### [ COVER OF THE WEEK ]

**Accuracy check** Source

### [ LOCAL EVENTS & SESSIONS]

- Jan 17, 2019 #WEB Webinar: Delivering Real-time Data with Azure and Power BI
- Jan 15, 2019 #WEB Live Webinar: “Bayesian Covariance for Portfolio Optimization” on January 15th
- Feb 16, 2019 #WEB Big Data Series – Spark Programming Essentials

### [ AnalyticsWeek BYTES]

**>>** 6 Best Practices for Maximizing Big Data Value by analyticsweekpick

**>>** To Trust A Bot or Not? Ethical Issues in AI by tony

**>>** Inside CXM: New Global Thought Leader Hub for Customer Experience Professionals by bobehayes

### [ NEWS BYTES]

**>>**

Sales Performance Management (SPM) Market 2018-2025: CAGR, Top Manufacturers, Drivers, Trends, Challenges … – The Dosdigitos (press release) (blog) Under Sales Analytics

**>>**

Teradata Vantage brings multiple data sources on one platform, hope to become core of large enterprises – The Indian Express Under Talent Analytics

**>>**

Duke Engineering Establishes Big Data, Precision Medicine Center – Health IT Analytics Under Big Data Analytics

### [ FEATURED COURSE]

**Pattern Discovery in Data Mining**

### [ FEATURED READ]

### [ 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 an outlier? Explain how you might screen for outliers and what would you do if you found them in your dataset. Also, explain what an inlier is and how you might screen for them and what would you do if you found them in your dataset**

A: Outliers:

– An observation point that is distant from other observations

– Can occur by chance in any distribution

– Often, they indicate measurement error or a heavy-tailed distribution

– Measurement error: discard them or use robust statistics

– Heavy-tailed distribution: high skewness, cant use tools assuming a normal distribution

– Three-sigma rules (normally distributed data): 1 in 22 observations will differ by twice the standard deviation from the mean

– Three-sigma rules: 1 in 370 observations will differ by three times the standard deviation from the mean

Three-sigma rules example: in a sample of 1000 observations, the presence of up to 5 observations deviating from the mean by more than three times the standard deviation is within the range of what can be expected, being less than twice the expected number and hence within 1 standard deviation of the expected number (Poisson distribution).

If the nature of the distribution is known a priori, it is possible to see if the number of outliers deviate significantly from what can be expected. For a given cutoff (samples fall beyond the cutoff with probability p), the number of outliers can be approximated with a Poisson distribution with lambda=pn. Example: if one takes a normal distribution with a cutoff 3 standard deviations from the mean, p=0.3% and thus we can approximate the number of samples whose deviation exceed 3 sigmas by a Poisson with lambda=3

Identifying outliers:

– No rigid mathematical method

– Subjective exercise: be careful

– Boxplots

– QQ plots (sample quantiles Vs theoretical quantiles)

Handling outliers:

– Depends on the cause

– Retention: when the underlying model is confidently known

– Regression problems: only exclude points which exhibit a large degree of influence on the estimated coefficients (Cooks distance)

Inlier:

– Observation lying within the general distribution of other observed values

– Doesnt perturb the results but are non-conforming and unusual

– Simple example: observation recorded in the wrong unit (°F instead of °C)

Identifying inliers:

– Mahalanobis distance

– Used to calculate the distance between two random vectors

– Difference with Euclidean distance: accounts for correlations

– Discard them

**Source**

### [ VIDEO OF THE WEEK]

#FutureOfData Podcast: Peter Morgan, CEO, Deep Learning Partnership

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### [ QUOTE OF THE WEEK]

Information is the oil of the 21st century, and analytics is the combustion engine. Peter Sondergaard

### [ PODCAST OF THE WEEK]

#FutureOfData with Rob(@telerob) / @ConnellyAgency on running innovation in agency

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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.