Oct 29, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

image
Data Accuracy  Source

[ FEATURED COURSE]

Machine Learning

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6.867 is an introductory course on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending … more

[ FEATURED READ]

How to Create a Mind: The Secret of Human Thought Revealed

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Ray Kurzweil is arguably today’s most influential—and often controversial—futurist. In How to Create a Mind, Kurzweil presents a provocative exploration of the most important project in human-machine civilization—reverse… 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:Explain Tufte’s concept of ‘chart junk’?
A: All visuals elements in charts and graphs that are not necessary to comprehend the information represented, or that distract the viewer from this information

Examples of unnecessary elements include:
– Unnecessary text
– Heavy or dark grid lines
– Ornamented chart axes
– Pictures
– Background
– Unnecessary dimensions
– Elements depicted out of scale to one another
– 3-D simulations in line or bar charts

Source

[ VIDEO OF THE WEEK]

@DrewConway on creating socially responsible data science practice #FutureOfData #Podcast

 @DrewConway on creating socially responsible data science practice #FutureOfData #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world. – Atul Butte, Stanford

[ PODCAST OF THE WEEK]

Andrea Gallego(@risenthink) / @BCG on Managing Analytics Practice #FutureOfData #Podcast

 Andrea Gallego(@risenthink) / @BCG on Managing Analytics Practice #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

According to estimates, the volume of business data worldwide, across all companies, doubles every 1.2 years.

Sourced from: Analytics.CLUB #WEB Newsletter

Oct 22, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Big Data knows everything  Source

[ AnalyticsWeek BYTES]

>> Logi Tutorial: Step-by-Step Guide for Setting Up a Logi Application on AWS by analyticsweek

>> Unraveling the Mystery of Big Data by v1shal

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

Wanna write? Click Here

[ FEATURED COURSE]

Tackle Real Data Challenges

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Learn scalable data management, evaluate big data technologies, and design effective visualizations…. more

[ FEATURED READ]

Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 4th Edition

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The eagerly anticipated Fourth Edition of the title that pioneered the comparison of qualitative, quantitative, and mixed methods research design is here! For all three approaches, Creswell includes a preliminary conside… 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:Give examples of data that does not have a Gaussian distribution, nor log-normal?
A: * Allocation of wealth among individuals
* Values of oil reserves among oil fields (many small ones, a small number of large ones)

Source

[ VIDEO OF THE WEEK]

@BrianHaugli @The_Hanover ?on Building a #Leadership #Security #Mindset #FutureOfData #Podcast

 @BrianHaugli @The_Hanover ?on Building a #Leadership #Security #Mindset #FutureOfData #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

The temptation to form premature theories upon insufficient data is the bane of our profession. – Sherlock Holmes

[ PODCAST OF THE WEEK]

Pascal Marmier (@pmarmier) @SwissRe discusses running data driven innovation catalyst

 Pascal Marmier (@pmarmier) @SwissRe discusses running data driven innovation catalyst

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Estimates suggest that by better integrating big data, healthcare could save as much as $300 billion a year — that’s equal to reducing costs by $1000 a year for every man, woman, and child.

Sourced from: Analytics.CLUB #WEB Newsletter

Oct 15, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Convincing  Source

[ FEATURED COURSE]

R, ggplot, and Simple Linear Regression

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Begin to use R and ggplot while learning the basics of linear regression… more

[ FEATURED READ]

Hypothesis Testing: A Visual Introduction To Statistical Significance

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Statistical significance is a way of determining if an outcome occurred by random chance, or did something cause that outcome to be different than the expected baseline. Statistical significance calculations find their … more

[ TIPS & TRICKS OF THE WEEK]

Strong business case could save your project
Like anything in corporate culture, the project is oftentimes about the business, not the technology. With data analysis, the same type of thinking goes. It’s not always about the technicality but about the business implications. Data science project success criteria should include project management success criteria as well. This will ensure smooth adoption, easy buy-ins, room for wins and co-operating stakeholders. So, a good data scientist should also possess some qualities of a good project manager.

[ DATA SCIENCE Q&A]

Q:What is the life cycle of a data science project ?
A: 1. Data acquisition
Acquiring data from both internal and external sources, including social media or web scraping. In a steady state, data extraction and routines should be in place, and new sources, once identified would be acquired following the established processes

2. Data preparation
Also called data wrangling: cleaning the data and shaping it into a suitable form for later analyses. Involves exploratory data analysis and feature extraction.

3. Hypothesis & modelling
Like in data mining but not with samples, with all the data instead. Applying machine learning techniques to all the data. A key sub-step: model selection. This involves preparing a training set for model candidates, and validation and test sets for comparing model performances, selecting the best performing model, gauging model accuracy and preventing overfitting

4. Evaluation & interpretation

Steps 2 to 4 are repeated a number of times as needed; as the understanding of data and business becomes clearer and results from initial models and hypotheses are evaluated, further tweaks are performed. These may sometimes include step5 and be performed in a pre-production.

5. Deployment

6. Operations
Regular maintenance and operations. Includes performance tests to measure model performance, and can alert when performance goes beyond a certain acceptable threshold

7. Optimization
Can be triggered by failing performance, or due to the need to add new data sources and retraining the model or even to deploy new versions of an improved model

Note: with increasing maturity and well-defined project goals, pre-defined performance can help evaluate feasibility of the data science project early enough in the data-science life cycle. This early comparison helps the team refine hypothesis, discard the project if non-viable, change approaches.

Steps 2 to 4 are repeated a number of times as needed; as the understanding of data and business becomes clearer and results from initial models and hypotheses are evaluated, further tweaks are performed. These may sometimes include step5 and be performed in a pre-production.

Deployment

Operations
Regular maintenance and operations. Includes performance tests to measure model performance, and can alert when performance goes beyond a certain acceptable threshold

Optimization
Can be triggered by failing performance, or due to the need to add new data sources and retraining the model or even to deploy new versions of an improved model

Note: with increasing maturity and well-defined project goals, pre-defined performance can help evaluate feasibility of the data science project early enough in the data-science life cycle. This early comparison helps the team refine hypothesis, discard the project if non-viable, change approaches.

Steps 2 to 4 are repeated a number of times as needed; as the understanding of data and business becomes clearer and results from initial models and hypotheses are evaluated, further tweaks are performed. These may sometimes include step5 and be performed in a pre-production.

Deployment

Operations
Regular maintenance and operations. Includes performance tests to measure model performance, and can alert when performance goes beyond a certain acceptable threshold

Optimization
Can be triggered by failing performance, or due to the need to add new data sources and retraining the model or even to deploy new versions of an improved model

Note: with increasing maturity and well-defined project goals, pre-defined performance can help evaluate feasibility of the data science project early enough in the data-science life cycle. This early comparison helps the team refine hypothesis, discard the project if non-viable, change approaches.

Steps 2 to 4 are repeated a number of times as needed; as the understanding of data and business becomes clearer and results from initial models and hypotheses are evaluated, further tweaks are performed. These may sometimes include step5 and be performed in a pre-production.

Deployment

Operations
Regular maintenance and operations. Includes performance tests to measure model performance, and can alert when performance goes beyond a certain acceptable threshold

Optimization
Can be triggered by failing performance, or due to the need to add new data sources and retraining the model or even to deploy new versions of an improved model

Note: with increasing maturity and well-defined project goals, pre-defined performance can help evaluate feasibility of the data science project early enough in the data-science life cycle. This early comparison helps the team refine hypothesis, discard the project if non-viable, change approaches.

Source

[ VIDEO OF THE WEEK]

Scott Harrison (@SRHarrisonJD) on leading the learning organization #JobsOfFuture #Podcast

 Scott Harrison (@SRHarrisonJD) on leading the learning organization #JobsOfFuture #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Torture the data, and it will confess to anything. – Ronald Coase

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Eloy Sasot, News Corp

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Eloy Sasot, News Corp

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

73% of organizations have already invested or plan to invest in big data by 2016

Sourced from: Analytics.CLUB #WEB Newsletter

How Marketing Automation Helps in Leads Generation

What’s your go-to move for lead generation?

If your answer is going to be “buying leads”, then we suggest that you cancel all your meetings because this is going to be a long discussion. Leads that are bought are hollow and give you nothing but empty promises. Quality leads are important for increasing conversion rates, but higher conversions are only possible if you can also generate leads through an effective and efficient manner— marketing automation.

Understanding the Role of Marketing Automation in Lead Generation

Let’s imagine that you run an organic, online food delivery service. You have a huge target market, which includes diabetics as well as Keto and Paleo lovers and more. In order to capture leads, you decided to build a marketing campaign. The ads are a hit and you see traffic coming in droves on your website. Some of the leads reach the end of the funnel and some of them exit in the middle. Your aim is to target those visitors who left without making a purchase. In order to reel them in, one of the most basic marketing methods you can use is email marketing. However, customizing emails for customers who are at different stages of the marketing funnel can be taxing. So, you choose to go with marketing automation solutions. You pick a software program, which streamlines the emailing process. It sends emails to all your visitors based on their browsing habits. When you receive feedback from these people, you divide them into groups and automate the next line of emails. As a result, your visitors become more interested in your product because you are providing them with the information they are actively looking for. Those visitors are now hot leads, which will most probably reach the end of the sales funnel.

Now that you know how marketing automation works in lead generation, let’s have a look at some of the strategies you can use:

1.     Lead Magnet

Lead magnets can help increase conversions and capture email addresses for your marketing automation software. You can offer a lead magnet in several ways. Make sure that what you are promising is attractive and offers something valuable. This can be anything from a free eBook to a discount voucher or a membership at a low cost.

2.     Landing Pages

Landing pages are one of the best lead generation components. If your landing page is receiving numerous visitors but no one clicks to the service or product page, then it’s time to change your lead strategy. We already know that your customers’ buying cycle will be recorded by the marketing automation software. However, to make sure that your email marketing ensures the return of the visitor, you can create separate contact forms for people who are inquiring about a specific product. This way, the software will automate customized campaigns that specifically target the group that is interested in a particular product.

3.     Drip Campaigns

To get the attention of the target audience without being pushy or annoying, drip campaigns were created. The slow delivery of emails, in a timely manner, allowed marketers to capture the attention of their website’s visitors and turn them into leads.

In order for drip campaigns to work, you first need to divide your visitors into segments and then add their email addresses into the marketing automation software. The software will then send emails to the leads with targeted content and at specific times. This will allow you to keep your brand’s names at the top of your leads’ minds. Think of this as business intelligence services that help you gather information without spending too much time or money.

4.     Re-Engagement Campaigns

After analyzing the data, you are made aware that some of the visitors on your page entered the marketing sales funnel but are now taking too long to make the purchase. These leads are just sitting there and you have no idea why. It could be that they no longer need the product or don’t want to make a hasty decision. As time passes by, these leads don’t reach the last stage of the funnel. How do you retarget these dormant leads?

Through re-engagement campaigns!

Let’s look at an example. When a customer buys a car from Nissan, the company creates a record with the person’s information for follow up. They are dedicated to making their customers happy, even years after the purchase. One way through which they follow up is by reminding their customers that their car needs maintenance. At the time of the purchase, the customer’s email address is added into the year category and at a specific time, they are reminded with an alert that it’s time for a checkup.

Besides the above-mentioned method, there are plenty of other ways to get your dormant lead’s attention, such as offering them free consultation or a discount subscription. Divide these leads into another segment and focus more of your automation marketing efforts on them.

5.     Highly Motivated Subscribers

Subscribers are fickle! They show interest and great enthusiasm in the beginning and then mellow down as time passes. This is where you need to strike. Don’t waste your efforts on those subscribers who hardly go through your content. Before using your automation workflow for another segment, divide your subscribers into warm and cold leads. Send all your subscribers an email with quality content. Those who click through can be pushed down the funnel and the rest can be set aside for the time being. Now, you can send them targeted content or incentives so that they make a purchase.

Marketing automation is not that tricky as businesses think. It is a great way to generate leads if you use it correctly and at the right time. Does buying leads still sound right to you? After all, they won’t be of any real value to you. Try out these techniques and you will see a drastic increase in your conversion rates!

The post How Marketing Automation Helps in Leads Generation appeared first on Big Data Made Simple.

Source

Oct 08, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

image
Pacman  Source

[ FEATURED COURSE]

Applied Data Science: An Introduction

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As the world’s data grow exponentially, organizations across all sectors, including government and not-for-profit, need to understand, manage and use big, complex data sets—known as big data…. more

[ FEATURED READ]

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

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Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the “data-analytic thinking” necessary for e… more

[ TIPS & TRICKS OF THE WEEK]

Analytics Strategy that is Startup Compliant
With right tools, capturing data is easy but not being able to handle data could lead to chaos. One of the most reliable startup strategy for adopting data analytics is TUM or The Ultimate Metric. This is the metric that matters the most to your startup. Some advantages of TUM: It answers the most important business question, it cleans up your goals, it inspires innovation and helps you understand the entire quantified business.

[ DATA SCIENCE Q&A]

Q:What is the maximal margin classifier? How this margin can be achieved?
A: * When the data can be perfectly separated using a hyperplane, there actually exists an infinite number of these hyperplanes
* Intuition: a hyperplane can usually be shifted a tiny bit up, or down, or rotated, without coming into contact with any of the observations
* Large margin classifier: choosing the hyperplance that is farthest from the training observations
* This margin can be achieved using support vectors

Source

[ VIDEO OF THE WEEK]

Pascal Marmier (@pmarmier) @SwissRe discusses running data driven innovation catalyst

 Pascal Marmier (@pmarmier) @SwissRe discusses running data driven innovation catalyst

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world. – Atul Butte, Stanford

[ PODCAST OF THE WEEK]

@AmyGershkoff on building #winning #DataScience #team #FutureOfData #Podcast

 @AmyGershkoff on building #winning #DataScience #team #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

By 2020, we will have over 6.1 billion smartphone users globally (overtaking basic fixed phone subscriptions).

Sourced from: Analytics.CLUB #WEB Newsletter