for a money investment to double
– 100$ at a rate of 9%: 72/9=8 years

Rule of three (Economics):
– There are always three major competitors in a free market within one industry

Source

[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @DavidRose, @DittoLabs

 #BigData @AnalyticsWeek #FutureOfData #Podcast with @DavidRose, @DittoLabs

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

We chose it because we deal with huge amounts of data. Besides, it sounds really cool. – Larry Page

[ PODCAST OF THE WEEK]

@SidProbstein / @AIFoundry on Leading #DataDriven Technology Transformation #FutureOfData #Podcast

 @SidProbstein / @AIFoundry on Leading #DataDriven Technology Transformation #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Poor data across businesses and the government costs the U.S. economy $3.1 trillion dollars a year.

Sourced from: Analytics.CLUB #WEB Newsletter

Top 5 Changes That AI Is Set To Have On The Education Industry

AI is everywhere. Whether you are conscious of it or not, the presence of automated tech is overwhelming, with applications in the average individual’s life that won’t even occur to them until it is pointed out. From online shopping, to financial trends, the data revolution is fueling huge amounts of AI technology that is shaping the future of all sorts of different industries. With an almost unlimited potential influence, it’s useful looking at the more unusual areas that AI can have an impact on. One such area is education. The importance of education is so great that it is always worth keeping up to date with how it is changing, so let’s look at 5 ways AI is changing the education industry.

Cutting Down On Admin

One of the biggest hampering forces in education is all the ‘other stuff’. It’s not as simple as sitting in a classroom with a teacher and learning, modern education is a bureaucratic nightmare at times. From medical forms, to safeguarding to insurance, there is a huge amount to worry about beyond the education of students. Artificial Intelligence can be used to automate these sorts of duties with ease and allow teachers to focus on the teaching and spend less time grading tests and calculating scores on a curve. AI will let the education come first in education.

Chatbots In The Classroom

This is an area of AI tech that Is seeing its first outings in a classroom scenario. “After their success in customer service, alternative applications for AI driven chatbots are being explored. One such area is education, where the load of a teacher with a class of 30 or 40 students is being lessened with the help of pre-programmed chatbots who can help answer the more straight forward, binary questions that kids will likely have”, explains Mac Johnson, IT writer at StateOfWriting and Essayroo. This might seem a bit ‘sci-fi’ but it’s actually a pretty simple response to the issue of the increasing burden on teachers, particularly in a high school setting.

Personalize The Learning Path

Personalization is a major benefit of AI. The more data that can be collected on an individual, the more it can be fed to a piece of AI-driven tech which can then decipher the most tailored paths through things like shopping, streaming recommendations and, now, education. “Everyone is different when it comes to education, in what they want to achieve and how they will most effectively achieve it. So using AI to tailor the experience is a no-brainer. Traditional methods of education will leave great swathes of people on either end feeling like they’re not getting what they need. This stops that from occurring”, explains Laura Washington, tech journalist at Academized and Boomessays. Personalization is one of those really great benefits from AI that should be take advantage of whenever possible.

An Education In Technology

The presence of AI in the process of education gives a wonderful opportunity for the woefully under-explored experience of technology education. Aside from those people who actively pursue computer-science degrees, the average individual is actually noticeably ignorant about technology, especially when you consider how important technology is to everyone these days. Introducing AI into standard education will encourage much needed conversations about how things like AI work, which will better prepare people for a world absolutely dominated by technology.

Smart Content

AI presents in many different forms, as we can see. In a lot of instances we barely even know it is there, in other cases it’s in the form of a physical robot that has AI written all over it. AI can help you to digitalize content in a way that feels futuristic, which can help to boost engagement and give an alternative approach to education. Digital content in the curriculum is here to stay and AI can help make it even more efficient.

Conclusion

Artificial Intelligence is such a rich and varied field with such a large range of applications that its integration into technology is as exciting as it is pre-ordained. Users will find their lives made easier and more engaging as they navigate the difficult task of receiving an education.

Aimee Laurence has worked in tech journalism and marketing for the past 3 years at Cheap Assignment and OXEssays. She works mainly on future tech and making technology consumer ready. She also works as a freelance editor at the PaperFellows portal.




Source: Top 5 Changes That AI Is Set To Have On The Education Industry

The What and Where of Big Data: A Data Definition Framework

I recently read a good article on the difference between structured and unstructured data. The author defines structured data as data that can be easily organized. As a result these type of data are easily analyzable. Unstructured data refers to information that either does not have a pre-defined data model and/or is not organized in a predefined manner. Unstructured data are not easy to analyze. A primary goal of a data scientist is to extract structure from unstructured data. Natural language processing is a process of extracting something useful (e.g., sentiment, topics) from something that is essentially useless (e.g., text).

While I like these definitions she offers, she included an infographic that is confusing. It equates the structural nature of the data with the source of the data, suggesting that structured data are generated solely from internal/enterprise systems while unstructured data are generated solely from social media sources. I think it would be useful to separate the format (structure vs. unstructured) of the data from source (internal vs. external) of data.

Sources of Data: Internal and External

Generally speaking, business data can come from either internal sources or from external sources. Internal sources of data reflect those data that are under the control of the business. These data are housed in financial reporting system, operational systems, HR systems and CRM systems, to name a few. Business leaders have a large say in the quality of internal data; they are essentially a byproduct of the processes and systems the leaders use to run the business and generate/store the data.

External sources of data, on the other hand, are any data generated outside the walls of the business. These data sources include social media, online communities, open data sources and more. Due to the nature of source of data, external sources of data are under less control by the business than are internal sources of data. These data are collected by other companies, each using their unique systems and processes.

Data Definition Framework

Data Definition Framework
Figure 1. Data Definition Framework

This 2×2 data framework is a way to think about your business data (See Figure 1). This model distinguishes the format of data from the source of data. The 2 columns represent the format of the data, either structured or unstructured. The 2 rows represent the source of the data, either internal or external. Data can fall into one of the four quadrants.

Using this framework, we see that unstructured data can come from both internal sources (e.g., open-ended survey questions, call center transcripts) and external sources (e.g., Twitter comments, Pinterest images). Unstructured data is primarily human-generated. Human-generated data are those that are input by people.

Structured data also can come from both inside (e.g., survey ratings, Web logs, process control measures) and outside (e.g., GPS for tweets, Yelp ratings) the business. Structured data includes both human-generated and machine-generated data. Machine-generated data are those that are calculated/collected automatically and without human intervention (e.g., metadata).

The quality of any analysis is dependent on the quality of the data. You are more likely to uncover something useful in your analysis if your data are reliable and valid. When measuring customers’ attitudes, we can use customer ratings or customer comments as our data source. Customer satisfaction ratings, due to the nature of the data (structured / internal), might be more reliable and valid than customer sentiment metrics from social media content (unstructured / external); as a result, the use of structured data might lead to a better understanding of your data.

Data format is not the same as data source. I offer this data framework as a way for businesses to organize and understand their data assets. Identify strengths and gaps in your own data collection efforts. Organize your data to help you assess your Big Data analytic needs. Understanding the data you have is a good first step in knowing what you can do with it.

What kind of data do you have?

 

Source: The What and Where of Big Data: A Data Definition Framework