Issue #47    Web Version
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[  COVER OF THE WEEK ]

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[ LOCAL EVENTS & SESSIONS]


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[ AnalyticsWeek BYTES]

>> Predicting UX Metrics with the PURE Method by analyticsweek

>> Looking Beyond OAS 3 (Part 1) by analyticsweekpick

>> Big universe, big data, astronomical opportunity by analyticsweekpick


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[ NEWS BYTES]

>>  Marketing & Social Media Manager – BevNET.com Under  Social Analytics

>>  The Finance Leader’s Guide to Balancing Risk and Performance – FEI Daily Under  Risk Analytics

>>  The tools keep getting better: McLeod’s applied data science initiative – FreightWaves Under  Data Science


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[ FEATURED COURSE]

Process Mining: Data science in Action

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Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be ap... more


[ FEATURED READ]

Machine Learning With Random Forests And Decision Trees: A Visual Guide For Beginners

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If you are looking for a book to help you understand how the machine learning algorithms "Random Forest" and "Decision Trees" work behind the scenes, then this is a good book for you. Those two algorithms are commonly u... more


[ TIPS & TRICKS OF THE WEEK]

Data aids, not replace judgement
Data is a tool and means to help build a consensus to facilitate human decision-making but not replace it. Analysis converts data into information, information via context leads to insight. Insights lead to decision making which ultimately leads to outcomes that brings value. So, data is just the start, context and intuition plays a role.


[ DATA SCIENCE Q&A]

Q:How do you know if one algorithm is better than other?
A: * In terms of performance on a given data set? * In terms of performance on several data sets? * In terms of efficiency? In terms of performance on several data sets: - 'Does learning algorithm A have a higher chance of producing a better predictor than learning algorithm B in the given context?” - 'Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets”, A. Lacoste and F. Laviolette - 'Statistical Comparisons of Classifiers over Multiple Data Sets”, Janez Demsar In terms of performance on a given data set: - One wants to choose between two learning algorithms - Need to compare their performances and assess the statistical significance One approach (Not preferred in the literature): - Multiple k-fold cross validation: run CV multiple times and take the mean and sd - You have: algorithm A (mean and sd) and algorithm B (mean and sd) - Is the difference meaningful? (Paired t-test) Sign-test (classification context): Simply counts the number of times A has a better metrics than B and assumes this comes from a binomial distribution. Then we can obtain a p-value of the HoHo test: A and B are equal in terms of performance. Wilcoxon signed rank test (classification context): Like the sign-test, but the wins (A is better than B) are weighted and assumed coming from a symmetric distribution around a common median. Then, we obtain a p-value of the HoHo test. Other (without hypothesis testing): - AUC - F-Score
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[ VIDEO OF THE WEEK]

@AnalyticsWeek #FutureOfData with Robin Thottungal(@rathottungal), Chief Data Scientist at @EPA

 @AnalyticsWeek #FutureOfData with Robin Thottungal(@rathottungal), Chief Data Scientist at @EPA


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[ QUOTE OF THE WEEK]

You can use all the quantitative data you can get, but you still have to distrust it and use your own intelligence and judgment. – Alvin Tof


[ PODCAST OF THE WEEK]

Future of HR is more Relationship than Data - Scott Kramer @ValpoU #JobsOfFuture #Podcast

 Future of HR is more Relationship than Data - Scott Kramer @ValpoU #JobsOfFuture #Podcast


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[ FACT OF THE WEEK]

A quarter of decision-makers surveyed predict that data volumes in their companies will rise by more than 60 per cent by the end of 2014, with the average of all respondents anticipating a growth of no less than 42 per cent.


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*This Newsletter is hand-curated and autogenerated using #TEAMTAO & TAO, excuse some initial blemishes. As with any AI, it may get worse before it will get relevant, excuse us with your patience & feedback.
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