January 2021 – AnalyticsCube

Jan 14, 21: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Data interpretation Source

[ 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:How to clean data?
A: 1. First: detect anomalies and contradictions
Common issues:
* Tidy data: (Hadley Wickam paper)
column names are values, not names, e.g. 26-45
multiple variables are stored in one column, e.g. m1534 (male of 15-34 years old age)
variables are stored in both rows and columns, e.g. tmax, tmin in the same column
multiple types of observational units are stored in the same table. e.g, song dataset and rank dataset in the same table
*a single observational unit is stored in multiple tables (can be combined)
* Data-Type constraints: values in a particular column must be of a particular type: integer, numeric, factor, boolean
* Range constraints: number or dates fall within a certain range. They have minimum/maximum permissible values
* Mandatory constraints: certain columns cant be empty
* Unique constraints: a field must be unique across a dataset: a same person must have a unique SS number
* Set-membership constraints: the values for a columns must come from a set of discrete values or codes: a gender must be female, male
* Regular expression patterns: for example, phone number may be required to have the pattern: (999)999-9999
* Misspellings
* Missing values
* Outliers
* Cross-field validation: certain conditions that utilize multiple fields must hold. For instance, in laboratory medicine: the sum of the different white blood cell must equal to zero (they are all percentages). In hospital database, a patients date or discharge cant be earlier than the admission date
2. Clean the data using:
* Regular expressions: misspellings, regular expression patterns
* KNN-impute and other missing values imputing methods
* Coercing: data-type constraints
* Melting: tidy data issues
* Date/time parsing
* Removing observations

Source

[ VIDEO OF THE WEEK]

@JustinBorgman on Running a data science startup, one decision at a time #Futureofdata #Podcast

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[ QUOTE OF THE WEEK]

What we have is a data glut. Vernon Vinge

[ PODCAST OF THE WEEK]

Jeff Palmucci @TripAdvisor discusses managing a #MachineLearning #AI Team

iTunes GooglePlay

[ FACT OF THE WEEK]

IDC Estimates that by 2020,business transactions on the internet- business-to-business and business-to-consumer will reach 450 billion per day.

Sourced from: Analytics.CLUB #WEB Newsletter

Originally Posted at: by

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

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[ COVER OF THE WEEK ]

Statistics Source

[ FEATURED COURSE]

Deep Learning Prerequisites: The Numpy Stack in Python

The Numpy, Scipy, Pandas, and Matplotlib stack: prep for deep learning, machine learning, and artificial intelligence… more

[ FEATURED READ]

Big Data: A Revolution That Will Transform How We Live, Work, and Think

Illuminating and very timely . . . a fascinating and sometimes alarming survey of big data’s growing effect on just about everything: business, government, science and medicine, privacy, and even on the way we think… more

[ TIPS & TRICKS OF THE WEEK]

Data Have Meaning
We live in a Big Data world in which everything is quantified. While the emphasis of Big Data has been focused on distinguishing the three characteristics of data (the infamous three Vs), we need to be cognizant of the fact that data have meaning. That is, the numbers in your data represent something of interest, an outcome that is important to your business. The meaning of those numbers is about the veracity of your data.

[ DATA SCIENCE Q&A]

Q:What is the difference between supervised learning and unsupervised learning? Give concrete examples
?
A: * Supervised learning: inferring a function from labeled training data
* Supervised learning: predictor measurements associated with a response measurement; we wish to fit a model that relates both for better understanding the relation between them (inference) or with the aim to accurately predicting the response for future observations (prediction)
* Supervised learning: support vector machines, neural networks, linear regression, logistic regression, extreme gradient boosting
* Supervised learning examples: predict the price of a house based on the are, size.; churn prediction; predict the relevance of search engine results.
* Unsupervised learning: inferring a function to describe hidden structure of unlabeled data
* Unsupervised learning: we lack a response variable that can supervise our analysis
* Unsupervised learning: clustering, principal component analysis, singular value decomposition; identify group of customers
* Unsupervised learning examples: find customer segments; image segmentation; classify US senators by their voting.

Source

[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Joe DeCosmo, @Enova

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[ QUOTE OF THE WEEK]

For every two degrees the temperature goes up, check-ins at ice cream shops go up by 2%. Andrew Hogue, Foursquare

[ PODCAST OF THE WEEK]

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

iTunes GooglePlay

[ FACT OF THE WEEK]

40% projected growth in global data generated per year vs. 5% growth in global IT spending.

Sourced from: Analytics.CLUB #WEB Newsletter

Source: by

Business Decisions: Don’t be Afraid to Break the Chain – Dan Volitich

Dan Volitich
President, John Daniel Associates, Inc.Â
Danâs Profile

When an elephant is young, its leg is chained to an anchor in the ground. The baby tries and tries to get free, but cannot. When that baby elephant grows up into a large adult, it has the power to break out of the chain, but it doesnât even try because of its prior experience of not being able to break free.

For nearly 20 years, we have been helping organizations find insight into their incredibly large amounts of data. The biggest obstacle for all companies is overcoming their perception based on past experiences. Techniques and tools that made companies successful over the years may not necessarily be in play today. The only way to overcome knowledge based on the past is to be curious and find new and better ways to solve business dilemmas.

Years ago, after listening to a business pain point of a prospect or customer, there may have been a distinct solution. Fast forward 20 years and are we all still making the same decisions we made 20 years ago? If you find yourself saying, âThat is how we have always done itâ then you will most likely find yourself heading down the road toward a great big wall and slamming into it. Many organizations behave like the elephant. They think if something was not possible years ago, how could it be possible now?

We all understand that a screwdriver is used to screw in screws, and a hammer to pound nails, and even a drill to make a hole in wood or metal. But for some reason, business decisions are often made to force every solution into ONE technology because that is what we have and know âand â that is how we always have done it. That may be fine for the most part, and the existing technology may even be good enough. However, many organizations do not consider other solutions because of what they already own as a current solution. Gartner addresses this with great research that shows the most successful businesses are engaging reporting and analytics with a pragmatic portfolio. Meaning that more than one tool will deliver the best solution based on the business problem and technology challenge.

As we make business decisions, let our past successes or failures influence us, but NOT make us afraid to break the chain so that our businesses can advance.

The post Business Decisions: Donât be Afraid to Break the Chain â Dan Volitich appeared first on John Daniel Associates, Inc..

Month: January 2021

Jan 14, 21: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[ COVER OF THE WEEK ]

[ AnalyticsWeek BYTES]

[ FEATURED COURSE]

[ FEATURED READ]

[ TIPS & TRICKS OF THE WEEK]

[ DATA SCIENCE Q&A]

[ VIDEO OF THE WEEK]

[ QUOTE OF THE WEEK]

[ PODCAST OF THE WEEK]

[ FACT OF THE WEEK]

Sourced from: Analytics.CLUB #WEB Newsletter

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

[ COVER OF THE WEEK ]

[ FEATURED COURSE]

[ FEATURED READ]

[ TIPS & TRICKS OF THE WEEK]

[ DATA SCIENCE Q&A]

[ VIDEO OF THE WEEK]

[ QUOTE OF THE WEEK]

[ PODCAST OF THE WEEK]

[ FACT OF THE WEEK]

Sourced from: Analytics.CLUB #WEB Newsletter

Source: by

Business Decisions: Don’t be Afraid to Break the Chain – Dan Volitich

Originally Posted at: Business Decisions: Donât be Afraid to Break the Chain â Dan Volitich

[ COVER OF THE WEEK ]

[ AnalyticsWeek BYTES]

[ FEATURED COURSE]

[ FEATURED READ]

[ TIPS & TRICKS OF THE WEEK]

[ DATA SCIENCE Q&A]

[ VIDEO OF THE WEEK]

[ QUOTE OF THE WEEK]

[ PODCAST OF THE WEEK]

[ FACT OF THE WEEK]

Sourced from: Analytics.CLUB #WEB Newsletter

[ COVER OF THE WEEK ]

[ FEATURED COURSE]

[ FEATURED READ]

[ TIPS & TRICKS OF THE WEEK]

[ DATA SCIENCE Q&A]

[ VIDEO OF THE WEEK]

[ QUOTE OF THE WEEK]

[ PODCAST OF THE WEEK]

[ FACT OF THE WEEK]

Sourced from: Analytics.CLUB #WEB Newsletter

Source: by

Originally Posted at: Business Decisions: Donât be Afraid to Break the Chain â Dan Volitich

Originally Posted at: Business Decisions: Donât be Afraid to Break the Chain â Dan Volitich