Feb 27, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Data interpretation  Source

[ AnalyticsWeek BYTES]

>> Analytics Implementation in 12 Steps: An Exhaustive Guide (Tracking Plan Included!) by analyticsweek

>> How to Host a Successful Tech Conference by administrator

>> The Key to DevOps for Big Data Applications: Containers and Stateful Storage by jelaniharper

Wanna write? Click Here

[ FEATURED COURSE]

Statistical Thinking and Data Analysis

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This course is an introduction to statistical data analysis. Topics are chosen from applied probability, sampling, estimation, hypothesis testing, linear regression, analysis of variance, categorical data analysis, and n… more

[ FEATURED READ]

Superintelligence: Paths, Dangers, Strategies

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The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant position. Other animals have stronger muscles or sharper claws, but … more

[ TIPS & TRICKS OF THE WEEK]

Winter is coming, warm your Analytics Club
Yes and yes! As we are heading into winter what better way but to talk about our increasing dependence on data analytics to help with our decision making. Data and analytics driven decision making is rapidly sneaking its way into our core corporate DNA and we are not churning practice ground to test those models fast enough. Such snugly looking models have hidden nails which could induce unchartered pain if go unchecked. This is the right time to start thinking about putting Analytics Club[Data Analytics CoE] in your work place to help Lab out the best practices and provide test environment for those models.

[ DATA SCIENCE Q&A]

Q:Is it better to have 100 small hash tables or one big hash table, in memory, in terms of access speed (assuming both fit within RAM)? What do you think about in-database analytics?
A: Hash tables:
– Average case O(1)O(1) lookup time
– Lookup time doesn’t depend on size

Even in terms of memory:
– O(n)O(n) memory
– Space scales linearly with number of elements
– Lots of dictionaries won’t take up significantly less space than a larger one

In-database analytics:
– Integration of data analytics in data warehousing functionality
– Much faster and corporate information is more secure, it doesn’t leave the enterprise data warehouse
Good for real-time analytics: fraud detection, credit scoring, transaction processing, pricing and margin analysis, behavioral ad targeting and recommendation engines

Source

[ VIDEO OF THE WEEK]

#GlobalBusiness at the speed of The #BigAnalytics

 #GlobalBusiness at the speed of The #BigAnalytics

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[ QUOTE OF THE WEEK]

You can have data without information, but you cannot have information without data. – Daniel Keys Moran

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData with Jon Gibs(@jonathangibs) @L2_Digital

 #BigData @AnalyticsWeek #FutureOfData with Jon Gibs(@jonathangibs) @L2_Digital

Subscribe 

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[ FACT OF THE WEEK]

And one of my favourite facts: At the moment less than 0.5% of all data is ever analysed and used, just imagine the potential here.

Sourced from: Analytics.CLUB #WEB Newsletter

Deriving Value from Data Lakes with AI

Blog

Artificial Intelligence and machine learning are the future of every industry, especially data and analytics. In Growing Up with AI, we help you keep up with all the ways this pioneering technology is changing the world.

Use AI to tackle huge datasets

Let’s talk about AI and machine learning (ML). Does your company actually have the tools and processes to use these innovations? Or do you just say that you have them? “AI-washing” is a disturbing trend among tech companies today, where they (correctly) ascertain that AI will be the deciding factor in the next generation of technologies, but don’t actually have the AI horsepower to back up their product, service, or claims. They slap a term like “AI-powered” or “AI-enhanced” to their offering and hope it’s enough to keep customers engaged.

“AI-washing” is a disturbing trend among tech companies today, where they slap AI language onto their product or service, but don’t actually have the AI horsepower to back up their claims.

However, half-measures just won’t cut it when it comes to handling huge datasets. Data is growing at a phenomenal rate and that’s not going to stop anytime soon. Many companies are already sitting on datasets so massive that it’s not possible for humans to deal with them unaided. AI and ML are the only ways to derive value from massive data lakes, cloud-native data warehouses, and other huge stores of information. This is both a daunting challenge and an inspiring opportunity, since effective use of AI and ML can cut through about 80% of data preparation (the annoying, routine stuff), leaving humans to handle the remaining 20%, the actual modeling and optimization.

Overcoming the obstacles between you and revenue

Once your data is prepared for analysis, the next question is: how else can AI help you? There’s a belief held by many in the AI and analytics worlds that the Holy Grail of data strategy is the ability to turn every question the business could ask (strategic, tactical, and operational) into requirements that an AI system can understand. Then the AI would go into the data, find the respective answers, and serve them up in easy-to-digest language that even a nontechnical team member can handle. This simple goal of “business questions to ML” would massively alter the business world, allowing users of any technical skill level to effortlessly draw deep insights out of their data. However, this sweeping paradigm shift will require the work of countless AI experts to tackle challenges that the industry simply hasn’t figured out yet.

There just aren’t enough AI and data science practitioners to go around to tackle this lofty goal. A recent Gartner report estimates that “by 2020, 50% of organizations will lack sufficient AI and data literacy skills to achieve business value.” Apply that metric to any other business-critical function. Can you imagine if 50% of companies didn’t have coders or sales reps or any of the other vital roles that keep the lights on? It’d be considered a crisis and an emergency. That’s the state of AI. Implementation of AI elements is expected to almost triple in the US while the overall supply of AI experts will stay significantly lower than demand.

Speaking of the demand, there’s another hurdle to AI success in the short-term. Business teams are looking for very specific tactical insights from their AI utilities (profitability, sustainability, brand, customers, etc.) compared to the Data Science/AI teams, which are more interested in strategic improvements including Natural Language Processing, computer vision, optical character recognition, and neural nets. Further complicating matters, research and development groups focus on bread-and-butter issues like scalability, security, reliability, and performance. Communication between these disparate teams, each with their own goals and desires, means that often novel AI solutions and applications are created and abandoned because stakeholders elsewhere in the company don’t understand the value they produce.

Get money out of your data

So where does this leave companies that want to survive and thrive in the coming AI apocalypse? 

Most existing business models are somewhere in the “diminishing returns” region of the economic value curve (investing more resources while revenue stays flat). More and more funds are poured into R&D with no additional associated revenue coming in. The right data and analytics platform can help you bridge the gap between your current AI and analytics paradigm and where you want your company to be in the future. 

Your platform should be able to effortlessly connect to numerous large datasets: cloud-native data warehouses and data lakes with millions or billions of rows. It should also be able to handle live datasets that constantly update and allow you to mash them up with cached data. If the system also allows for in-database preparation and materialized views that can be optimized for faster queries and lay the foundation for advanced analytics with Python and R, even better. All these utilities put powerful tools into the hands of data engineers to perform more complex analysis and use ML on huge datasets.

Whatever you want to do with AI and ML in the long term, the right analytics platform will be key to building it. It’s also a better way to monetize your data in the short term. It’s a win-win that more and more companies will rush to embrace as they undergo digital transformations and cloud migrations. It’s the future.

Inna Tokarev-Sela, Sisense’s Head of AI, has over 15 years’ experience in the tech industry. She spent the last decade at SAP, driving innovations in cloud architecture, in-memory products, and Machine Learning video analytics. A frequent speaker at industry events like IBC, NAB, WonderlandAI, and Media Festival, Inna holds a BS in Physics and Computer Science, an MBA, and an MS in Information Systems, having written her thesis on Neural Networks.

Source by analyticsweek

Feb 20, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Tour of Accounting  Source

[ AnalyticsWeek BYTES]

>> Fabric and Future of Organization through #FutureOfWork #JobsOfFuture #Podcast by v1shal

>> Taking Operational Business Decisions to the Next Level by joedecosmo

>> The Big Analytics Trend for 2019? Humans. by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

Lean Analytics Workshop – Alistair Croll and Ben Yoskovitz

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Use data to build a better startup faster in partnership with Geckoboard… more

[ FEATURED READ]

On Intelligence

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Jeff Hawkins, the man who created the PalmPilot, Treo smart phone, and other handheld devices, has reshaped our relationship to computers. Now he stands ready to revolutionize both neuroscience and computing in one strok… more

[ TIPS & TRICKS OF THE WEEK]

Finding a success in your data science ? Find a mentor
Yes, most of us dont feel a need but most of us really could use one. As most of data science professionals work in their own isolations, getting an unbiased perspective is not easy. Many times, it is also not easy to understand how the data science progression is going to be. Getting a network of mentors address these issues easily, it gives data professionals an outside perspective and unbiased ally. It’s extremely important for successful data science professionals to build a mentor network and use it through their success.

[ DATA SCIENCE Q&A]

Q:What is cross-validation? How to do it right?
A: It’s a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set. Mainly used in settings where the goal is prediction and one wants to estimate how accurately a model will perform in practice. The goal of cross-validation is to define a data set to test the model in the training phase (i.e. validation data set) in order to limit problems like overfitting, and get an insight on how the model will generalize to an independent data set.

Examples: leave-one-out cross validation, K-fold cross validation

How to do it right?

the training and validation data sets have to be drawn from the same population
predicting stock prices: trained for a certain 5-year period, it’s unrealistic to treat the subsequent 5-year a draw from the same population
common mistake: for instance the step of choosing the kernel parameters of a SVM should be cross-validated as well
Bias-variance trade-off for k-fold cross validation:

Leave-one-out cross-validation: gives approximately unbiased estimates of the test error since each training set contains almost the entire data set (n?1n?1 observations).

But: we average the outputs of n fitted models, each of which is trained on an almost identical set of observations hence the outputs are highly correlated. Since the variance of a mean of quantities increases when correlation of these quantities increase, the test error estimate from a LOOCV has higher variance than the one obtained with k-fold cross validation

Typically, we choose k=5 or k=10, as these values have been shown empirically to yield test error estimates that suffer neither from excessively high bias nor high variance.
Source

[ VIDEO OF THE WEEK]

The History and Use of R

 The History and Use of R

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[ QUOTE OF THE WEEK]

It is a capital mistake to theorize before one has data. Insensibly, one begins to twist the facts to suit theories, instead of theories to

[ PODCAST OF THE WEEK]

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

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

Subscribe 

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[ FACT OF THE WEEK]

Akamai analyzes 75 million events per day to better target advertisements.

Sourced from: Analytics.CLUB #WEB Newsletter

It’s Time To Go Beyond Dashboards

Another year, another Gartner Magic Quadrant for Analytics and Business Intelligence Platforms. For me, this year is a bit different as it’s my first as part of the Sisense team. In the past year that I’ve been with Sisense, I’ve watched as we’ve ensured our platform encompasses everything organizations need for instant insights, launched some of the most innovative features out in the BI market to date, and continued to tirelessly invest in our commitment to treating customers as partners.

Seeing all these efforts culminate in being acknowledged as a Visionary in the Gartner Magic Quadrant for Analytics and Business Intelligence Platforms and being positioned highest among Visionary vendors on the ‘ability to execute’ axis, solidifies our consistent commitment to innovation, customer success, and developing features to empower and enable analytics builders.

Partnership

Our drive for innovative and creative ways to answer business intelligence problems stems from the needs of our customers and the market. It’s in our DNA to think of our customers as partners, which is why we’re ranked the highest for ethics and customer experience, which includes support, time to resolve, expertise, and upgrade experience.

This ranking gets me especially excited. As much as we obsess over the success of our customers and follow our Net Promoter Score religiously to make sure our customers see value as quickly as possible, to have them confirm that our platform and the services we offer truly do the job has all of us over the moon.


Reframing What’s Possible

As a true end-to-end analytics platform, we pride ourselves on the ability to provide value for every person in the analytics chain – starting with data engineers. This year we saw the launch of our Elastic Data Hub, which allows you to leverage your entire data landscape with both live and cached connectivity for key cloud-native high-performance data warehouses such as Redshift, Google BigQuery, and Snowflake.

The Elastic Data Hub provides the ability to deliver on your organization’s analytics strategy even when changes occur to underlying source systems and reframes what’s possible when it comes to data management. Supporting our goal to give the fastest time to value, this ensures that time to insight is reduced drastically by eliminating the need for lengthy migration processes before analysis can be performed.

Future-Proof Platform

Constant innovation and finding creative solutions to business intelligence problems is part of who we are. Beyond the Elastic Data Hub, in the past year, we literally stayed on the edge of innovation by releasing Sisense Hunch™, our data cognition engine, which enables millisecond analysis on the edge through deep neural networks. We also launched Analyze with Insight Miner, which automatically uncovers patterns, trends, and anomalies in your data through artificial intelligence and machine learning in their truest forms. Not to mention Sisense BloX, which gives developers a tool to create actionable analytic apps in an open, low-code environment.

Why do we put such a strong emphasis on innovation? We know that not everyone is a data person and sometimes, a dashboard just won’t deliver the impact it can. Adding features into our platform that allow developers to customize their organization’s analytics offerings and deliver value fast (and beyond dashboards) is integral to future proofing any analytics deployment.

Embeddability

With more and more consumers expecting to have a glimpse at their data, product teams will need to figure out the best way to deliver this data to their end users. Companies that fail to incorporate analytics into their strategy will quickly lose market share to their competitors who do.

I’m happy to say that Sisense has deep expertise in embedded analytics and has partnered with organizations from around the globe to enhance their products and services with powerful white-labeled analytics that their customers love. And we’ve also got almost 50% of our installed base embedding our analytics platform into their products, so you know you can count on us when you’re ready to put analytics in your product.

Insights For Everyone

So, what does this all mean? Our efforts in streamlining data management, innovation, and customer support ultimately ensure those insights are accessible to everyone. As data continues to be democratized and people look for more ways to consume data outside of standard dashboards, we promise that we will be at the forefront of enabling that to happen with a platform that is enterprise-ready, future-proof, and scalable to fit any analytics use case that may come your way.

Gartner Magic Quadrant for Analytics and Business Intelligence Platforms, Cindi Howson, James Richardson, Rita Sallam, Austin Kronz, 11 February 2019.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

Source

Feb 13, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Complex data  Source

[ AnalyticsWeek BYTES]

>> The power of data in the financial services industry by analyticsweekpick

>> Put big data to work with Cortana Analytics by analyticsweekpick

>> August 2019 Newsletter by administrator

Wanna write? Click Here

[ FEATURED COURSE]

CS229 – Machine Learning

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This course provides a broad introduction to machine learning and statistical pattern recognition. … more

[ FEATURED READ]

On Intelligence

image

Jeff Hawkins, the man who created the PalmPilot, Treo smart phone, and other handheld devices, has reshaped our relationship to computers. Now he stands ready to revolutionize both neuroscience and computing in one strok… 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:How would you come up with a solution to identify plagiarism?
A: * Vector space model approach
* Represent documents (the suspect and original ones) as vectors of terms
* Terms: n-grams; n=1 to as much we can (detect passage plagiarism)
* Measure the similarity between both documents
* Similarity measure: cosine distance, Jaro-Winkler, Jaccard
* Declare plagiarism at a certain threshold

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek Panel Discussion: Big Data Analytics

 @AnalyticsWeek Panel Discussion: Big Data Analytics

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

It is a capital mistake to theorize before one has data. Insensibly, one begins to twist the facts to suit theories, instead of theories to

[ PODCAST OF THE WEEK]

Nick Howe (@Area9Nick @Area9Learning) talks about fabric of learning organization to bring #JobsOfFuture #Podcast

 Nick Howe (@Area9Nick @Area9Learning) talks about fabric of learning organization to bring #JobsOfFuture #Podcast

Subscribe 

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[ FACT OF THE WEEK]

In 2008, Google was processing 20,000 terabytes of data (20 petabytes) a day.

Sourced from: Analytics.CLUB #WEB Newsletter

Improving the Customer Experience Through Big Data [VIDEO]

I was invited by  David Pittman of IBM Big Data to participate in a Google Hangout about Big Data and customer experience management (CEM). Stacy Leidwinger, Sr. Dir. of Product Management at IBM Vivisimo, and I talked about how businesses can improve the customer experience using Big Data principles.

Also, you can read more about CEM and Big Data in my recent post on IBM Big Data Hub.

Enjoy the video (I promise to have better lighting next time!).

[iframe_loader src=”http://www.youtube.com/embed/OQwLmsPgkhE” height=”315″ width=”560″ allowfullscreen=”” frameborder=”0″]

Video originally appeared here on IBM Big Data Hub.

 

Source: Improving the Customer Experience Through Big Data [VIDEO]

Predictive Analytics in Manufacturing: A Winning Edge

Blog

The modern manufacturing world is a delicate dance, filled with interconnected pieces that all need to work perfectly in order to produce the goods that keep the world running. In Moving Parts, we explore the unique data and analytics challenges manufacturing companies face every day.

Building an accurate predictive analytics model isn’t easy. It requires a skilled data team, advanced tools, and enormous amounts of clean data from the right combination of inputs. It’s a difficult process, but an effective predictive analytics engine is an enormous asset for any organization.

Manufacturing-Data-banner

Big challenges, big rewards

Manufacturing companies are in a unique position regarding data: they create and capture tons of it every day. The process of producing goods is an enormous opportunity for data optimization. Raw materials need to be ordered, received, constructed, packaged, and shipped out for sale in the most efficient manner possible. Because the steps are repeated so many times through the process, a small edge created via predictive analytics in manufacturing will be magnified at every repetition to produce significant benefit.

Because of the cyclical nature of the manufacturing process, data-driven companies are building superior processes to create bigger and bigger advantages. Here are a few examples of companies using manufacturing analytics to win the future: 

Predicting return rate

Skullcandy’s dive into predictive analytics started with the challenge of understanding return rates on new products. The logic was that if the team could predict certain features or aspects of a product that would lead to a return, they could optimize those policies around returning products. They used BigSquid to blend and analyze historical data related to returns and added their learnings to features and products on their roadmap. From there, the team could ask new questions of that dataset to understand the way customers were interacting with their products and ultimately build a better warranty policy for products before they were even released. This data was also useful for product managers, giving them a clear picture of what was making customers adopt Skullcandy’s products (or not).

Once the return rate questions were answered, the team focused their efforts on uncovering insights around reviews and warranty claims to generate insights about positive and negative drivers. Data like this is ideal for making decisions for product roadmaps. All those customer insights can be used in a number of creative ways to better focus resources and improve products.

Improve forecasts and maximize revenue

Gentex made the most of their budget to optimize their incoming revenue. Just six months after implementing predictive analytics, their ecommerce sales increased by 50%!

Gentex deployed Sisense to comb through millions of records after switching over from an outdated ERP system. They needed a platform that could churn through all that data quickly and deliver quick intelligence about both current and future revenues. Initially, Gentex created dashboards for their sales and operations teams that collected information about sales, quotes, and orders across the company. Those dashboards answered immediate questions about the current state of the business.

To answer more forward-looking questions, Gentex creates a sales forecast for an entire year using just a few months of data. They use predictive models to forecast revenues based on spending. They even incorporate trend data to improve accuracy over time. Currently, Gentex builds visualizations of year-to-date revenue data to forecast up to 15 months into the future.

Operating off those accurate forecasts, Gentex made the most of their budget to optimize their incoming revenue. Just six months after implementing predictive analytics, their ecommerce sales increased by 50%!

Improve inventory management with demand forecasting

Making a product that consumers want to buy is only useful if a company can find a way to get that product in front of the consumers who demand it. Several of today’s most cutting-edge manufacturers are blending historical customer data and external factors to predict demand for goods so they can increase production when demand will be high and decrease production when demand will be low. These companies aren’t just building for the future, they’re building the future.

The need to accurately forecast demand is crucial to these manufacturers. Assessing demand in real-time is ineffective since companies need to make decisions about demand far enough in advance to complete an entire production cycle and get that product in front of customers. With a solid predictive analytics model in place, manufacturers can create exactly the right amount of products (and the right variety of those products) to satisfy future customers.

These forecasts optimize sales revenue, but it also avoids unnecessary costs associated with producing, shipping, and stocking items that won’t sell. Accurate predictions are a win-win for any manufacturer.

Build your manufacturing business with analytics

Predictive analytics in manufacturing have gone from being science fiction to being a make-or-break addition to any company’s technology stack. Using a platform like Sisense for manufacturing analytics, combining internal and external information into a series of accurate forecasts is incredibly invaluable to any manufacturer. Improving any step of the manufacturing process is an advantage over the competition, but improving every step is a data-driven way to become an industry leader faster.

Adam Bonefeste is a veteran content marketing manager. When he isn’t writing copy, he’s probably reading books, running through San Francisco or getting lost in YouTube holes about math/logic problems.

Source: Predictive Analytics in Manufacturing: A Winning Edge by analyticsweek

Feb 06, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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SQL Database  Source

[ AnalyticsWeek BYTES]

>> AI pet-swapping app shows you what your dog would look like as a bear by administrator

>> Nov 16, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

>> Godzilla Vs. Megalon: Is There Really a Battle Between R and SAS for Corporate and Data Scientist Attention? by tony

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[ FEATURED COURSE]

Lean Analytics Workshop – Alistair Croll and Ben Yoskovitz

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Use data to build a better startup faster in partnership with Geckoboard… more

[ FEATURED READ]

Data Science from Scratch: First Principles with Python

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Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn … more

[ TIPS & TRICKS OF THE WEEK]

Keeping Biases Checked during the last mile of decision making
Today a data driven leader, a data scientist or a data driven expert is always put to test by helping his team solve a problem using his skills and expertise. Believe it or not but a part of that decision tree is derived from the intuition that adds a bias in our judgement that makes the suggestions tainted. Most skilled professionals do understand and handle the biases well, but in few cases, we give into tiny traps and could find ourselves trapped in those biases which impairs the judgement. So, it is important that we keep the intuition bias in check when working on a data problem.

[ DATA SCIENCE Q&A]

Q:What is your definition of big data?
A: Big data is high volume, high velocity and/or high variety information assets that require new forms of processing
– Volume: big data doesn’t sample, just observes and tracks what happens
– Velocity: big data is often available in real-time
– Variety: big data comes from texts, images, audio, video…

Difference big data/business intelligence:
– Business intelligence uses descriptive statistics with data with high density information to measure things, detect trends etc.
– Big data uses inductive statistics (statistical inference) and concepts from non-linear system identification to infer laws (regression, classification, clustering) from large data sets with low density information to reveal relationships and dependencies or to perform prediction of outcomes or behaviors

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[ VIDEO OF THE WEEK]

@AnalyticsWeek Panel Discussion: Finance and Insurance Analytics

 @AnalyticsWeek Panel Discussion: Finance and Insurance Analytics

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[ QUOTE OF THE WEEK]

Everybody gets so much information all day long that they lose their common sense. – Gertrude Stein

[ PODCAST OF THE WEEK]

Understanding Data Analytics in Information Security with @JayJarome, @BitSight

 Understanding Data Analytics in Information Security with @JayJarome, @BitSight

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[ FACT OF THE WEEK]

Every person in the world having more than 215m high-resolution MRI scans a day.

Sourced from: Analytics.CLUB #WEB Newsletter

Spark + AI Summit Europe Agenda Announced

London, as a financial center and cosmopolitan city, has its historical charm, cultural draw, and technical allure for everyone, whether you are an artist, entrepreneur or high-tech engineer. As such, we are excited to announce that London is our next stop for Spark + AI Summit Europe, from October 2-4th, 2018, so prepare yourself for the largest Spark + AI community gathering in EMEA!

Today, we announced our agenda for Spark + AI Summit Europe, with over 100 sessions across 11 tracks, including AI Use Cases, Deep Learning Techniques, Productionizing Machine Learning, and Apache Spark Streaming. Sign up before July 27th for early registration and save £300.00.

While we will announce all our exceptional keynotes soon, we are delighted to have these notable technical visionaries as part of the keynotes: Databricks CEO and Co-founder Ali Ghodsi; Matei Zaharia, the original creator of Apache Spark and Databricks chief technologist; Reynold Xin, Databricks co-founder and chief architect; and Soumith Chintala, creator of PyTorch and AI researcher at Facebook.

Along with these visionary keynotes, our agenda features a stellar lineup of community talks led by engineers, data scientists, researchers, entrepreneurs, and machine learning experts from Facebook, Microsoft, Uber, CERN, IBM, Intel, Redhat, Pinterest and, of course, Databricks. There is also a full day of hands-on Apache Spark and Deep Learning training, with courses for both beginners and advanced users, on both AWS and Azure clouds.

All of the above keynotes and sessions will reinforce the idea that Data + AI and Unified Analytics are an integral part of accelerating innovation. For example, early this month, we had our first expanded Spark + AI Summit at Moscone Center in San Francisco, where over 4,000 Spark and Machine Learning enthusiasts attended, representing over 40 countries and regions. The overall theme of Data + AI as a unifying and driving force of innovation resonated in many sessions, including notable keynotes, as Apache Spark forays into new frontiers because of its capability to unify new data workloads and capacity to process data at scale.

With four new tracks, over 180 sessions, Apache Spark and Deep Learning training on both AWS and Azure Cloud, and myriad community-related events, the San Francisco summit was a huge success and a new experience for many attendees! One attendee notes:

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We want our European attendees to have a similar experience and gain the same knowledge, so make this your moment, keep calm and come to London in October. With an early bird registration, you can save you £300.00.

Try Databricks for free. Get started today.

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Source: Spark + AI Summit Europe Agenda Announced